Skip to main content
SpringerLink
Log in

A survey of graph neural networks in various learning paradigms: methods, applications, and challenges

  • Manuscript
  • Published:
Artificial Intelligence Review Aims and scope Submit manuscript

Abstract

In the last decade, deep learning has reinvigorated the machine learning field. It has solved many problems in computer vision, speech recognition, natural language processing, and other domains with state-of-the-art performances. In these domains, the data is generally represented in the Euclidean space. Various other domains conform to non-Euclidean space, for which a graph is an ideal representation. Graphs are suitable for representing the dependencies and inter-relationships between various entities. Traditionally, handcrafted features for graphs are incapable of providing the necessary inference for various tasks from this complex data representation. Recently, there has been an emergence of employing various advances in deep learning for graph-based tasks (called Graph Neural Networks (GNNs)). This article introduces preliminary knowledge regarding GNNs and comprehensively surveys GNNs in different learning paradigms—supervised, unsupervised, semi-supervised, self-supervised, and few-shot or meta-learning. The taxonomy of each graph-based learning setting is provided with logical divisions of methods falling in the given learning setting. The approaches for each learning task are analyzed from theoretical and empirical standpoints. Further, we provide general architecture design guidelines for building GNN models. Various applications and benchmark datasets are also provided, along with open challenges still plaguing the general applicability of GNNs.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

References

  • Abadal S, Jain A, Guirado R, López-Alonso J, Alarcón E (2021) Computing graph neural networks: a survey from algorithms to accelerators

  • Adhikari B, Zhang Y, Ramakrishnan N, Prakash BA (2018) Sub2vec: feature learning for subgraphs. In: Pacific-Asia conference on knowledge discovery and data mining. Springer, Melbourne, Australia. pp 170–182

  • Afzal MZ, Kölsch A, Ahmed S, Liwicki M (2017) Cutting the error by half: Investigation of very deep CNN and advanced training strategies for document image classification. In: 2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR), vol 1, pp 883–888. IEEE, Kyoto, Japan. IEEE

  • Ahmed A, Shervashidze N, Narayanamurthy S, Josifovski V, Smola AJ (2013) Distributed large-scale natural graph factorization. In: Proceedings of the 22nd international conference on World Wide Web, pp 37–48. Association for Computing Machinery, New York, NY, USA

  • Ahmad W, Zhang Z, Ma X, Hovy E, Chang K-W, Peng N (2019) On difficulties of cross-lingual transfer with order differences: a case study on dependency parsing. In: Proceedings of the 2019 conference of the north american chapter of the association for computational linguistics: human language technologies, volume 1 (Long and Short Papers), pp. 2440–2452. Association for Computational Linguistics, Minneapolis, Minnesota

  • Albert R, Jeong H, Barabási A-L (1999) Diameter of the world-wide web. Nature 401(6749):130–131

    Google Scholar 

  • Alibaba: Euler (2021a). https://github.com/alibaba/euler

  • Alibaba: Graph-learn (2021b) https://github.com/alibaba/graph-learn

  • Allamanis M, Brockschmidt M, Khademi M (2017) Learning to represent programs with graphs

  • Audebert N, Herold C, Slimani K, Vidal C (2019) Multimodal deep networks for text and image-based document classification

  • Bai C, Kumar S, Leskovec J, Metzger M, Nunamaker J, Subrahmanian V (2019) Predicting the visual focus of attention in multi-person discussion videos. In: IJCAI 2019, pp 4504–4510. International Joint Conferences on Artificial Intelligence

  • Bansal M, Yang J, Karan C, Menden MP, Costello JC, Tang H, Xiao G, Li Y, Allen J, Zhong R et al (2014) A community computational challenge to predict the activity of pairs of compounds. Nat Biotechnol 32(12):1213–1222

    Google Scholar 

  • Barceló P, Kostylev EV, Monet M, Pérez J, Reutter J, Silva JP (2019) The logical expressiveness of graph neural networks. In: International conference on learning representations, pp 1–21. ICLR, Ethiopia

  • Bastings J, Titov I, Aziz W, Marcheggiani D, Sima’an K (2017) Graph convolutional encoders for syntax-aware neural machine translation

  • Battaglia PW, Pascanu R, Lai M, Rezende D, Kavukcuoglu K (2016) Interaction networks for learning about objects, relations and physics

  • Beck D, Haffari G, Cohn T (2018) Graph-to-sequence learning using gated graph neural networks

  • Belkin M, Niyogi P (2001) Laplacian eigenmaps and spectral techniques for embedding and clustering. In: Proceedings of the 14th International conference on neural information processing systems: natural and synthetic. NIPS’01, pp 585–591. MIT Press, Cambridge, MA, USA

  • Berg Rvd, Kipf TN, Welling M (2017) Graph convolutional matrix completion

  • Bertinetto L, Henriques JF, Valmadre J, Torr P, Vedaldi A (2016) Learning feed-forward one-shot learners. Advances in neural information processing systems 29

  • Berton L, De Andrade Lopes A (2014) Graph construction based on labeled instances for semi-supervised learning. In: 2014 22nd international conference on pattern recognition, pp 2477–2482. IEEE, Stockholm, Sweden. https://doi.org/10.1109/ICPR.2014.428

  • Berton L, de Paulo FT, Valejo A, Valverde-Rebaza J, de Andrade LA (2017) Rgcli: Robust graph that considers labeled instances for semi-supervised learning. Neurocomputing 226:238–248

    Google Scholar 

  • Borgwardt KM, Ong CS, Schönauer S, Vishwanathan S, Smola AJ, Kriegel H-P (2005) Protein function prediction via graph kernels. Bioinformatics 21:47–56

    Google Scholar 

  • Bruna J, Zaremba W, Szlam A, LeCun Y (2013) Spectral networks and locally connected networks on graphs. arXiv:1312.6203

  • Bui ND, Yu Y, Jiang L (2021) Infercode: Self-supervised learning of code representations by predicting subtrees. In: 2021 IEEE/ACM 43rd international conference on software engineering (ICSE), pp 1186–1197. IEEE, Madrid, ES. IEEE

  • Cai D, Lam W (2020) Graph transformer for graph-to-sequence learning. Proc AAAI Conf Artif Intell 34:7464–7471

    Google Scholar 

  • Cai H, Zheng VW, Chang KC-C (2017) Active learning for graph embedding

  • Cao S, Lu W, Xu Q (2015) Grarep: Learning graph representations with global structural information. In: Proceedings of the 24th ACM International on Conference on Information and Knowledge Management. CIKM ’15, pp. 891–900. Association for Computing Machinery, New York, NY, USA. https://doi.org/10.1145/2806416.2806512

  • Cao S, Lu W, Xu Q (2016) Deep neural networks for learning graph representations. In: Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence. AAAI’16, pp. 1145–1152. AAAI Press, Phoenix, Arizona

  • Cao Z, Li X, Zhao L (2020) Unsupervised Feature Learning by Autoencoder and Prototypical Contrastive Learning for Hyperspectral Classification

  • Cao J, Lin X, Guo S, Liu L, Liu T, Wang B (2021) Bipartite graph embedding via mutual information maximization. In: Proceedings of the 14th ACM International Conference on Web Search and Data Mining. WSDM ’21, pp. 635–643. Association for Computing Machinery, New York, NY, USA. https://doi.org/10.1145/3437963.3441783

  • Caron M, Bojanowski P, Joulin A, Douze M (2018) Deep clustering for unsupervised learning of visual features. In: Ferrari V, Hebert M, Sminchisescu C, Weiss Y (eds) Computer vision - ECCV 2018. Springer, Cham, pp 139–156

    Google Scholar 

  • Chami I, Ying Z, Ré C, Leskovec J (2019) Hyperbolic graph convolutional neural networks. Advances in neural information processing systems 32

  • Che F, Yang G, Zhang D, Tao J, Liu T (2021) Self-supervised graph representation learning via bootstrapping. Neurocomputing 456:88–96

    Google Scholar 

  • Chen C, Wang H-L, Wu S-H, Huang H, Zou J-L, Chen J, Jiang T-Z, Zhou Y, Wang G-H (2015) Abnormal degree centrality of bilateral putamen and left superior frontal gyrus in schizophrenia with auditory hallucinations: a resting-state functional magnetic resonance imaging study. Chin Med J 128(23):3178

    Google Scholar 

  • Chen X, Sun Y, Athiwaratkun B, Cardie C, Weinberger K (2018a) Adversarial deep averaging networks for cross-lingual sentiment classification. Trans Assoc Comput Linguist 6:557–570

    Google Scholar 

  • Chen X, Li L-J, Fei-Fei L, Gupta A (2018b) Iterative visual reasoning beyond convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7239–7248. IEEE, Salt Lake City, UT, USA

  • Chen B, Sun L, Han X (2018c) Sequence-to-action: End-to-end semantic graph generation for semantic parsing

