Abstract
Heterogeneous graphs (HGs), possessing various node and edge types, are essential in capturing complex relationships in networks. Link prediction on heterogeneous graphs has wide applications in real-world. Although existing methods for learning representations of HGs have made substantial progress in link prediction tasks, they primarily focus on the heterogeneous attributes of nodes when capturing the heterogeneity of heterogeneous graphs, therefore, it performs poorly in maintaining pairwise relationships in HG. To address this limitation, we propose a simple yet effective model for link prediction on HGs via Mutual Information Maximization between Node Pairs (MIMNP). We use an Multi-Layer Perceptron as a node encoder to learn node embeddings and maximizes the mutual information between node pairs. Our model effectively preserves the pairwise relationships between nodes, resulting in enhanced link prediction performance. Extensive experiments conducted on three real-world datasets consistently demonstrate that MIMNP outperforms state-of-the-art baselines in link prediction.
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
Tang, J., et al.: Arnetminer: extraction and mining of academic social networks. In: Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2008)
El-Kishky, A., et al.: Twhin: embedding the twitter heterogeneous information network for personalized recommendation. In: Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (2022)
Shao, K., Zhang, Y., Wen, Y., et al.: DTI-HETA: prediction of drug-target interactions based on GCN and GAT on heterogeneous graph. Brief. Bioinform. 23(3), bbac109 (2022)
Jin, D., et al.: A survey of community detection approaches: from statistical modeling to deep learning. IEEE Trans. Knowl. Data Eng. 35(2), 1149–1170 (2021)
Tao, X., et al.: Mining health knowledge graph for health risk prediction. World Wide Web 23, 2341–2362 (2020)
Wang, X., et al.: Heterogeneous graph attention network. In: The World Wide Web Conference (2019)
Fu, X., et al.: MAGNN: metapath aggregated graph neural network for heterogeneous graph embedding. In: Proceedings of the Web Conference 2020 (2020)
Zhang, C., et al.: Heterogeneous graph neural network. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (2019)
Fu, T.-Y., Lee, W.-C., Lei, Z.: Hin2vec: explore meta-paths in heterogeneous information networks for representation learning. In: Proceedings of the 2017 ACM on Conference on Information and Knowledge Management (2017)
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 (2017)
Schlichtkrull, M., Kipf, T.N., Bloem, P., van den Berg, R., Titov, I., Welling, M.: Modeling relational data with graph convolutional networks. In: Gangemi, A., et al. (eds.) ESWC 2018. LNCS, vol. 10843, pp. 593–607. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-93417-4_38
Hu, Z., et al.: Heterogeneous graph transformer. In: Proceedings of the Web Conference 2020 (2020)
Liu, Z., et al.: HeteEdgeWalk: a heterogeneous edge memory random walk for heterogeneous information network embedding. Entropy 25(7), 998 (2023)
Barabási, A.-L., Albert, R.: Emergence of scaling in random networks. Science 286(5439), 509–512 (1999)
Zhou, T., Lü, L., Zhang, Y.-C.: Predicting missing links via local information. Eur. Phys. J. B 71, 623–630 (2009)
Adamic, L.A., Adar, E.: Friends and neighbors on the web. Soc. Netw. 25(3), 211–230 (2003)
Zhang, M., Chen, Y.: Weisfeiler-Lehman neural machine for link prediction. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2017)
Zhang, M., Chen, Y.: Link prediction based on graph neural networks. In: Advances in Neural Information Processing Systems, vol. 31 (2018)
Dong, W., et al.: Node representation learning in graph via node-to-neighbourhood mutual information maximization. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (2022)
Ren, Y., Liu, B.: Heterogeneous deep graph infomax. In: Workshop of Deep Learning on Graphs: Methodologies and Applications co-located with the Thirty-Fourth AAAI Conference on Artificial Intelligence (2020)
Park, C., et al.: Task-guided pair embedding in heterogeneous network. In: Proceedings of the 28th ACM International Conference on Information and Knowledge Management (2019)
Zhang, C., Swami, A., Chawla, N.V.: SHNE: representation learning for semantic-associated heterogeneous networks. In: Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining (2019)
Fan, H., et al.: Heterogeneous hypergraph variational autoencoder for link prediction. IEEE Trans. Pattern Anal. Mach. Intell. 44(8), 4125–4138 (2021)
Schulman, J., et al.: High-Dimensional Continuous Control Using Generalized Advantage Estimation. CoRR abs/1506.02438 (2015)
Kipf, T.N., Welling, M.: Variational graph auto-encoders. In: NIPS Workshop on Bayesian Deep Learning (2016)
Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. In: International Conference on Learning Representations (2016)
Shi, C., et al.: Heterogeneous information network embedding for recommendation. IEEE Trans. Knowl. Data Eng. 31(2), 357–370 (2018)
Belghazi, M.I., et al.: Mutual information neural estimation. In: International Conference on Machine Learning. PMLR (2018)
van den Oord, A., Li, Y., Vinyals, O.: Representation learning with contrastive predictive coding. arXiv preprint arXiv:1807.03748 (2018)
Hamilton, W., Ying, Z., Leskovec, J.: Inductive representation learning on large graphs. In: Advances in Neural Information Processing Systems, vol. 30 (2017)
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 (2016)
Veličković, P., et al.: Deep graph infomax. In: International Conference on Learning Representations (2018)
Jiao, P., et al.: Role discovery-guided network embedding based on autoencoder and attention mechanism. IEEE Trans. Cybern. 53(1), 365–378 (2021)
Gao, M., et al.: Inductive link prediction via interactive learning across relations in multiplex networks. IEEE Trans. Comput. Soc. Syst. (2022)
Jiao, P., et al.: HB-DSBM: modeling the dynamic complex networks from community level to node level. IEEE Trans. Neural Netw. Learn. Syst. (2022)
Jiao, P., et al.: A survey on role-oriented network embedding. IEEE Trans. Big Data 84, 933–952 (2021)
Acknowledgement
This work was supported in part by the Zhejiang Provincial Natural Science Foundation of China under Grant LDT23F01015F01 and Grant LDT23F01012F01, in part by the Fundamental Research Funds for the Provincial Universities of Zhejiang Grant GK229909299001-008 and in part by the National Natural Science Foundation of China under Grant 62003120.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Lu, Y., Liu, Z., Gao, M., Jiao, P. (2024). Heterogeneous Link Prediction via Mutual Information Maximization Between Node Pairs. In: Fang, L., Pei, J., Zhai, G., Wang, R. (eds) Artificial Intelligence. CICAI 2023. Lecture Notes in Computer Science(), vol 14473. Springer, Singapore. https://doi.org/10.1007/978-981-99-8850-1_37
Download citation
DOI: https://doi.org/10.1007/978-981-99-8850-1_37
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-99-8849-5
Online ISBN: 978-981-99-8850-1
eBook Packages: Computer ScienceComputer Science (R0)