Abstract
Link prediction is one of the most important tasks in graph machine learning, which aims at predicting whether two nodes in a network have an edge. Real-world graphs typically contain abundant node and edge attributes, thus how to perform link prediction by simultaneously learning structure and attribute information from both interactions/paths between two associated nodes and local neighborhood among node’s ego subgraph is intractable.
To address this issue, we develop a novel Path-aware Graph Neural Network (PaGNN) method for link prediction, which incorporates interaction and neighborhood information into graph neural networks via broadcasting and aggregating operations. And a cache strategy is developed to accelerate the inference process. Extensive experiments show a superior performance of our proposal over state-of-the-art methods on real-world link prediction tasks.
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
Similar content being viewed by others
References
AbuOda, G., Morales, G.D.F., Aboulnaga, A.: Link prediction via higher-order motif features. In: ECML PKDD, pp. 412–429 (2019)
Brin, S., Page, L.: Reprint of: the anatomy of a large-scale hypertextual web search engine. Comput. Netw. 56(18), 3825–3833 (2012)
Cui, P., Wang, X., Pei, J., Zhu, W.: A survey on network embedding. IEEE Trans. Knowl. Data Eng. 31(5), 833–852 (2018)
Feng, Y., Hu, B., Lv, F., Liu, Q., Zhang, Z., Ou, W.: ATBRG: adaptive target-behavior relational graph network for effective recommendation (2020)
Grover, A., Leskovec, J.: node2vec: scalable feature learning for networks. In: SIGKDD, pp. 855–864 (2016)
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)
Hu, B., Hu, Z., Zhang, Z., Zhou, J., Shi, C.: KGNN: distributed framework for graph neural knowledge representation. In: ICML Workshop (2020)
Hu, B., Shi, C., Zhao, W.X., Yu, P.S.: Leveraging meta-path based context for top-n recommendation with a neural co-attention model. In: SIGKDD, pp. 1531–1540 (2018)
Katz, L.: A new status index derived from sociometric analysis. Psychometrika 18(1), 39–43 (1953)
Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009)
Kovács, I.A., et al.: Network-based prediction of protein interactions. Nat. Commun. 10(1), 1–8 (2019)
Kumar, A., Singh, S.S., Singh, K., Biswas, B.: Link prediction techniques, applications, and performance: A survey. Phys. A 553, 124289 (2020)
Liben-Nowell, D., Kleinberg, J.: The link-prediction problem for social networks. J. Am. Soc. Inform. Sci. Technol. 58(7), 1019–1031 (2007)
Perozzi, B., Al-Rfou, R., Skiena, S.: DeepWalk: online learning of social representations. In: SIGKDD, pp. 701–710 (2014)
Sankar, A., Zhang, X., Chang, K.C.C.: Meta-GNN: metagraph neural network for semi-supervised learning in attributed heterogeneous information networks. In: ASONAM, pp. 137–144 (2019)
Sha, X., Sun, Z., Zhang, J.: Attentive knowledge graph embedding for personalized recommendation. arXiv preprint arXiv:1910.08288 (2019)
Shi, B., Weninger, T.: ProjE: embedding projection for knowledge graph completion. In: AAAI, vol. 31 (2017)
Shi, C., Hu, B., Zhao, W.X., Philip, S.Y.: Heterogeneous information network embedding for recommendation. IEEE Trans. Knowl. Data Eng. 31(2), 357–370 (2018)
Shibata, N., Kajikawa, Y., Sakata, I.: Link prediction in citation networks. J. Am. Soc. Inform. Sci. Technol. 63(1), 78–85 (2012)
Teru, K., Denis, E., Hamilton, W.: Inductive relation prediction by subgraph reasoning. In: ICML, pp. 9448–9457 (2020)
Veličković, P., Cucurull, G., Casanova, A., Romero, A., Lio, P., Bengio, Y.: Graph attention networks. arXiv preprint arXiv:1710.10903 (2017)
Wang, D., Cui, P., Zhu, W.: Structural deep network embedding. In: SIGKDD, pp. 1225–1234 (2016)
Wang, H., Lian, D., Zhang, Y., Qin, L., Lin, X.: GOGNN: graph of graphs neural network for predicting structured entity interactions, pp. 1317–1323 (2020)
Wang, Z., Liao, J., Cao, Q., Qi, H., Wang, Z.: FriendBook: a semantic-based friend recommendation system for social networks. IEEE Trans. Mob. Comput. 14(3), 538–551 (2014)
Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE Trans. Neural Netw. Learn. Syst. 32, 4–24 (2020)
Wu, Z., Pan, S., Long, G., Jiang, J., Zhang, C.: Graph WaveNet for deep spatial-temporal graph modeling. arXiv preprint arXiv:1906.00121 (2019)
Xu, C., Cui, Z., Hong, X., Zhang, T., Yang, J., Liu, W.: Graph inference learning for semi-supervised classification. arXiv preprint arXiv:2001.06137 (2020)
Xu, N., Wang, P., Chen, L., Tao, J., Zhao, J.: MR-GNN: multi-resolution and dual graph neural network for predicting structured entity interactions, pp. 3968–3974 (2019)
Yang, S., et al.: Financial risk analysis for SMEs with graph-based supply chain mining. In: IJCAI, pp. 4661–4667 (2020)
Yang, S., Zou, L., Wang, Z., Yan, J., Wen, J.R.: Efficiently answering technical questions–a knowledge graph approach. In: AAAI, vol. 31 (2017)
Yang, Z., Cohen, W., Salakhudinov, R.: Revisiting semi-supervised learning with graph embeddings. In: ICML, pp. 40–48 (2016)
Ying, R., He, R., Chen, K., Eksombatchai, P., Hamilton, W.L., Leskovec, J.: Graph convolutional neural networks for web-scale recommender systems. In: SIGKDD, pp. 974–983 (2018)
Zhang, D., et al.: AGL: a scalable system for industrial-purpose graph machine learning. arXiv preprint arXiv:2003.02454 (2020)
Zhang, M., Chen, Y.: Link prediction based on graph neural networks. In: NeurIPS, pp. 5165–5175 (2018)
Zhang, W., Fang, Y., Liu, Z., Wu, M., Zhang, X.: mg2vec: learning relationship-preserving heterogeneous graph representations via metagraph embedding. IEEE Trans. Knowl. Data Eng. (2020)
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
Yang, S. et al. (2021). Inductive Link Prediction with Interactive Structure Learning on Attributed Graph. In: Oliver, N., Pérez-Cruz, F., Kramer, S., Read, J., Lozano, J.A. (eds) Machine Learning and Knowledge Discovery in Databases. Research Track. ECML PKDD 2021. Lecture Notes in Computer Science(), vol 12976. Springer, Cham. https://doi.org/10.1007/978-3-030-86520-7_24
Download citation
DOI: https://doi.org/10.1007/978-3-030-86520-7_24
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-86519-1
Online ISBN: 978-3-030-86520-7
eBook Packages: Computer ScienceComputer Science (R0)