  • Chen T, Kornblith S, Norouzi M, Hinton G (2020b) A simple framework for contrastive learning of visual representations. Proceedings of the 37th International Conference on Machine Learning 119:1597–1607

  • Chen Z, Villar S, Chen L, Bruna J (2019) On the equivalence between graph isomorphism testing and function approximation with gnns

  • Chen D, Lin Y, Li W, Li P, Zhou J, Sun X (2020a) Measuring and relieving the over-smoothing problem for graph neural networks from the topological view. Proc AAAI Conf Artif Intell 34(04):3438–3445. https://doi.org/10.1609/aaai.v34i04.5747

    Article  Google Scholar 

  • Cheng S, Zhang L, Jin B, Zhang Q, Lu X (2021) Drug target prediction using graph representation learning via substructures contrast

  • Choi E, Bahadori MT, Song L, Stewart WF, Sun J (2017) Gram: Graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’17, pp. 787–795. Association for Computing Machinery, New York, NY, USA. https://doi.org/10.1145/3097983.3098126

  • Choi E, Xiao C, Stewart WF, Sun J (2018) Mime: Multilevel medical embedding of electronic health records for predictive healthcare

  • Choudhary N, Rao N, Katariya S, Subbian K, Reddy CK (2021) Self-supervised hyperboloid representations from logical queries over knowledge graphs. In: Proceedings of the Web Conference 2021. WWW ’21, pp. 1373–1384. Association for Computing Machinery, New York, NY, USA. https://doi.org/10.1145/3442381.3449974

  • Chu Y, Kaushik AC, Wang X, Wang W, Zhang Y, Shan X, Salahub DR, Xiong Y, Wei D-Q (2021) Dti-cdf: a cascade deep forest model towards the prediction of drug-target interactions based on hybrid features. Brief Bioinform 22(1):451–462

    Google Scholar 

  • Cui G, Zhou J, Yang C, Liu Z (2020) Adaptive graph encoder for attributed graph embedding. In: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. KDD ’20, pp. 976–985. Association for Computing Machinery, NY, USA

  • Daiquocnguyen: QGNN. (2021) https://github.com/daiquocnguyen/QGNN

  • Danielegrattarola: Spektral (2021) https://github.com/danielegrattarola/spektral

  • Das A, Roy S, Bhattacharya U, Parui SK (2018) Document image classification with intra-domain transfer learning and stacked generalization of deep convolutional neural networks. In: 2018 24th International Conference on Pattern Recognition (ICPR), pp. 3180–3185. IEEE, Beijing, China. IEEE

  • Data61 C (2018) StellarGraph Machine Learning Library. GitHub

  • De Cao N, Kipf T (2018) MolGAN: An implicit generative model for small molecular graphs

  • Debnath AK, Lopez de Compadre RL, Debnath G, Shusterman AJ, Hansch C (1991) Structure–activity relationship of mutagenic aromatic and heteroaromatic nitro compounds correlation with molecular orbital energies and hydrophobicity. J Med Chem 34(2):786–797

    Google Scholar 

  • DeepGraphLearning: graphvite (2021). https://github.com/DeepGraphLearning/graphvite

  • Deepmind: Graph_nets (2021). https://github.com/deepmind/graph_nets

  • Defferrard M, Bresson X, Vandergheynst P (2016) Convolutional neural networks on graphs with fast localized spectral filtering. Adv Neural Inf Process Syst 29:3844–3852

    Google Scholar 

  • Dehmamy N, Barabási A-L, Yu R (2019) Understanding the representation power of graph neural networks in learning graph topology

  • Deng W, Zhang B, Zou W, Zhang X, Cheng X, Guan L, Lin Y, Lao G, Ye B, Li X et al (2019) Abnormal degree centrality associated with cognitive dysfunctions in early bipolar disorder. Front Psych 10:140

    Google Scholar 

  • Dengel A (1993) Initial learning of document structure. In: Proceedings of 2nd International Conference on Document Analysis and Recognition (ICDAR’93), pp. 86–90. IEEE Tsukuba, Japan. IEEE

  • Denk TI, Reisswig C (2019) Bertgrid: Contextualized embedding for 2d document representation and understanding

  • Devlin J, Chang M-W, Lee K, Toutanova K (2018) Bert: Pre-training of deep bidirectional transformers for language understanding

  • Devlin J, Chang M-W, Lee K, Toutanova K (2019) BERT: Pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota

  • Dhillon PS, Talukdar PP, Crammer K (2010) Learning better data representation using inference-driven metric learning. In: Proceedings of the ACL 2010 Conference Short Papers. ACLShort ’10, pp. 377–381. Association for Computational Linguistics, USA

  • Diligenti M, Frasconi P, Gori M (2003) Hidden tree Markov models for document image classification. IEEE Trans Pattern Anal Mach Intell 25(4):519–523

    Google Scholar 

  • Dong XL, de Melo G (2019) A robust self-learning framework for cross-lingual text classification. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 6306–6310. Association for Computational Linguistics, Hong Kong, China

  • Dong Y, Chawla NV, Swami A (2017) Metapath2vec: Scalable representation learning for heterogeneous networks. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 135–144. Association for Computing Machinery, New York, NY, USA. https://doi.org/10.1145/3097983.3098036

  • Du L, Wang Y, Song G, Lu Z, Wang J (2018) Dynamic network embedding: An extended approach for skip-gram based network embedding. In: IJCAI, vol. 2018, pp. 2086–2092. AAAI Press Stockholm, Sweden

  • Duvenaud D, Maclaurin D, Aguilera-Iparraguirre J, Gómez-Bombarelli R, Hirzel T, Aspuru-Guzik A, Adams RP (2015) Convolutional networks on graphs for learning molecular fingerprints

  • Dwivedi VP, Joshi CK, Laurent T, Bengio Y (2020) Benchmarking graph neural networks

  • Edwards H, Storkey A (2016) Towards a neural statistician. arXiv:1606.02185

  • Fei-Fei L, Fergus R, Perona P (2006) One-shot learning of object categories. IEEE Trans Pattern Anal Mach Intell 28(4):594–611

    Google Scholar 

  • Feng Y, You H, Zhang Z, Ji R, Gao Y (2019) Hypergraph neural networks. Proc AAAI Conf Artif Intell 33:3558–3565

    Google Scholar 

  • Feng Y-H, Zhang S-W, Shi J-Y (2020) Dpddi: a deep predictor for drug–drug interactions. BMC Bioinform 21(1):1–15

    Google Scholar 

  • Fey M, Lenssen JE (2019) Fast graph representation learning with PyTorch Geometric. In: ICLR Workshop on Representation Learning on Graphs and Manifolds, pp. 1–9

  • Finn C, Abbeel P, Levine S (2017) Model-agnostic meta-learning for fast adaptation of deep networks. In: International Conference on Machine Learning, pp. 1126–1135. PMLR

  • Fornito A, Zalesky A, Breakspear M (2013) Graph analysis of the human connectome: promise, progress, and pitfalls. Neuroimage 80:426–444

    Google Scholar 

  • Fouss F, Pirotte A, Renders J-M, Saerens M (2007) Random-walk computation of similarities between nodes of a graph with application to collaborative recommendation. IEEE Trans Knowl Data Eng 19(3):355–369

    Google Scholar 

  • Fout AM (2017) Protein interface prediction using graph convolutional networks. PhD thesis, Colorado State University

  • Gao L, Yang H, Zhou C, Wu J, Pan S, Hu Y (2018) Active discriminative network representation learning. In: IJCAI International Joint Conference on Artificial Intelligence, pp. 2142–2148. AAAI Press, Stockholm, Sweden

  • Garcia V, Bruna J (2017) Few-shot learning with graph neural networks. arXiv:1711.04043

  • Garg V, Jegelka S, Jaakkola T (2020) Generalization and representational limits of graph neural networks. Proceedings of the 37th International Conference on Machine Learning 119:3419–3430

  • Giles CL, Bollacker KD, Lawrence S (1998) Citeseer: An automatic citation indexing system. In: Proceedings of the Third ACM Conference on Digital Libraries. DL ’98, pp. 89–98. Association for Computing Machinery, New York, NY, USA. https://doi.org/10.1145/276675.276685

  • Gilmer J, Schoenholz SS, Riley PF, Vinyals O, Dahl GE (2017) Neural message passing for quantum chemistry. Proceedings of the 34th International Conference on Machine Learning 70:1263–1272

  • Godwin* J, Keck* T, Battaglia P, Bapst V, Kipf T, Li Y, Stachenfeld K, Veličković P, Sanchez-Gonzalez A (2020) Jraph: A library for graph neural networks in jax. http://github.com/deepmind/jraph

  • Gori M, Monfardini G, Scarselli F (2005) A new model for learning in graph domains. In: Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005., vol. 2, pp. 729–734. IEEE, Montreal, QC, Canada. IEEE

