Abstract
Graph neural networks, the mainstream paradigm of graph data mining, optimize the traditional feature-based node classification models with supplementing spatial topology. However, those isolated nodes not well connected to the whole graph are difficult to capture effective information through structural aggregation and sometimes even bring the negative local over-smoothing phenomenon, which is called structure fairness problem. To the best of our knowledge, current methods mainly focus on amending the network structure to improve the expressiveness with absence of the influence of the isolated parts. To facilitate this line of research, we innovatively propose a Multi-task Graph Neural Network for Optimizing the Structure Fairness (GNN-OSF). In GNN-OSF, nodes set is divided into diverse positions with a comprehensive investigation of the correlation between node position and accuracy in global topology. Besides, the link matrix is constructed to express the consistency of node labels, which expects isolated nodes to learn the same embedding and label when nodes share similar features. Afterward, the GNN-OSF network structure is explored by introducing the auxiliary link prediction task, where the task-shared and task-specific layer of diverse tasks are integrated with the auto-encoder architecture. Our comprehensive experiments demonstrate that GNN-OSF achieves superior node classification performance on both public benchmark and real-world industrial datasets, which effectively alleviates the structure unfairness of the isolated parts and leverages off-the -shelf models with the interaction of auxiliary tasks.
Supported by the National Natural Science Foundation of China (Grant No. 62002216), the Shanghai Sailing Program (Grant No. 20YF1414400), the Shanghai Polytechnic University Research Projects (Grant No. EGD23DS05).
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References
Besta, M., Iff, P., Scheidl, F., et al.: Neural graph databases. In: Learning on Graphs Conference, vol. 198, p. 31 (2022)
Ren, H., Galkin, M., Cochez, M., et al.: Neural graph reasoning: complex logical query answering meets graph databases, CoRR abs/2303.14617 (2023)
Zhang, Z., Cui, P., Zhu, W.: Deep learning on graphs: a survey. IEEE Trans. Knowl. Data Eng. 34(1), 249–270 (2022)
Wu, Z., Pan, S., Chen, F., et al.: A comprehensive survey on graph neural networks. IEEE Trans. Neural Netw. Learn. Syst. 32(1), 4–24 (2021)
Wang, J., Guo, Y., Wang, Z., et al.: Graph neural network with feature enhancement of isolated marginal groups. Appl. Intell. 52(14), 16962–16974 (2022)
Kang, J., Zhu, Y., Xia, Y., et al.: RawlsGCN: towards Rawlsian difference principle on graph convolutional network. In: ACM Web Conference, pp. 1214–1225 (2022)
Henaff, M., Bruna, J., LeCun, Y.: Deep convolutional networks on graph-structured data. arXiv preprint arXiv:1506.05163 (2015)
Defferrard, M., Bresson, X., Vandergheynst, P.: Convolutional neural networks on graphs with fast localized spectral filtering. In: Advances in Neural Information Processing Systems, vol. 29, pp. 3837–3845 (2016)
Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. In: International Conference on Learning Representations, ICLR (2017)
Xu, B., Shen, H., Cao, Q., et al.: Graph convolutional networks using heat kernel for semi-supervised learning. In: International Joint Conference on Artificial Intelligence, IJCAI, pp. 1928–1934 (2019)
Klicpera, J., Bojchevski, A., Günnemann, S.: Predict then propagate: graph neural networks meet personalized pagerank. In: International Conference on Learning Representations, ICLR (2019)
Chen, M., Wei, Z., Huang, Z., et al.: Simple and deep graph convolutional networks. In: International Conference on Machine Learning, vol. 119, pp. 1725–1735 (2020)
Xu, B., Shen, H., Cao, Q., et al.: Graph wavelet neural network. In: International Conference on Learning Representations, ICLR (2019)
Wu, F., Souza, A., Zhang, T., et al.: Simplifying graph convolutional networks. In: International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 97, pp. 6861–6871 (2019)
Hamilton, W.L., Ying, Z., Leskovec, J.: Inductive representation learning on large graphs. In: Advances in Neural Information Processing Systems, vol. 30, pp. 1024–1034 (2017)
Velickovic, P., Cucurull, G., Casanova, A., et al.: Graph attention networks. In: International Conference on Learning Representations, ICLR (2018)
Shi, Y., Huang, Z., Feng, S., et al.: Masked label prediction: unified message passing model for semi-supervised classification. In: International Joint Conference on Artificial Intelligence, pp. 1548–1554. ijcai.org (2021)
Corso, G., Cavalleri, L., Beaini, D., et al.: Principal neighbourhood aggregation for graph nets. In: Advances in Neural Information Processing Systems (2020)
Zhang, S., Xie, L.: Improving attention mechanism in graph neural networks via cardinality preservation. In: International Joint Conference on Artificial Intelligence, IJCAI, pp. 1395–1402 (2020)
Rong, Y., Huang, W., Xu, T., et al.: DropEdge: towards deep graph convolutional networks on node classification. In: International Conference on Learning Representations, ICLR (2020)
Xu, K., Li, C., Tian, Y., et al.: Representation learning on graphs with jumping knowledge networks. In: International Conference on Machine Learning, ICML, vol. 80, pp. 5449–5458 (2018)
Zhao, L., Akoglu, L.: PairNorm: tackling oversmoothing in GNNs. In: International Conference on Learning Representations. OpenReview.net (2020)
Yang, H., Ma, K., Cheng, J.: Rethinking graph regularization for graph neural networks. In: AAAI Conference on Artificial Intelligence, pp. 4573–4581 (2021)
Chen, D., Lin, Y., Li, W., et al.: Measuring and relieving the over-smoothing problem for graph neural networks from the topological view. In: AAAI Conference on Artificial Intelligence, pp. 3438–3445. AAAI Press (2020)
Grover, A., Leskovec, J.: Node2vec: scalable feature learning for networks. In: ACM SIGKDD International Conference, pp. 855–864 (2016)
Yang, Z., Cohen, W.W., Salakhutdinov, R.: Revisiting semi-supervised learning with graph embeddings. In: International Conference on Machine Learning, ICML, vol. 48, pp. 40–48 (2016)
Liao, R., Brockschmidt, M., Tarlow, D., et al.: Graph partition neural networks for semi-supervised classification. In: International Conference on Learning Representations, ICLR (2018)
Yu, S., Yang, X., Zhang, W.: PKGCN: prior knowledge enhanced graph convolutional network for graph-based semi-supervised learning. Int. J. Mach. Learn. Cybern. 10, 3115–3127 (2019)
Lei, F., Liu, X., Dai, Q., et al.: Hybrid low-order and higher-order graph convolutional networks. Comput. Intell. Neurosci. 2020, 3283890:1–3283890:9 (2020)
Chen, S., Tian, X., Ding, C.H.Q., et al.: Graph convolutional network based on manifold similarity learning. Cogn. Comput. 12(6), 1144–1153 (2020)
Manessi, F., Rozza, A.: Graph-based neural network models with multiple self-supervised auxiliary tasks. Pattern Recogn. Lett. 148, 15–21 (2021)
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Wang, J., Li, M., Chen, F., Meng, X., Yu, C. (2023). Multi-task Graph Neural Network for Optimizing the Structure Fairness. In: Strauss, C., Amagasa, T., Kotsis, G., Tjoa, A.M., Khalil, I. (eds) Database and Expert Systems Applications. DEXA 2023. Lecture Notes in Computer Science, vol 14147. Springer, Cham. https://doi.org/10.1007/978-3-031-39821-6_29
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