Abstract
Learning from multiplex heterogeneous networks is a crucial task in many real-world applications such as recommender systems. Usually, a multiplex heterogeneous network has multiple types of nodes and edges (or relations). Multiplex heterogeneous network embedding aims to learn from abundant structural and semantic information of a graph and embed nodes into low-dimensional representations. Existing works usually split the graph into several relation-specific subgraphs to distinguish different relations. However, these works either omit the important information of metapath in aggregation or fail to fully utilize the multiplex property in the network. To tackle the above challenges, we propose a novel model DiffGNN, which is designed to capture different behaviors in an elegant and efficient manner. DiffGNN adopts two powerful modules, i.e., the relation-specific attention (RsAtt) and metapath aware aggregation (MetAware), where MetAware aggregates information from different metapaths in each relation-specific subgraph and RsAtt combines and integrates the information with attentive weights. The experiments are conducted on three real-world datasets, and the experimental results show that our DiffGNN achieves significant improvement compared to the state-of-the-art models.
This work was supported by the National Natural Science Foundation of China (No. 61872207) and Kuaishou Inc.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Bian, R., Koh, Y.S., Dobbie, G., Divoli, A.: Network embedding and change modeling in dynamic heterogeneous networks. In: Proceedings of the 42nd SIGIR Conference on Research and Development in Information Retrieval, pp. 861–864 (2019)
Cen, Y., Zou, X., Zhang, J., Yang, H., Zhou, J., Tang, J.: Representation learning for attributed multiplex heterogeneous network. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 1358–1368 (2019)
Chen, C.M., Wang, C.J., Tsai, M.F., Yang, Y.H.: Collaborative similarity embedding for recommender systems. In: WWW Conference, pp. 2637–2643 (2019)
Chen, J., Zhang, H., He, X., Nie, L., Liu, W., Chua, T.S.: Attentive collaborative filtering: Multimedia recommendation with item-and component-level attention. In: Proceedings of the 40th SIGIR Conference on Research and Development in Information Retrieval, pp. 335–344 (2017)
Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006)
Dong, Y., Chawla, N.V., Swami, A.: 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 (2017)
Fu, X., Zhang, J., Meng, Z., King, I.: Magnn: metapath aggregated graph neural network for heterogeneous graph embedding. In: Proceedings of The Web Conference 2020, pp. 2331–2341 (2020)
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 (2016)
Hamilton, W., Ying, Z., Leskovec, J.: Inductive representation learning on large graphs. In: Advances in Neural Information Processing Systems, pp. 1024–1034 (2017)
Hanley, J.A., McNeil, B.J.: The meaning and use of the area under a receiver operating characteristic (roc) curve. Radiology 143(1), 29–36 (1982)
He, R., McAuley, J.: Ups and downs: modeling the visual evolution of fashion trends with one-class collaborative filtering. In: WWW Conference, pp. 507–517 (2016)
He, X., Deng, K., Wang, X., Li, Y., Zhang, Y., Wang, M.: Lightgcn: simplifying and powering graph convolution network for recommendation. arXiv preprint arXiv:2002.02126 (2020)
He, Y., Song, Y., Li, J., Ji, C., Peng, J., Peng, H.: Hetespaceywalk: a heterogeneous spacey random walk for heterogeneous information network embedding. In: Proceedings of the 28th ACM International Conference on Information and Knowledge Management, pp. 639–648 (2019)
Jin, J., et al.: An efficient neighborhood-based interaction model for recommendation on heterogeneous graph. In: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 75–84 (2020)
Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907 (2016)
Lu, Y., Shi, C., Hu, L., Liu, Z.: Relation structure-aware heterogeneous information network embedding. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, pp. 4456–4463 (2019)
Maaten, L.v.d., Hinton, G.: Visualizing data using t-SNE. J. Mach. Learn. Res. 9(11), 2579–2605 (2008)
McAuley, J., Targett, C., Shi, Q., Van Den Hengel, A.: Image-based recommendations on styles and substitutes. In: Proceedings of the 38th SIGIR Conference on Research and Development in Information Retrieval, pp. 43–52 (2015)
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 (2014)
Tang, J., Qu, M., Wang, M., Zhang, M., Yan, J., Mei, Q.: Line: Llarge-scale information network embedding. In: WWW Conference, pp. 1067–1077 (2015)
Tang, L., Liu, H.: Uncovering cross-dimension group structures in multi-dimensional networks. In: SDM Workshop on Analysis of Dynamic Networks, pp. 568–575 (2009)
Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems, pp. 5998–6008 (2017)
Wang, D., Cui, P., Zhu, W.: Structural deep network embedding. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1225–1234 (2016)
Wang, W., Yin, H., Du, X., Hua, W., Li, Y., Nguyen, Q.V.H.: Online user representation learning across heterogeneous social networks. In: Proceedings of the 42nd SIGIR Conference on Research and Development in Information Retrieval, pp. 545–554 (2019)
Wang, X., He, X., Wang, M., Feng, F., Chua, T.S.: Neural graph collaborative filtering. In: Proceedings of the 42nd SIGIR Conference on Research and Development in Information Retrieval, pp. 165–174 (2019)
Wang, X., Jin, H., Zhang, A., He, X., Xu, T., Chua, T.S.: Disentangled graph collaborative filtering. In: Proceedings of the 43rd SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2020, pp. 1001–1010. Association for Computing Machinery, New York (2020)
Weston, J., Yee, H., Weiss, R.J.: Learning to rank recommendations with the k-order statistic loss. In: Proceedings of the 7th ACM Conference on Recommender Systems, pp. 245–248 (2013)
Ying, R., He, R., Chen, K., Eksombatchai, P., Hamilton, W.L., Leskovec, J.: 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 (2018)
Zhang, D., Yin, J., Zhu, X., Zhang, C.: MetaGraph2Vec: complex semantic path augmented heterogeneous network embedding. In: Phung, D., Tseng, V.S., Webb, G.I., Ho, B., Ganji, M., Rashidi, L. (eds.) PAKDD 2018. LNCS (LNAI), vol. 10938, pp. 196–208. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-93037-4_16
Zhang, H., Qiu, L., Yi, L., Song, Y.: Scalable multiplex network embedding. In: IJCAI, pp. 3082–3088 (2018)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Gu, T., Wang, C., Wu, C. (2021). DiffGNN: Capturing Different Behaviors in Multiplex Heterogeneous Networks for Recommendation. In: Fang, L., Chen, Y., Zhai, G., Wang, J., Wang, R., Dong, W. (eds) Artificial Intelligence. CICAI 2021. Lecture Notes in Computer Science(), vol 13069. Springer, Cham. https://doi.org/10.1007/978-3-030-93046-2_2
Download citation
DOI: https://doi.org/10.1007/978-3-030-93046-2_2
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-93045-5
Online ISBN: 978-3-030-93046-2
eBook Packages: Computer ScienceComputer Science (R0)