  • Grill J-B, Strub F, Altché F, Tallec C, Richemond PH, Buchatskaya E, Doersch C, Pires BA, Guo ZD, Azar MG, et al (2020) Bootstrap your own latent: a new approach to self-supervised learning

  • Grover A, Leskovec J (2016) node2vec: Scalable feature learning for networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 855–864. Association for Computing Machinery, New York, NY, USA

  • Gu S, Wang X, Shi C, Xiao D Self-supervised graph neural networks for multi-behavior recommendation

  • Guan C, Zhang Z, Li H, Chang H, Zhang Z, Qin Y, Jiang J, Wang X, Zhu W (2021) AutoGL: A library for automated graph learning. In: ICLR 2021 Workshop on Geometrical and Topological Representation Learning, pp. 1–8. https://openreview.net/forum?id=0yHwpLeInDn

  • Guo M, Chou E, Huang D-A, Song S, Yeung S, Fei-Fei L (2018) Neural graph matching networks for fewshot 3d action recognition. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 653–669. Springer, Cham

  • Hamilton WL, Ying R, Leskovec J (2017a) Inductive representation learning on large graphs. Proceedings of the 31st International Conference on Neural Information Processing Systems 30:1025–1035

  • Hamilton WL, Ying R, Leskovec J (2017b) Representation learning on graphs: Methods and applications

  • Hammond DK, Vandergheynst P, Gribonval R (2011) Wavelets on graphs via spectral graph theory. Appl Comput Harmon Anal 30(2):129–150

    MathSciNet  MATH  Google Scholar 

  • Hassani K, Khasahmadi AH (2020) Contrastive multi-view representation learning on graphs. Proceedings of the 37th International Conference on Machine Learning 119, 4116–4126. PMLR

  • He R, McAuley J (2016) Ups and downs: Modeling the visual evolution of fashion trends with one-class collaborative filtering. In: Proceedings of the 25th International Conference on World Wide Web, pp. 507–517. International World Wide Web Conferences Steering Committee, Republic and Canton of Geneva, CHE

  • He R, Packer C, McAuley J (2016) Learning compatibility across categories for heterogeneous item recommendation. In: 2016 IEEE 16th International Conference on Data Mining (ICDM), pp. 937–942. IEEE, Barcelona, Spain. IEEE

  • He K, Fan H, Wu Y, Xie S, Girshick R (2020) Momentum contrast for unsupervised visual representation learning. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9729–9738. IEEE Seattle, WA, USA

  • Henaff M, Bruna J, LeCun Y (2015) Deep convolutional networks on graph-structured data. arXiv:1506.05163

  • Hjelm RD, Fedorov A, Lavoie-Marchildon S, Grewal K, Bachman P, Trischler A, Bengio Y (2018) Learning deep representations by mutual information estimation and maximization

  • Hu Z, Fan C, Chen T, Chang K-W, Sun Y (2019a) Pre-training graph neural networks for generic structural feature extraction

  • Hu W, Liu B, Gomes J, Zitnik M, Liang P, Pande V, Leskovec J (2019b) Strategies for pre-training graph neural networks

  • Hu Z, Dong Y, Wang K, Chang K-W, Sun Y (2020a) Gpt-gnn: Generative pre-training of graph neural networks. In: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 1857–1867. Association for Computing Machinery, New York, NY, USA

  • Hu W, Fey M, Zitnik M, Dong Y, Ren H, Liu B, Catasta M, Leskovec J (2020b) Open graph benchmark: Datasets for machine learning on graphs

  • Hu J, Ruder S, Siddhant A, Neubig G, Firat O, Johnson M (2020c) XTREME: A massively multilingual multi-task benchmark for evaluating cross-lingual generalisation. Proceedings of the 37th International Conference on Machine Learning 119, 4411–4421

  • Hu S, Xiong Z, Qu M, Yuan X, Côté M-A, Liu Z, Tang J (2020d) Graph policy network for transferable active learning on graphs

  • Hu Y, Li X, Wang Y, Wu Y, Zhao Y, Yan C, Yin J, Gao Y (2021) Adaptive hypergraph auto-encoder for relational data clustering. IEEE Transactions on Knowledge and Data Engineering

  • Huang C, Xu H, Xu Y, Dai P, Xia L, Lu M, Bo L, Xing H, Lai X, Ye Y (2021a) Knowledge-aware coupled graph neural network for social recommendation. Proc AAAI Conf Artif Intell 35:4115–4122

    Google Scholar 

  • Huang H, Shi R, Zhou W, Wang X, Jin H, Fu X (2021b) Temporal heterogeneous information network embedding. In: IJCAI, pp. 1470–1476

  • Hwang D, Park J, Kwon S, Kim K-M, Ha J-W, Kim HJ (2020) Self-supervised auxiliary learning with meta-paths for heterogeneous graphs

  • Ioannidis VN, Marques AG, Giannakis GB (2019) A recurrent graph neural network for multi-relational data. In: ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8157–8161. IEEE, Brighton, UK. IEEE

  • Jain A, Zamir AR, Savarese S, Saxena A (2016) Structural-rnn: Deep learning on spatio-temporal graphs. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5308–5317. IEEE, Las Vegas, NV, USA

  • Jamasb AR, Lio P, Blundell T (2020) Graphein - a python library for geometric deep learning and network analysis on protein structures. https://doi.org/10.1101/2020.07.15.204701

  • Jiao Y, Xiong Y, Zhang J, Zhang Y, Zhang T, Zhu Y (2020) Sub-graph contrast for scalable self-supervised graph representation learning. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 222–231. IEEE, Sorrento, Italy. IEEE

  • Ji H, Wang X, Shi C, Wang B, Yu P (2021) Heterogeneous graph propagation network. IEEE Transactions on Knowledge and Data Engineering

  • Jin W, Derr T, Liu H, Wang Y, Wang S, Liu Z, Tang J (2020) Self-supervised learning on graphs: deep insights and new direction

  • Jin W, Liu X, Zhao X, Ma Y, Shah N, Tang J (2021a) Automated self-supervised learning for graphs

  • Jin M, Zheng Y, Li Y-F, Gong C, Zhou C, Pan S (2021b) Multi-scale contrastive Siamese networks for self-supervised graph representation learning

  • Johnson DD (2016) Learning Graphical State Transitions. In: Proceedings of 5th International Conference on Learning Representations, pp. 1–19. ICLR, Palais des Congrès Neptune, Toulon, France

  • Johnson J, Gupta A, Fei-Fei L (2018) Image generation from scene graphs. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1219–1228. IEEE, Salt Lake City, UT, USA

  • Kang L, Kumar J, Ye P, Li Y, Doermann D (2014) Convolutional neural networks for document image classification. In: 2014 22nd International Conference on Pattern Recognition, pp. 3168–3172. IEEE Stockholm, Sweden. IEEE

  • Kawamoto T, Tsubaki M, Obuchi T (2019) Mean-field theory of graph neural networks in graph partitioning. J Stat Mech 2019(12):124007

    MathSciNet  MATH  Google Scholar 

  • Kearnes S, McCloskey K, Berndl M, Pande V, Riley P (2016) Molecular graph convolutions: moving beyond fingerprints. J Comput Aided Mol Des 30(8):595–608

    Google Scholar 

  • Keriven N, Peyré G (2019) Universal invariant and equivariant graph neural networks. Adv Neural Inf Process Syst 32:7092–7101

    Google Scholar 

  • Kim D, Oh A (2020) How to find your friendly neighborhood: Graph attention design with self-supervision. In: International Conference on Learning Representations, pp. 1–25. ICLR, Vienna, Austria

  • Kim J, Kim T, Kim S, Yoo CD (2019) Edge-labeling graph neural network for few-shot learning. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11–20

  • Kingma DP, Welling M (2013) Auto-encoding variational bayes. arXiv:1312.6114

  • Kipf TN, Welling M (2016a) Semi-supervised classification with graph convolutional networks

  • Kipf TN, Welling M (2016b) Variational graph auto-encoders

  • Klicpera J, Weißenberger S, Günnemann S (2019) Diffusion improves graph learning. Adv Neural Inf Process Syst 32:13354–13366

    Google Scholar 

  • Knyazev B, Taylor GW, Amer MR (2019) Understanding attention and generalization in graph neural networks

  • Kumar S, Hamilton WL, Leskovec J, Jurafsky D (2018) Community interaction and conflict on the web. In: Proceedings of the 2018 World Wide Web Conference on World Wide Web, pp. 933–943. International World Wide Web Conferences Steering Committee

  • Kumar S, Zhang X, Leskovec J (2019) Predicting dynamic embedding trajectory in temporal interaction networks. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 1269–1278. ACM

