Abstract
Networks are structures that naturally capture relations between entities in different data sources and information systems. To establish the connections among different networks, the task of network alignment is proposed and intensively studied in network-related research field. Most network alignment methods are based on the representation learning of network structure, which rely only on network topology and are susceptible to structural noise. In addition, such methods focus on the global features, while largely neglect local structure features and fail to take account of the data sparsity issue in real networks. To address these pivotal issues, we propose a novel network alignment method based on iterative deep network learning and local feature augmentation. We first design an iterative deep graph learning model to learn high-quality network structural representation and reduce the structural noise. Furthermore, we embed knowledge representation learning method into the alignment process, which helps to characterize better local structure and alleviate the data sparsity issue. Experiments on real-world network datasets demonstrate that our proposed model achieves state-of-the-art alignment results.
Supported by Ministry of Science and Technology of China under grants No. 2020AAA0108800, NSFC under grants Nos. 71971212 and 61902417.
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References
Albert, R., Barabási, A.L.: Statistical mechanics of complex networks. Rev. Mod. Phys. 74(1), 47 (2002)
Bayati, M., Gerritsen, M., Gleich, D.F., Saberi, A., Wang, Y.: Algorithms for large, sparse network alignment problems. In: 2009 Ninth IEEE International Conference on Data Mining, pp. 705–710. IEEE (2009)
Bordes, A., Weston, J., Collobert, R., Bengio, Y.: Learning structured embeddings of knowledge bases. In: Twenty-Fifth AAAI Conference on Artificial Intelligence (2011)
Chen, Y., Wu, L., Zaki, M.: Iterative deep graph learning for graph neural networks: better and robust node embeddings. Adv. Neural. Inf. Process. Syst. 33, 19314–19326 (2020)
Gao, H., Zhang, Y., Li, B.: Improving the link prediction by exploiting the collaborative and context-aware social influence. In: Li, J., Wang, S., Qin, S., Li, X., Wang, S. (eds.) ADMA 2019. LNCS (LNAI), vol. 11888, pp. 302–315. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-35231-8_22
Goyal, P., Ferrara, E.: Graph embedding techniques, applications, and performance: a survey. Knowl.-Based Syst. 151, 78–94 (2018)
Guzzi, P.H., Milenković, T.: Survey of local and global biological network alignment: the need to reconcile the two sides of the same coin. Brief. Bioinform. 19(3), 472–481 (2018)
Heimann, M., Shen, H., Safavi, T., Koutra, D.: Regal: representation learning-based graph alignment. In: Proceedings of the 27th ACM International Conference on Information and Knowledge Management, pp. 117–126 (2018)
Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907 (2016)
Koutra, D., Tong, H., Lubensky, D.: Big-align: fast bipartite graph alignment. In: 2013 IEEE 13th International Conference on Data Mining, pp. 389–398. IEEE (2013)
Levy, O., Goldberg, Y.: Neural word embedding as implicit matrix factorization. In: Advances in Neural Information Processing Systems, vol. 27 (2014)
Liu, J., Shao, Y., Su, S.: Multiple local community detection via high-quality seed identification over both static and dynamic networks. Data Sci. Eng. 6(3), 249–264 (2021)
Liu, L., Cheung, W.K., Li, X., Liao, L.: Aligning users across social networks using network embedding. In: IJCAI, pp. 1774–1780 (2016)
Man, T., Shen, H., Jin, X., Cheng, X.: Cross-domain recommendation: an embedding and mapping approach. In: Proceedings of the 26th International Joint Conference on Artificial Intelligence, pp. 2464–2470 (2017)
Man, T., Shen, H., Liu, S., Jin, X., Cheng, X.: Predict anchor links across social networks via an embedding approach. In: IJCAI, vol. 16, pp. 1823–1829 (2016)
Ou, M., Cui, P., Pei, J., Zhang, Z., Zhu, W.: Asymmetric transitivity preserving graph embedding. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1105–1114 (2016)
Qiu, J., Dong, Y., Ma, H., Li, J., Wang, K., Tang, J.: Network embedding as matrix factorization: unifying Deepwalk, Line, PTE, and Node2Vec. In: Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining, pp. 459–467 (2018)
Singh, R., Xu, J., Berger, B.: Global alignment of multiple protein interaction networks with application to functional orthology detection. Proc. Natl. Acad. Sci. 105(35), 12763–12768 (2008)
Sun, Z., Hu, W., Zhang, Q., Qu, Y.: Bootstrapping entity alignment with knowledge graph embedding. In: IJCAI, vol. 18, pp. 4396–4402 (2018)
Sun, Z., Deng, Z.H., Nie, J.Y., Tang, J.: Rotate: knowledge graph embedding by relational rotation in complex space. In: International Conference on Learning Representations (2018)
Trouillon, T., Welbl, J., Riedel, S., Gaussier, É., Bouchard, G.: Complex embeddings for simple link prediction. In: International Conference on Machine Learning, pp. 2071–2080. PMLR (2016)
Trung, H.T., Van Vinh, T., Tam, N.T., Yin, H., Weidlich, M., Hung, N.Q.V.: Adaptive network alignment with unsupervised and multi-order convolutional networks. In: 2020 IEEE 36th International Conference on Data Engineering (ICDE), pp. 85–96. IEEE (2020)
Viswanath, B., Mislove, A., Cha, M., Gummadi, K.P.: On the evolution of user interaction in Facebook. In: Proceedings of the 2nd ACM Workshop on Online Social Networks, pp. 37–42 (2009)
Wang, Z., Zhang, J., Feng, J., Chen, Z.: Knowledge graph embedding by translating on hyperplanes. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 28 (2014)
Yang, B., Yih, W.T., He, X., Gao, J., Deng, L.: Embedding entities and relations for learning and inference in knowledge bases. arXiv preprint arXiv:1412.6575 (2014)
Zeng, W., Zhao, X., Wang, W., Tang, J., Tan, Z.: Degree-aware alignment for entities in tail. In: Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 811–820 (2020)
Zhang, J., Philip, S.Y.: Multiple anonymized social networks alignment. In: 2015 IEEE International Conference on Data Mining, pp. 599–608. IEEE (2015)
Zhang, S., Tong, H.: Final: fast attributed network alignment. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1345–1354 (2016)
Zhou, C., Liu, Y., Liu, X., Liu, Z., Gao, J.: Scalable graph embedding for asymmetric proximity. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017)
Zhou, F., Liu, L., Zhang, K., Trajcevski, G., Wu, J., Zhong, T.: Deeplink: a deep learning approach for user identity linkage. In: IEEE INFOCOM 2018-IEEE Conference on Computer Communications. pp. 1313–1321. IEEE (2018)
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Tang, J., Tan, Z., Guo, H., Huang, X., Zeng, W., Peng, H. (2023). Iterative Deep Graph Learning with Local Feature Augmentation for Network Alignment. In: Li, B., Yue, L., Tao, C., Han, X., Calvanese, D., Amagasa, T. (eds) Web and Big Data. APWeb-WAIM 2022. Lecture Notes in Computer Science, vol 13421. Springer, Cham. https://doi.org/10.1007/978-3-031-25158-0_41
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