Abstract
A low-dimensional embedding can be easily applied in the downstream tasks for network mining and analysis. In the meantime, the popular models of random walk-based network embedding are viewed as the form of matrix factorization, whose computational cost is very expensive. Moreover, mapping different types of nodes into one metric space may result in incompatibility. To cope with the two challenges above, a weighted meta-path embedding framework (WMPE) is proposed in this paper. On one hand, a nearly-linear approximate embedding approach is leveraged to reduce the computational cost. On the other hand, the meta-path and its weight are learned to integrate the incompatible semantics in the form of weighted combination. Experiment results show that WMPE is effective and outperforms the state-of-the-art baselines on two real-world datasets.
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References
Lee, G., Kang, S., Whang, J.: Hyperlink classification via structured graph embedding. In: Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 1017–1020 (2019)
Fan, S., et al.: One2Multi graph autoencoder for multi-view graph clustering. In: WWW’20: Proceedings of The Web Conference 2020, pp. 3070–3076 (2020)
Rosso, P., Yang, D., Philippe, C.M.: Beyond triplets: hyper-relational knowledge graph embedding for link prediction. In: WWW’20: Proceedings of The Web Conference 2020, pp 1885–1896 (2020)
Wen, Y., et al.: Network embedding based recommendation method in social networks. In: Companion of the Web Conference 2018, pp. 11–12 (2018)
Ng, A.Y., Jordan, M.I., Weiss, Y.: On spectral clustering: analysis and an algorithm. In: Advances in Neural Information Processing Systems, pp. 849–856 (2002)
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., et al.: LINE: large-scale information network embedding. In: Proceedings of 24th International Conference on World Wide Web, pp. 1067–1077 (2015)
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)
Qiu, J., et al.: Network embedding as matrix factorization: unifying DeepWalk, LINE, PTE, and node2vec. In: Proceedings of the 11th ACM International Conference on Web Search and Data Mining, pp. 459–467 (2018)
Sun, Y., et al.: Pathsim: meta path-based top-k similarity search in heterogeneous information networks. Proc. VLDB Endow, pp. 992–1003 (2011)
Sun, Y., et al.: Integrating meta-path selection with user-guided object clustering in heterogeneous information networks. In: Proceedings of 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 723–724 (2012)
Shi, C., et al.: HeteSim: a general framework for relevance measure in heterogeneous networks. IEEE Trans. Knowl. Data Eng. 26, 2479–2492 (2014)
Agarwal, A., Phillips, J.M., Venkatasubramanian, S.: Universal multi-dimensional scaling. In: Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1149–1158 (2010)
Belkin, M., Niyogi, P.: Laplacian eigenmaps and spectral techniques for embedding and clustering. In: Advances in Neural Information Processing Systems, pp. 585–591 (2002)
Cao, S., Lu, W., Xu, Q.: GraRep: learning graph representations with global structural information. In: Proceedings of the 24th ACM International Conference on Information and Knowledge Management, pp. 891–900 (2015)
Ou, M., et al.: Asymmetric transitivity preserving graph embedding. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1105–1114 (2016)
Wang, X., et al.: Community preserving network embedding. In: 31st AAAI Conference on Artificial Intelligence, pp. 203–209 (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, pp. 135–144 (2017)
Fu, T., Lee, W., Lei, Z.: HIN2Vec: explore meta-paths in heterogeneous information networks for representation learning. In: Proceedings of the 26th ACM on Conference on Information and Knowledge Management, pp. 1797–1806 (2017)
Hu, Z., et al.: Heterogeneous graph transformer. In: Proceedings of the World Wide Web Conference (2020)
Wang, X., et al.: Heterogeneous graph attention network. In: Proceedings of the World Wide Web Conference, pp. 2022–2032 (2019)
Hu, B., Fang, Y., Shi, C.: Adversarial learning on heterogeneous information networks. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 120–129 (2019)
Shi, Y., et al.: Easing embedding learning by comprehensive transcription of heterogeneous information networks. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2190–2199 (2018)
Khoa, N.L.D., Chawla, S.: Large scale spectral clustering using resistance distance and Spielman-Teng solvers. In: International Conference on Discovery Science, pp. 7–21 (2012)
Spielman, D.A., Srivastava, N.: Graph sparsification by effective resistances. In: Proceedings of the 40th Annual ACM Symposium on Theory of Computing, pp. 563–568 (2008)
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Zhang, Y., Yang, X., Wang, L. (2020). Weighted Meta-Path Embedding Learning for Heterogeneous Information Networks. In: Huang, Z., Beek, W., Wang, H., Zhou, R., Zhang, Y. (eds) Web Information Systems Engineering – WISE 2020. WISE 2020. Lecture Notes in Computer Science(), vol 12342. Springer, Cham. https://doi.org/10.1007/978-3-030-62005-9_3
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