  • Kunal K, Dhar T, Madhusudan M, Poojary J, Sharma A, Xu W, Burns SM, Hu J, Harjani R, Sapatnekar SS (2020) Gana: Graph convolutional network based automated netlist annotation for analog circuits. In: 2020 Design, Automation & Test in Europe Conference & Exhibition (DATE), pp. 55–60. IEEE

  • Lample G, Conneau A (2019) Cross-lingual language model pretraining

  • Landrieu L, Simonovsky M (2018) Large-scale point cloud semantic segmentation with superpoint graphs. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4558–4567. IEEE Salt Lake City, UT, USA

  • Leskovec J, Krevl A (2014) SNAP Datasets: Stanford large network dataset collection. http://snap.stanford.edu/data

  • Leskovec J, Kleinberg J, Faloutsos C (2005) Graphs over time: densification laws, shrinking diameters and possible explanations. In: Proceedings of the Eleventh ACM SIGKDD International Conference on Knowledge Discovery in Data Mining, pp. 177–187

  • Leskovec J, Adamic LA, Huberman BA (2007) The dynamics of viral marketing. ACM Trans Web (TWEB) 1(1):5

    Google Scholar 

  • Leskovec J, Lang K, Dasgupta A, Mahoney M (2008) Community structure in large networks: Natural cluster sizes and the absence of large well-defined clusters. arXiv:0810.1355

  • Leskovec J, Lang KJ, Dasgupta A, Mahoney MW (2009) Community structure in large networks: natural cluster sizes and the absence of large well-defined clusters. Internet Math 6(1):29–123

    MathSciNet  MATH  Google Scholar 

  • Leskovec J, Huttenlocher D, Kleinberg J (2010) Signed networks in social media. In: Proceedings of the SIGCHI conference on human factors in computing systems, pp. 1361–1370

  • Levie R, Huang W, Bucci L, Bronstein M.M, Kutyniok G (2019) Transferability of spectral graph convolutional neural networks

  • Lewis D, Agam G, Argamon S, Frieder O, Grossman D, Heard J (2006) Building a test collection for complex document information processing. In: Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 665–666. Association for Computing Machinery, New York, NY, USA

  • Li MM, Zitnik M (2021) Deep contextual learners for protein networks

  • Li Y, Tarlow D, Brockschmidt M, Zemel R (2015) Gated graph sequence neural networks

  • Li Y, Yu R, Shahabi C, Liu Y (2017) Diffusion convolutional recurrent neural network: data-driven traffic forecasting

  • Li Q, Han Z, Wu X-M (2018a) Deeper insights into graph convolutional networks for semi-supervised learning. In: Thirty-second AAAI conference on artificial intelligence, pp. 3538–3545. AAAI Press, New Orleans, USA

  • Li Y, Vinyals O, Dyer C, Pascanu R, Battaglia P (2018b) Learning deep generative models of graphs

  • Li Z, Chen Q, Koltun V (2018c) Combinatorial optimization with graph convolutional networks and guided tree search

  • Li Y, Ouyang W, Zhou B, Shi J, Zhang C, Wang X (2018d) Factorizable net: an efficient subgraph-based framework for scene graph generation. In: Ferrari V, Hebert M, Sminchisescu C, Weiss Y (eds) Computer vision—ECCV 2018. Springer, Cham, pp 346–363

    Google Scholar 

  • Li P, Wang J, Qiao Y, Chen H, Yu Y, Yao X, Gao P, Xie G, Song S (2020a) Learn molecular representations from large-scale unlabeled molecules for drug discovery. arXiv:2012.11175

  • Li Z, Kumar M, Headden W, Yin B, Wei Y, Zhang Y, Yang Q (2020c) Learn to cross-lingual transfer with meta graph learning across heterogeneous languages. In: Proceedings of the 2020 conference on empirical methods in natural language processing (EMNLP), pp. 2290–2301. Association for computational linguistics

  • Li S, Xu F, Wang R, Zhong S (2021a) Self-supervised incremental deep graph learning for ethereum phishing scam detection

  • Li Y, Jin W, Xu H, Tang J (2020b) Deeprobust: a pytorch library for adversarial attacks and defenses. arXiv:2005.06149

  • Li I, Yan V, Li T, Qu R, Radev D (2021b) Unsupervised cross-domain prerequisite chain learning using variational graph autoencoders

  • Li P, Wang J, Qiao Y, Chen H, Yu Y, Yao X, Gao P, Xie G, Song S (2021c) An effective self-supervised framework for learning expressive molecular global representations to drug discovery. Brief Bioinform 22(6):109

    Google Scholar 

  • Liu Y, Lee J, Park M, Kim S, Yang E, Hwang SJ, Yang Y (2018) Learning to propagate labels: transductive propagation network for few-shot learning. arXiv:1805.10002

  • Li S, Zhou J, Xu T, Huang L, Wang F, Xiong H, Huang W, Dou D, Xiong H (2021d) Structure-aware interactive graph neural networks for the prediction of protein–ligand binding affinity. In: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, pp. 975–985

  • Lin J, Cai Q, Lin M (2021a) Multi-label classification of fundus images with graph convolutional network and self-supervised learning. IEEE Signal Process Lett 28:454–458

    Google Scholar 

  • Lin Q, Zhu F-Y, Shu Y-Q, Zhu P-W, Ye L, Shi W-Q, Min Y-L, Li B, Yuan Q, Shao Y (2021b) Altered brain network centrality in middle-aged patients with retinitis pigmentosa: a resting-state functional magnetic resonance imaging study. Brain Behav 11(2):01983

    Google Scholar 

  • Linmei H, Yang T, Shi C, Ji H, Li X (2019) Heterogeneous graph attention networks for semi-supervised short text classification. In: Proceedings of the 2019 conference on empirical methods in natural language processing and the 9th international joint conference on natural language processing (EMNLP-IJCNLP), pp. 4821–4830. Association for Computational Linguistics, Hong Kong, China

  • Liu Q, Nickel M, Kiela D (2019a) Hyperbolic graph neural networks. Advances in Neural Information Processing Systems 32

  • Liu L, Zhou T, Long G, Jiang J, Yao L, Zhang C (2019b) Prototype propagation networks (PPN) for weakly-supervised few-shot learning on category graph

  • Liu N, Tan Q, Li Y, Yang H, Zhou J, Hu X (2019c) Is a single vector enough? exploring node polysemy for network embedding. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 932–940. Association for Computing Machinery, New York, NY, USA

  • Liu Z, Huang C, Yu Y, Fan B, Dong J (2020a) Fast attributed multiplex heterogeneous network embedding. In: Proceedings of the 29th ACM International Conference on Information & Knowledge Management, pp. 995–1004

  • Liu M, Zhu K, Gu J, Shen L, Tang X, Sun N, Pan DZ (2020b) Towards decrypting the art of analog layout: Placement quality prediction via transfer learning. In: 2020 Design, Automation & Test in Europe Conference & Exhibition (DATE), pp. 496–501. IEEE

  • Liu Q, Hu Z, Jiang R, Zhou M (2020c) Deepcdr: a hybrid graph convolutional network for predicting cancer drug response. Bioinformatics 36:911–918

    Google Scholar 

  • Liu Z, Li X, You Z, Yang T, Fan W, Yu P (2021a) Medical triage chatbot diagnosis improvement via multi-relational hyperbolic graph neural network. In: Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 1965–1969

  • Liu Y, Pan S, Jin M, Zhou C, Xia F, Yu PS (2021b) Graph self-supervised learning: a survey

  • Liu M, Turner WJ, Kokai GF, Khailany B, Pan DZ, Ren H (2021c) Parasitic-aware analog circuit sizing with graph neural networks and bayesian optimization. In: 2021 Design, Automation & Test in Europe Conference & Exhibition (DATE), pp. 1372–1377. IEEE

  • Liu X, Zhang F, Hou Z, Mian L, Wang Z, Zhang J, Tang J (2021d) Self-supervised learning: generative or contrastive. IEEE Trans Knowl Data Eng Early Access. https://doi.org/10.1109/TKDE.2021.3090866

    Article  Google Scholar 

  • Liu Y, Li M, Li X, Giunchiglia F, Feng X, Guan R (2022) Few-shot node classification on attributed networks with graph meta-learning. In: Proceedings of the 45th international ACM SIGIR conference on research and development in information retrieval, pp. 471–481

  • Long X, Little G, Treit S, Beaulieu C, Gong G, Lebel C (2020) Altered brain white matter connectome in children and adolescents with prenatal alcohol exposure. Brain Struct Funct 225(3):1123–1133

    Google Scholar 

  • Loukas A (2019) What graph neural networks cannot learn: depth vs width

  • Lu C, Liu Q, Wang C, Huang Z, Lin P, He L (2019) Molecular property prediction: a multilevel quantum interactions modeling perspective. Proc AAAI Conf Artif Intell 33:1052–1060

    Google Scholar 

  • Maiya AS (2020) ktrain: a low-code library for augmented machine learning. arXiv:2004.10703

