Abstract
Classifying Multi-Label nodes in networks is a well-known and widely used task in different domains. Current classification models rely on mining the network structure either by random walks or through approximating the laplacian of the network graph which gives insight about the nodes’ neighborhood. In implicit feedback relations, these models assume all relation edges to be equally strong and important. However, in real life, this is not necessarily the case as some edges might have different semantic weights such as friendship relation. To tackle this limitation we propose in this paper a weighted two-stage multi-relational matrix factorization model with Bayesian personalized ranking loss for network classification that utilizes different weighting functions for approximating the implicit feedback relation weights. Experiments on four real-world datasets show that the proposed model significantly outperforms the state-of-art models. Results also show that selecting the right weighting functions for approximating relation weights significantly improves classification accuracy.
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References
Adamic, L.A., Adar, E.: Friends and neighbors on the web. Soc. Netw. 25(3), 211–230 (2003)
Ahmed, C., ElKorany, A., Bahgat, R.: A supervised learning approach to link prediction in twitter. Soc. Netw. Anal. Min. 6(1), 24 (2016)
Breitkreutz, B.J., et al.: The biogrid interaction database: 2008 update. Nucleic Acids Res. 36(suppl\_1), D637–D640 (2007)
Cai, H., Zheng, V.W., Chang, K.: A comprehensive survey of graph embedding: problems, techniques and applications. IEEE Trans. Knowl. Data Eng. 30, 1616–1637 (2018)
Grover, A., Leskovec, J.: node2vec: Scalable feature learning for networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 855–864. ACM (2016)
Jaccard, P.: Lois de distribution florale dans la zone alpine. Bull. Soc. Vaudoise Sci. Nat. 38, 69–130 (1902)
Jamali, M., Ester, M.: A matrix factorization technique with trust propagation for recommendation in social networks. In: Proceedings of the Fourth ACM Conference on Recommender Systems, pp. 135–142. ACM (2010)
Krohn-Grimberghe, A., Drumond, L., Freudenthaler, C., Schmidt-Thieme, L.: Multi-relational matrix factorization using Bayesian personalized ranking for social network data. In: Proceedings of the Fifth ACM International Conference on Web Search and Data Mining, pp. 173–182. ACM (2012)
Lerche, L., Jannach, D.: Using graded implicit feedback for Bayesian personalized ranking. In: Proceedings of the 8th ACM Conference on Recommender Systems, pp. 353–356. ACM (2014)
Liben-Nowell, D., Kleinberg, J.: The link-prediction problem for social networks. J. Assoc. Inf. Sci. Technol. 58(7), 1019–1031 (2007)
Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013)
Perozzi, B., Al-Rfou, R., Skiena, S.: Deepwalk: online learning of social representations. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 701–710. ACM (2014)
Rashed, A., Grabocka, J., Schmidt-Thieme, L.: Multi-label network classification via weighted personalized factorizations. In: Proceedings of the 11th International Conference on Agents and Artificial Intelligence, pp. 357–366. SCITEPRESS-Science and Technology Publications, Lda (2019)
Rendle, S., Freudenthaler, C.: Improving pairwise learning for item recommendation from implicit feedback. In: Proceedings of the 7th ACM International Conference on Web Search and Data Mining, pp. 273–282. ACM (2014)
Rendle, S., Freudenthaler, C., Gantner, Z., Schmidt-Thieme, L.: BPR: Bayesian personalized ranking from implicit feedback. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 452–461. AUAI Press (2009)
Sen, P., Namata, G., Bilgic, M., Getoor, L., Galligher, B., Eliassi-Rad, T.: Collective classification in network data. AI Mag. 29(3), 93 (2008)
Singh, A.P., Gordon, G.J.: Relational learning via collective matrix factorization. In: Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 650–658. ACM (2008)
Symeonidis, P., Tiakas, E., Manolopoulos, Y.: Transitive node similarity for link prediction in social networks with positive and negative links. In: Proceedings of the Fourth ACM Conference on Recommender Systems, pp. 183–190. ACM (2010)
Tang, J., Qu, M., Wang, M., Zhang, M., Yan, J., Mei, Q.: 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 (2015)
Tang, L., Liu, H.: Relational learning via latent social dimensions. In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 817–826. ACM (2009)
Tang, L., Liu, H.: Scalable learning of collective behavior based on sparse social dimensions. In: Proceedings of the 18th ACM Conference on Information and Knowledge Management, pp. 1107–1116. ACM (2009)
Thomas, N.K., Welling, M.: Semi-supervised classification with graph convolutional networks. arxiv preprint. arXiv preprint arXiv:1609.02907 103 (2016)
Tu, C., Zhang, W., Liu, Z., Sun, M.: Max-margin DeepWalk: discriminative learning of network representation. In: IJCAI (2016)
Yang, C., Liu, Z., Zhao, D., Sun, M., Chang, E.Y.: Network representation learning with rich text information. In: IJCAI, pp. 2111–2117 (2015)
Zafarani, R., Liu, H.: Social computing data repository at ASU (2009)
Acknowledgements
The authors gratefully acknowledge the funding of their work through the ĂśberDax project (https://www.ismll.uni-hildesheim.de/projekte/UberDax.html) which is sponsored by the Bundesministerium fĂĽr Bildung und Forschung (BMBF) under the grant agreement no. 01IS17053.
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Rashed, A., Grabocka, J., Schmidt-Thieme, L. (2019). Weighted Personalized Factorizations for Network Classification with Approximated Relation Weights. In: van den Herik, J., Rocha, A., Steels, L. (eds) Agents and Artificial Intelligence. ICAART 2019. Lecture Notes in Computer Science(), vol 11978. Springer, Cham. https://doi.org/10.1007/978-3-030-37494-5_6
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