  • Mallat S (1999) A wavelet tour of signal processing. Elsevier, Amsterdam

    MATH  Google Scholar 

  • Manessi F, Rozza A (2021) Graph-based neural network models with multiple self-supervised auxiliary tasks. Pattern Recogn Lett 148:15–21

    Google Scholar 

  • Marcheggiani D, Perez-Beltrachini L (2018) Deep graph convolutional encoders for structured data to text generation. arXiv:1810.09995

  • Marcheggiani D, Bastings J, Titov I (2018) Exploiting semantics in neural machine translation with graph convolutional networks. arXiv:1804.08313

  • Ma Y, Ren H, Khailany B, Sikka H, Luo L, Natarajan K, Yu B (2019) High performance graph convolutional networks with applications in testability analysis. In: Proceedings of the 56th Annual Design Automation Conference 2019, pp. 1–6

  • Maron H, Ben-Hamu H, Shamir N, Lipman Y (2018) Invariant and equivariant graph networks

  • Maron H, Fetaya E, Segol N, Lipman Y (2019) On the universality of invariant networks. In: International Conference on Machine Learning, pp. 4363–4371. PMLR

  • Mavromatis C, Karypis G (2020) Graph InfoClust: Leveraging cluster-level node information for unsupervised graph representation learning

  • McAuley JJ, Leskovec J (2012) Learning to discover social circles in ego networks. In: NIPS, vol. 2012, pp. 548–56. Citeseer

  • McAuley J, Pandey R, Leskovec J (2015a) Inferring networks of substitutable and complementary products. In: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 785–794. Association for Computing Machinery, New York, NY, USA

  • McAuley J, Targett C, Shi Q, Van Den Hengel A (2015b) Image-based recommendations on styles and substitutes. In: Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 43–52

  • McCallum AK, Nigam K, Rennie J, Seymore K (2000) Automating the construction of internet portals with machine learning. Inf Retrieval 3(2):127–163

    Google Scholar 

  • McPherson M, Smith-Lovin L, Cook JM (2001) Birds of a feather: homophily in social networks. Ann Rev Sociol 27(1):415–444

    Google Scholar 

  • Merkwirth C, Lengauer T (2005) Automatic generation of complementary descriptors with molecular graph networks. J Chem Inf Model 45(5):1159–1168

    Google Scholar 

  • Micheli A, Sona D, Sperduti A (2004) Contextual processing of structured data by recursive cascade correlation. IEEE Trans Neural Netw 15(6):1396–1410

    Google Scholar 

  • Mikolov T, Chen K, Corrado G, Dean J (2013a) Efficient estimation of word representations in vector space

  • Mikolov T, Sutskever I, Chen K, Corrado GS, Dean J (2013b) Distributed representations of words and phrases and their compositionality. In: Advances in Neural Information Processing Systems, pp. 3111–3119

  • Mirhoseini A, Goldie A, Yazgan M, Jiang JW, Songhori E, Wang S, Lee Y-J, Johnson E, Pathak O, Nazi A et al (2021) A graph placement methodology for fast chip design. Nature 594(7862):207–212

    Google Scholar 

  • Mislove A, Koppula HS, Gummadi KP, Druschel P, Bhattacharjee B (2008) Growth of the flickr social network. In: Proceedings of the First Workshop on Online Social Networks. WOSN ’08, pp. 25–30. Association for Computing Machinery, New York, NY, USA. https://doi.org/10.1145/1397735.1397742

  • Monti F, Bronstein MM, Bresson X (2017) Geometric matrix completion with recurrent multi-graph neural networks

  • Morris C, Ritzert M, Fey M, Hamilton WL, Lenssen JE, Rattan G, Grohe M (2019) Weisfeiler and leman go neural: higher-order graph neural networks. Proc AAAI Conf Artif Intell 33(01):4602–4609

    Google Scholar 

  • Narasimhan M, Lazebnik S, Schwing AG (2018) Out of the box: Reasoning with graph convolution nets for factual visual question answering

  • Neudorf J, Ekstrand C, Kress S, Borowsky R (2020) Brain structural connectivity predicts brain functional complexity: diffusion tensor imaging derived centrality accounts for variance in fractal properties of functional magnetic resonance imaging signal. Neuroscience 438:1–8

    Google Scholar 

  • Newman ME (2005) A measure of betweenness centrality based on random walks. Soc Netw 27(1):39–54

    MathSciNet  Google Scholar 

  • Nguyen TH, Grishman R (2018) Graph convolutional networks with argument-aware pooling for event detection. In: Thirty-second AAAI Conference on Artificial Intelligence, pp. 5900–5907

  • Nguyen T, Le H, Quinn TP, Nguyen T, Le TD, Venkatesh S (2021) Graphdta: predicting drug–target binding affinity with graph neural networks. Bioinformatics 37(8):1140–1147

    Google Scholar 

  • Nt H, Maehara T (2019) Revisiting graph neural networks: All we have is low-pass filters

  • Okuda M, Satoh S, Sato Y, Kidawara Y (2021) Unsupervised common particular object discovery and localization by analyzing a match graph. In: ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1540–1544. IEEE

  • Oono K, Suzuki T (2019) Graph neural networks exponentially lose expressive power for node classification

  • Oord A, Li Y, Vinyals O (2018) Representation learning with contrastive predictive coding

  • Opolka FL, Solomon A, Cangea C, Veličković P, Liò P (2019) Spatio-temporal deep graph infomax

  • Ouali Y, Hudelot C, Tami M (2020) An overview of deep semi-supervised learning

  • Ou M, Cui P, Pei J, Zhang Z, Zhu W (2016) Asymmetric transitivity preserving graph embedding. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1105–1114. Association for Computing Machinery, New York, NY, USA

  • Ozaki K, Shimbo M, Komachi M, Matsumoto Y (2011) Using the mutual k-nearest neighbor graphs for semi-supervised classification on natural language data. In: Proceedings of the Fifteenth Conference on Computational Natural Language Learning, pp. 154–162

  • PaddlePaddle: PGL (2021) https://github.com/PaddlePaddle/PGL

  • Page L, Brin S, Motwani R, Winograd T (1999) The pagerank citation ranking: bringing order to the web. Technical report, Stanford InfoLab

  • Pan S, Hu R, Fung S-F, Long G, Jiang J, Zhang C (2019) Learning graph embedding with adversarial training methods. IEEE Trans Cybern 50(6):2475–2487

    Google Scholar 

  • Pan S, Hu R, Long G, Jiang J, Yao L, Zhang C (2018) Adversarially regularized graph autoencoder for graph embedding. In: Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, IJCAI-18, pp. 2609–2615. International Joint Conferences on Artificial Intelligence Organization

  • Paranjape A, Benson AR, Leskovec J (2017) Motifs in temporal networks. In: Proceedings of the Tenth ACM International Conference on Web Search and Data Mining, pp. 601–610

  • Park J, Cho J, Chang HJ, Choi JY (2021) Unsupervised hyperbolic representation learning via message passing auto-encoders. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5516–5526

  • Park C, Kim D, Han J, Yu H (2020) Unsupervised attributed multiplex network embedding. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 5371–5378

  • Park J, Lee M, Chang HJ, Lee K, Choi JY (2019) Symmetric graph convolutional autoencoder for unsupervised graph representation learning. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 6519–6528

  • Peng Z, Dong Y, Luo M, Wu X-M, Zheng Q (2020a) Self-supervised graph representation learning via global context prediction

  • Peng Z, Huang W, Luo M, Zheng Q, Rong Y, Xu T, Huang J (2020b) Graph representation learning via graphical mutual information maximization. In: Proceedings of The Web Conference 2020, pp. 259–270

  • Perozzi B, Al-Rfou R, Skiena S (2014) Deepwalk: Online learning of social representations. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 701–710. Association for Computing Machinery New York, NY, USA

  • Pise NN, Kulkarni P (2008) A survey of semi-supervised learning methods. In: 2008 International Conference on Computational Intelligence and Security, vol. 2, pp. 30–34. IEEE

  • Prakash VJ, Nithya DL (2014) A survey on semi-supervised learning techniques

  • Qi X, Liao R, Jia J, Fidler S, Urtasun R (2017) 3d graph neural networks for rgbd semantic segmentation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 5199–5208

  • Qiu J, Chen Q, Dong Y, Zhang J, Yang H, Ding M, Wang K, Tang J (2020) Gcc: Graph contrastive coding for graph neural network pre-training. In: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 1150–1160. Association for Computing Machinery, New York, NY, USA

  • Qiu J, Dong Y, Ma H, Li J, Wang K, Tang J (2018a) Network embedding as matrix factorization: Unifying deepwalk, line, pte, and node2vec. In: Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining, pp. 459–467

  • Qiu J, Tang J, Ma H, Dong Y, Wang K, Tang J (2018b) Deepinf: Social influence prediction with deep learning. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2110–2119. Association for Computing Machinery

  • Qi S, Wang W, Jia B, Shen J, Zhu S-C (2018) Learning human-object interactions by graph parsing neural networks. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 401–417. Springer Cham

  • Raizman R, Tavor I, Biegon A, Harnof S, Hoffmann C, Tsarfaty G, Fruchter E, Tatsa-Laur L, Weiser M, Livny A (2020) Traumatic brain injury severity in a network perspective: a diffusion MRI based connectome study. Sci Rep 10(1):1–12

    Google Scholar 

  • Rakesh V, Wang S, Shu K, Liu H (2019) Linked variational autoencoders for inferring substitutable and supplementary items. In: Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining, pp. 438–446

  • Ravi S, Larochelle H (2016) Optimization as a model for few-shot learning

  • Ren H, Kokai GF, Turner WJ, Ku T-S (2020a) Paragraph: Layout parasitics and device parameter prediction using graph neural networks. In: 2020 57th ACM/IEEE Design Automation Conference (DAC), pp. 1–6. IEEE

  • Ren Y, Liu B, Huang C, Dai P, Bo L, Zhang J (2020b) Hdgi: An unsupervised graph neural network for representation learning in heterogeneous graph. In: AAAI Workshop, pp. 1638–1645

  • Ribeiro LF, Saverese PH, Figueiredo DR (2017) struc2vec: Learning node representations from structural identity. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 385–394. Association for Computing Machinery, New York, NY, USA

  • Rohani N, Eslahchi C, Katanforoush A (2020) Iscmf: integrated similarity-constrained matrix factorization for drug–drug interaction prediction. Netw Model Anal Health Inform Bioinform 9(1):1–8

    Google Scholar 

  • Rohban MH, Rabiee HR (2012) Supervised neighborhood graph construction for semi-supervised classification. Pattern Recogn 45(4):1363–1372

    MATH  Google Scholar 

  • Rong Y, Bian Y, Xu T, Xie W, Wei Y, Huang W, Huang J (2020) Self-supervised graph transformer on large-scale molecular data

  • Roweis ST, Saul LK (2000) Nonlinear dimensionality reduction by locally linear embedding. Science 290(5500):2323–2326

    Google Scholar 

  • Rozemberczki B, Scherer P, He Y, Panagopoulos G, Riedel A, Astefanoaei M, Kiss O, Beres F, Lopez G, Collignon N, Sarkar R (2021) PyTorch geometric temporal: spatiotemporal signal processing with neural machine learning models

  • Ruiz L, Chamon L, Ribeiro A (2020) Graphon neural networks and the transferability of graph neural networks. Advances in Neural Information Processing Systems 33

  • Sakhuja A (2021) Unsupervised learning of latent edge types from multi-relational data. PhD thesis, Applied sciences: school of computing science

  • Salakhutdinov R, Tenenbaum J, Torralba A (2012) One-shot learning with a hierarchical nonparametric bayesian model. In: Proceedings of ICML Workshop on Unsupervised and Transfer Learning, pp. 195–206. JMLR Workshop and Conference Proceedings

  • Santoro A, Bartunov S, Botvinick M, Wierstra D, Lillicrap T (2016) Meta-learning with memory-augmented neural networks. In: International Conference on Machine Learning, pp. 1842–1850. PMLR

  • Sato R (2020) A survey on the expressive power of graph neural networks

  • Scarselli F, Gori M, Tsoi AC, Hagenbuchner M, Monfardini G (2008) The graph neural network model. IEEE Trans Neural Netw 20(1):61–80

    Google Scholar 

  • Scarselli F, Tsoi AC, Hagenbuchner M (2018) The Vapnik-Chervonenkis dimension of graph and recursive neural networks. Neural Netw 108:248–259

    MATH  Google Scholar 

  • Schlichtkrull M, Kipf TN, Bloem P, Van Den Berg R, Titov I, Welling M (2018) Modeling relational data with graph convolutional networks. In: European Semantic Web Conference, pp. 593–607. Springer

  • Sen P, Namata G, Bilgic M, Getoor L, Galligher B, Eliassi-Rad T (2008) Collective classification in network data. AI Magn 29(3):93–93

    Google Scholar 

  • SeongokRyu: Graph-neural-networks (2021). https://github.com/SeongokRyu/Graph-neural-networks

  • Shchur O, Mumme M, Bojchevski A, Günnemann S (2018) Pitfalls of graph neural network evaluation

  • Shin C, Doermann D, Rosenfeld A (2001) Classification of document pages using structure-based features. Int J Doc Anal Recogn 3(4):232–247

    Google Scholar 

  • Song Z, Yang X, Xu Z, King I (2021) Graph-based semi-supervised learning: a comprehensive review

  • Song L, Zhang Y, Wang Z, Gildea D (2018) A graph-to-sequence model for AMR-to-text generation

  • Sperduti A, Starita A (1997) Supervised neural networks for the classification of structures. IEEE Trans Neural Netw 8(3):714–735

    Google Scholar 

  • Spitzer F (2013) Principles of random walk, vol 34. Springer, Cham

    MATH  Google Scholar 

  • Subramonian A (2021) MOTIF-driven contrastive learning of graph representations. AAAI 35(18):15980–15981

    Google Scholar 

  • Sun F-Y, Hoffmann J, Verma V, Tang J (2019) Infograph: Unsupervised and semi-supervised graph-level representation learning via mutual information maximization

  • Sun Q, Li J, Peng H, Wu J, Ning Y, Yu PS, He L (2021b) Sugar: Subgraph neural network with reinforcement pooling and self-supervised mutual information mechanism. In: Proceedings of the web conference 2021, pp. 2081–2091

  • Sun K, Lin Z, Zhu Z (2020) Multi-stage self-supervised learning for graph convolutional networks on graphs with few labeled nodes. In: Proceedings of the AAAI conference on artificial intelligence, vol. 34, pp. 5892–5899

  • Sun Y, Shan Y, Tang C, Hu Y, Dai Y, Yu J, Sun J, Huang F, Si L (2021c) Unsupervised learning of deterministic dialogue structure with edge-enhanced graph auto-encoder. In: Proceedings of the AAAI conference on artificial intelligence, vol. 35, pp. 13869–13877

  • Sun X, Yin H, Liu B, Chen H, Cao J, Shao Y, Viet Hung NQ (2021d) Heterogeneous hypergraph embedding for graph classification. In: Proceedings of the 14th ACM international conference on web search and data mining, pp. 725–733

  • Sung F, Yang Y, Zhang L, Xiang T, Torr PH, Hospedales TM (2018) Learning to compare: Relation network for few-shot learning. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 1199–1208

  • Sun L, Zhang Z, Zhang J, Wang F, Peng H, Su S, Philip SY (2021a) Hyperbolic variational graph neural network for modeling dynamic graphs. Proc AAAI Conf Artif Intell 35:4375–4383

    Google Scholar 

  • Svjan5: GNNs-for-NLP (2021). https://github.com/svjan5/GNNs-for-NLP

  • Taheri A, Gimpel K, Berger-Wolf T (2019) Learning to represent the evolution of dynamic graphs with recurrent models. In: Companion Proceedings of The 2019 World Wide Web Conference. WWW ’19, pp. 301–307. Association for Computing Machinery, New York, NY, USA. https://doi.org/10.1145/3308560.3316581

  • Taherkhani F, Kazemi H, Nasrabadi NM (2019) Matrix completion for graph-based deep semi-supervised learning. Proc AAAI Conf Artif Intell 33:5058–5065

    Google Scholar 

  • Tang J, Liu H (2012) Unsupervised feature selection for linked social media data. In: Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 904–912

  • Tang J, Qu M, Wang M, Zhang M, Yan J, Mei Q (2015b) Line: Large-scale information network embedding. In: Proceedings of the 24th International Conference on World Wide Web, pp. 1067–1077. International World Wide Web Conferences Steering Committee, Republic and Canton of Geneva, CHE

  • Tang J, Zhang J, Yao L, Li J, Zhang L, Su Z (2008) Arnetminer: Extraction and mining of academic social networks. In: Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’08, pp. 990–998. Association for Computing Machinery, New York, NY, USA. https://doi.org/10.1145/1401890.1402008

  • Tang J, Qu M, Mei Q (2015a) Pte: Predictive text embedding through large-scale heterogeneous text networks. In: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1165–1174. Association for Computing Machinery, New York, NY, USA

  • Tatonetti NP, Patrick PY, Daneshjou R, Altman RB (2012) Data-driven prediction of drug effects and interactions. Sci Transl Med 4(125):125

    Google Scholar 

  • Te G, Hu W, Zheng A, Guo Z (2018) Rgcnn: Regularized graph cnn for point cloud segmentation. In: Proceedings of the 26th ACM International Conference on Multimedia, pp. 746–754. Association for Computing Machinery, New York, NY, USA

  • Thudm: Cogdl (2021). https://github.com/THUDM/cogdl

  • Thunlp: OpenNE (2021). https://github.com/thunlp/OpenNE/tree/pytorch

  • Tu K, Cui P, Wang X, Yu PS, Zhu W (2018) Deep recursive network embedding with regular equivalence. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2357–2366. Association for Computing Machinery, New York, NY, USA

  • Turkiewicz J, Bhatt RR, Wang H, Vora P, Krause B, Sauk JS, Jacobs JP, Bernstein CN, Kornelsen J, Labus JS et al (2021) Altered brain structural connectivity in patients with longstanding gut inflammation is correlated with psychological symptoms and disease duration. NeuroImage 30:102613

    Google Scholar 

  • Van Engelen JE, Hoos HH (2020) A survey on semi-supervised learning. Mach Learn 109(2):373–440

    MathSciNet  MATH  Google Scholar 

  • Veličković P, Cucurull G, Casanova A, Romero A, Lio P, Bengio Y (2017) Graph attention networks

  • Velickovic P, Fedus W, Hamilton WL, Liò P, Bengio Y, Hjelm RD (2019) Deep graph infomax. ICLR 2(3):4

    Google Scholar 

  • Verma S, Zhang Z-L (2019) Stability and generalization of graph convolutional neural networks. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 1539–1548. Association for Computing Machinery, New York, NY, USA

  • Vincent P, Larochelle H, Bengio Y, Manzagol P-A (2008) Extracting and composing robust features with denoising autoencoders. In: Proceedings of the 25th International Conference on Machine Learning, pp. 1096–1103

  • Vinyals O, Blundell C, Lillicrap T, Wierstra D, et al. (2016) Matching networks for one shot learning. Advances in neural information processing systems 29

  • Wan X (2009) Co-training for cross-lingual sentiment classification. In: Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP, pp. 235–243

  • Wang M, Zheng D, Ye Z, Gan Q, Li M, Song X, Zhou J, Ma C, Yu L, Gai Y, Xiao T, He T, Karypis G, Li J, Zhang Z (2021d) DGL. https://github.com/dmlc/dgl

  • Wang C, Pan S, Long G, Zhu X, Jiang J (2017) Mgae: Marginalized graph autoencoder for graph clustering. In: Proceedings of the 2017 ACM on conference on information and knowledge management, pp. 889–898

  • Wang H, Wang K, Yang J, Shen L, Sun N, Lee H-S, Han S (2020) Gcn-rl circuit designer: Transferable transistor sizing with graph neural networks and reinforcement learning. In: 2020 57th ACM/IEEE design automation conference (DAC), pp. 1–6. IEEE

  • Wang Y, Sun Y, Liu Z, Sarma SE, Bronstein MM, Solomon JM (2019a) Dynamic graph CNN for learning on point clouds. Acm Trans Graph 38(5):1–12

    Google Scholar 

  • Wan S, Pan S, Yang J, Gong C (2020) Contrastive and generative graph convolutional networks for graph-based semi-supervised learning

  • Wang Z, Jiang Z, Ren Z, Tang J, Yin D (2018) A path-constrained framework for discriminating substitutable and complementary products in e-commerce. In: Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining, pp. 619–627

  • Wang Z, Liu X, Yang P, Liu S, Wang Z (2021b) Cross-lingual text classification with heterogeneous graph neural network

  • Wang P, Agarwal K, Ham C, Choudhury S, Reddy CK (2021a) Self-supervised learning of contextual embeddings for link prediction in heterogeneous networks. In: Proceedings of the Web Conference 2021, pp. 2946–2957

  • Wang D, Cui P, Zhu W (2016) Structural deep network embedding. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1225–1234. Association for Computing Machinery, New York, NY, USA

  • Wang Y, Fass J, Stern C, hodera J (2019b) Luolibrary for graph neural networks in jaxK. Graph nets for partial charge prediction. arXiv:1909.07903

  • Wang H, Xu T, Liu Q, Lian D, Chen E, Du D, Wu H, Su W (2019c) MCNE: an end-to-end framework for learning multiple conditional network representations of social network. In: Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining, pp. 1064–1072. Association for computing machinery, New York, NY, USA

  • Wang Y, Min Y, Chen X, Wu J (2021c) Multi-view graph contrastive representation learning for drug-drug interaction prediction. In: Proceedings of the Web Conference 2021, pp. 2921–2933

  • Wink AM, Tijms BM, Ten Kate M, Raspor E, de Munck JC, Altena E, Ecay-Torres M, Clerigue M, Estanga A, Garcia-Sebastian M et al (2018) Functional brain network centrality is related to APOE genotype in cognitively normal elderly. Brain Behav 8(9):01080

    Google Scholar 

  • Wu F, Souza A, Zhang T, Fifty C, Yu T, Weinberger K (2019) Simplifying graph convolutional networks. Proceedings of the 36th international conference on machine learning, vol 97, pp 6861–6871. PMLR

  • Wu Y, Song Y, Huang H, Ye F, Xie X, Jin H (2021a) Enhancing graph neural networks via auxiliary training for semi-supervised node classification. Knowl-Based Syst 220:106884

    Google Scholar 

  • Wu Z, Pan S, Chen F, Long G, Zhang C, Yu PS (2021b) A comprehensive survey on graph neural networks. IEEE Trans Neural Netw Learn Syst 32(1):4–24. https://doi.org/10.1109/TNNLS.2020.2978386

    Article  MathSciNet  Google Scholar 

  • Wu X, Cheng Q (2021) Deepened graph auto-encoders help stabilize and enhance link prediction

  • Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 833–844. Association for Computational Linguistics

  • Wu J, Wang X, Feng F, He X, Chen L, Lian J, Xie X (2021c) Self-supervised graph learning for recommendation. In: Proceedings of the 44th international ACM SIGIR conference on research and development in information retrieval, pp. 726–735

  • Xu X, Pang G, Wu D, Shang M (2022) Joint hyperbolic and Euclidean geometry contrastive graph neural networks. Inf Sci

  • Xu K, Hu W, Leskovec J, Jegelka S (2018) How powerful are graph neural networks?

  • Xu B, Shen H, Cao Q, Qiu Y, Cheng X (2019b) Graph wavelet neural network

  • Xu Q-H, Li Q-Y, Yu K, Ge Q-M, Shi W-Q, Li B, Liang R-B, Lin Q, Zhang Y-Q, Shao Y (2020b) Altered brain network centrality in patients with diabetic optic neuropathy: a resting-state FMRI study. Endocr Pract 26(12):1399–1405

    Google Scholar 

  • Xu B, Lin Y, Tang X, Li S, Shen L, Sun N, Pan DZ (2019a) Wellgan: Generative-adversarial-network-guided well generation for analog/mixed-signal circuit layout. In: 2019 56th ACM/IEEE Design Automation Conference (DAC), pp. 1–6. IEEE

  • Xu D, Zhu Y, Choy CB, Fei-Fei L (2017) Scene graph generation by iterative message passing. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 5410–5419

  • Xu Y, Li M, Cui L, Huang S, Wei F, Zhou M (2020a) Layoutlm: Pre-training of text and layout for document image understanding. In: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 1192–1200. Association for Computing Machinery, New York, NY, USA

  • Yang L, Li L, Zhang Z, Zhou X, Zhou E, Liu Y (2020b) Dpgn: Distribution propagation graph network for few-shot learning. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 13390–13399

  • Yan S, Xiong Y, Lin D (2018) Spatial temporal graph convolutional networks for skeleton-based action recognition. In: Thirty-second AAAI conference on artificial intelligence, pp. 744–7452. AAAI Press New Orleans, USA

  • Yang Z, Cohen W, Salakhudinov R (2016) Revisiting semi-supervised learning with graph embeddings. In: Proceedings of the 33rd international conference on machine learning, vol 48, pp 40–48. PMLR

  • Yang L, Gu J, Wang C, Cao X, Zhai L, Jin D, Guo Y (2020a) Toward unsupervised graph neural network: interactive clustering and embedding via optimal transport. In: 2020 IEEE international conference on data mining (ICDM), pp. 1358–1363. IEEE

  • Yang K, Swanson K, Jin W, Coley C, Eiden P, Gao H, Guzman-Perez A, Hopper T, Kelley B, Mathea M et al (2019) Analyzing learned molecular representations for property prediction. J Chem Inf Model 59(8):3370–3388

    Google Scholar 

  • Yang J, Leskovec J (2015) Defining and evaluating network communities based on ground-truth. Knowl Inf Syst 42(1):181–213

    Google Scholar 

  • Yang X, Yumer E, Asente P, Kraley M, Kifer D, Lee Giles C (2017) Learning to extract semantic structure from documents using multimodal fully convolutional neural networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 5315–5324

  • Yang J, Lu J, Lee S, Batra D, Parikh D (2018) Graph R-CNN for scene graph generation. In: Proceedings of the European conference on computer vision (ECCV), pp. 670–685. Springer, Cham

  • Yao L, Mao C, Luo Y (2019) Graph convolutional networks for text classification. In: Proceedings of the AAAI conference on artificial intelligence, vol. 33, pp. 7370–7377

  • Yao H, Wu F, Ke J, Tang X, Jia Y, Lu S, Gong P, Ye J, Li Z (2018) Deep multi-view spatial-temporal network for taxi demand prediction. In: Proceedings of the AAAI conference on artificial intelligence, vol. 32, pp. 2588–2595

  • Ying R, He R, Chen K, Eksombatchai P, Hamilton WL, Leskovec J (2018) Graph convolutional neural networks for web-scale recommender systems. In: Proceedings of the 24th ACM SIGKDD international conference on knowledge discovery & data mining, pp. 974–983. Association for Computing Machinery, New York, NY, USA

  • Yi L, Su H, Guo X, Guibas LJ (2017) SyncSpecCNN: Synchronized spectral CNN for 3D shape segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 2282–2290. IEEE Computer Society, Los Alamitos, CA, USA. https://doi.org/10.1109/CVPR.2017.697

  • You Y, Chen T, Sui Y, Chen T, Wang Z, Shen Y (2020a) Graph contrastive learning with augmentations. Adv Neural Inf Process Syst 33:5812–5823

    Google Scholar 

  • You Y, Chen T, Wang Z, Shen Y (2020b) When does self-supervision help graph convolutional networks? In: Proceedings of the 37th international conference on machine learning, vol 119, pp 10871–10880. PMLR

  • You J, Liu B, Ying R, Pande V, Leskovec J (2018) Graph convolutional policy network for goal-directed molecular graph generation

  • You J, Ying R, Leskovec J (2019) Position-aware graph neural networks. In: International conference on machine learning, pp. 7134–7143. PMLR

  • You J, Ying Z, Leskovec J (2020c) Design space for graph neural networks. Adv Neural Inform Process Syst 33

  • Yu Z, Tao L, Qian Z, Wu J, Liu H, Yu Y, Song J, Wang S, Sun J (2016) Altered brain anatomical networks and disturbed connection density in brain tumor patients revealed by diffusion tensor tractography. Int J Comput Assist Radiol Surg 11(11):2007–2019

    Google Scholar 

  • Yu T, He S, Song Y-Z, Xiang T (2022) Hybrid graph neural networks for few-shot learning. In: Proceedings of the AAAI conference on artificial intelligence, vol. 36, pp. 3179–3187

  • Yu E-Y, Wang Y-P, Fu Y, Chen D-B, Xie M (2020) Identifying critical nodes in complex networks via graph convolutional networks. Knowl-Based Syst 198:105893

    Google Scholar 

  • Yu B, Yin H, Zhu Z (2017) Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting

  • Yu J, Lin Z, Yang J, Shen X, Lu X, Huang TS (2018) Generative image inpainting with contextual attention. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 5505–5514. IEEE, Salt Lake City, UT, USA

  • Zachary WW (1977) An information flow model for conflict and fission in small groups. J Anthropol Res 452–473

  • Zeng J, Xie P (2020) Contrastive self-supervised learning for graph classification

  • Zhang M, Chen Y (2018) Link prediction based on graph neural networks. Adv Neural Inf Process Syst 31:5165–5175

    MathSciNet  Google Scholar 

  • Zhang M, Fujinuma Y, Boyd-Graber J (2020b) Exploiting cross-lingual subword similarities in low-resource document classification. Proc AAAI Conf Artif Intell 34:9547–9554

    Google Scholar 

  • Zhang H, Lin S, Liu W, Zhou P, Tang J, Liang X, Xing EP (2020c) Iterative graph self-distillation

  • Zhang M, Wu S, Yu X, Liu Q, Wang L (2022) Dynamic graph neural networks for sequential recommendation. IEEE Trans Knowl Data Eng

  • Zhang G, He H, Katabi D (2019a) Circuit-GNN: graph neural networks for distributed circuit design. In: International conference on machine learning, pp. 7364–7373. PMLR

  • Zhang Z, Cui P, Zhu W (2020a) Deep learning on graphs: a survey. IEEE Trans Knowl Data Eng

  • Zhang J, Shi X, Xie J, Ma H, King I, Yeung D-Y (2018b) Gaan: Gated attention networks for learning on large and spatiotemporal graphs

  • Zhang S, Zhou Z, Huang Z, Wei Z (2018c) Few-shot classification on graphs with structural regularized gcns

  • Zhang X, Liu H, Li Q, Wu X-M (2019b) Attributed graph clustering via adaptive graph convolution

  • Zhang L, Wang X, Li H, Zhu G, Shen P, Li P, Lu X, Shah SAA, Bennamoun M (2020d) Structure-feature based graph self-adaptive pooling. In: Proceedings of the web conference 2020, pp. 3098–3104

  • Zhang B, Yu Z, Zhang W (2020f) Community-centric graph convolutional network for unsupervised community detection. In: IJCAI, pp. 551–556

  • Zhang J, Zhang H, Xia C, Sun L (2020g) Graph-bert: only attention is needed for learning graph representations

  • Zhang R, Xu L, Yu Z, Shi Y, Mu C, Xu M (2021) Deep-irtarget: an automatic target detector in infrared imagery using dual-domain feature extraction and allocation. IEEE Trans Multimedia 24:1735–1749

    Google Scholar 

  • Zhang Y, Lu H, Niu W, Caverlee J (2018a) Quality-aware neural complementary item recommendation. In: Proceedings of the 12th ACM conference on recommender systems, pp. 77–85

  • Zhang Y, Yu X, Cui Z, Wu S, Wen Z, Wang L (2020e) Every document owns its structure: inductive text classification via graph neural networks. In: Proceedings of the 58th annual meeting of the association for computational linguistics, pp. 334–339. Association for Computational Linguistics

  • Zhao Q, Zhang Y, Zhang Y, Friedman D (2017) Multi-product utility maximization for economic recommendation. In: Proceedings of the Tenth ACM International Conference on Web Search and Data Mining, pp. 435–443

  • Zhou Q, Womer FY, Kong L, Wu F, Jiang X, Zhou Y, Wang D, Bai C, Chang M, Fan G (2017) Trait-related cortical-subcortical dissociation in bipolar disorder: analysis of network degree centrality. J Clin Psychiatry 78(5):0–0

    Google Scholar 

  • Zhou J, Cui G, Hu S, Zhang Z, Yang C, Liu Z, Wang L, Li C, Sun M (2020) Graph neural networks: a review of methods and applications. AI Open 1:57–81

    Google Scholar 

  • Zhu XJ (2005) Semi-supervised learning literature survey. University of Wisconsin–Madison Department of Computer Sciences

  • Zhuang L, Zhou Z, Gao S, Yin J, Lin Z, Ma Y (2017) Label information guided graph construction for semi-supervised learning. IEEE Trans Image Process 26(9):4182–4192

    MathSciNet  MATH  Google Scholar 

  • Zhu K, Liu M, Lin Y, Xu B, Li S, Tang X, Sun N, Pan DZ (2019) Geniusroute: a new analog routing paradigm using generative neural network guidance. In: 2019 IEEE/ACM international conference on computer-aided design (ICCAD), pp. 1–8. IEEE

  • Zhu Y, Xu Y, Yu F, Liu Q, Wu S, Wang L (2020a) Deep graph contrastive representation learning

  • Zhu Y, Xu Y, Yu F, Liu Q, Wu S, Wang L (2021) Graph contrastive learning with adaptive augmentation. In: Proceedings of the web conference 2021, pp. 2069–2080

  • Zhu Y, Xu Y, Yu F, Wu S, Wang L (2020b) Cagnn: Cluster-aware graph neural networks for unsupervised graph representation learning

  • Zitnik M, Agrawal M, Leskovec J (2018) Modeling polypharmacy side effects with graph convolutional networks. Bioinformatics 34(13):457–466

    Google Scholar 

  • Zuo X-N, Ehmke R, Mennes M, Imperati D, Castellanos FX, Sporns O, Milham MP (2012) Network centrality in the human functional connectome. Cereb Cortex 22(8):1862–1875

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science & Engineering, National Institute of Technology Silchar, Silchar, Assam, 788010, India

    Lilapati Waikhom & Ripon Patgiri

Authors
  1. Lilapati Waikhom
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ripon Patgiri
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ripon Patgiri.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Waikhom, L., Patgiri, R. A survey of graph neural networks in various learning paradigms: methods, applications, and challenges. Artif Intell Rev 56, 6295–6364 (2023). https://doi.org/10.1007/s10462-022-10321-2

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10462-022-10321-2

Keywords

Search

Navigation