Abstract
As a crucial prerequisite for graph mining, graph alignment aims to find node correspondences across multiple correlated graphs. The main difficulty of graph alignment lies in how to seamlessly bridge multiple graphs with distinct topology structures and attribute distributions. A vast majority of earlier efforts tackle this problem based on alignment consistency, which directly measures the attribute and structure similarity of nodes. However, alignment consistency is prone to be violated due to the radically different patterns owned by different graphs. Another group of methods tackle the problem in a supervised manner by learning a mapping function that maps the node representations of both the source and target graphs into the same feature space. However, these methods heavily rely on observed anchor links between different graphs while these anchor links are usually limited or even absent in many real-world applications. To address these issues, we propose an unsupervised cross-graph representation learning framework to jointly learn the node representations of different graphs in a unified deep model. Specifically, we employ an auto-encoder model to learn the cross-graph node representations based on both attribute and structure reconstruction, where source and target graphs share the same encoder but are decoded by their respective decoders. To step further, we also introduce a discriminator to better align the learned representations for different graphs via adversarial training. Extensive experiments on both synthetic and real-world datasets demonstrate the effectiveness of the proposed approach.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Change history
25 April 2021
In the original edition of this chapter, an institution affiliation was missing in the authors’ section for the following authors: Weifan Wang, Minnan Lu, and Qinghua Zheng. The missing institution has been now added.
References
Bayati, M., Gerritsen, M., Gleich, D.F., Saberi, A., Wang, Y.: Algorithms for large, sparse network alignment problems. In: ICDM (2009)
Cao, S., Lu, W., Xu, Q.: Deep neural networks for learning graph representations. In: AAAI (2016)
Chen, C., et al.: Unsupervised adversarial graph alignment with graph embedding. arXiv preprint arXiv:1907.00544 (2019)
Cohen, W., Ravikumar, P., Fienberg, S.: A comparison of string metrics for matching names and records. In: ACM KDD Workshop on Data Cleaning and Object Consolidation (2003)
Grover, A., Leskovec, J.: node2vec: Scalable feature learning for networks. In: ACM SIGKDD (2016)
Heimann, M., Shen, H., Safavi, T., Koutra, D.: Regal: representation learning-based graph alignment. In: ACM CIKM (2018)
Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907 (2016)
Kipf, T.N., Welling, M.: Variational graph auto-encoders. arXiv preprint arXiv:1611.07308 (2016)
Kong, X., Zhang, J., Yu, P.S.: Inferring anchor links across multiple heterogeneous social networks. In: ACM CIKM (2013)
Koutra, D., Tong, H., Lubensky, D.: Big-align: fast bipartite graph alignment. In: ICDM (2013)
Li, J., Hu, X., Tang, J., Liu, H.: Unsupervised streaming feature selection in social media. In: ACM CIKM (2015)
Li, Q., Zhong, J., Li, Q., Cao, Z., Wang, C.: Enhancing network embedding with implicit clustering. In: Li, G., Yang, J., Gama, J., Natwichai, J., Tong, Y. (eds.) DASFAA 2019. LNCS, vol. 11446, pp. 452–467. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-18576-3_27
Liu, L., Cheung, W.K., Li, X., Liao, L.: Aligning users across social networks using network embedding. In: IJCAI (2016)
Lü, L., Zhou, T.: Link prediction in weighted networks: the role of weak ties. EPL (Europhys. Lett.) 89, 18001 (2010)
Man, T., Shen, H., Liu, S., Jin, X., Cheng, X.: Predict anchor links across social networks via an embedding approach. In: IJCAI (2016)
Menon, A.K., Elkan, C.: Link prediction via matrix factorization. In: Gunopulos, D., Hofmann, T., Malerba, D., Vazirgiannis, M. (eds.) ECML PKDD 2011. LNCS (LNAI), vol. 6912, pp. 437–452. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-23783-6_28
Moore, C., Yan, X., Zhu, Y., Rouquier, J.B., Lane, T.: Active learning for node classification in assortative and disassortative networks. In: ACM SIGKDD (2011)
Perozzi, B., Al-Rfou, R., Skiena, S.: DeepWalk: online learning of social representations. In: ACM SIGKDD (2014)
Ramaswamy, L., Gedik, B., Liu, L.: A distributed approach to node clustering in decentralized peer-to-peer networks. IEEE Trans. Parallel Distrib. Syst. 16(9), 814–829 (2005)
Sasikumar, P., Khara, S.: K-means clustering in wireless sensor networks. In: CICN (2012)
Singh, R., Xu, J., Berger, B.: Pairwise global alignment of protein interaction networks by matching neighborhood topology. In: Speed, T., Huang, H. (eds.) RECOMB 2007. LNCS, vol. 4453, pp. 16–31. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-71681-5_2
Tang, J., Qu, M., Wang, M., Zhang, M., Yan, J., Mei, Q.: Line: large-scale information network embedding. In: WWW (2015)
Trouillon, T., Welbl, J., Riedel, S., Gaussier, É., Bouchard, G.: Complex embeddings for simple link prediction. In: ICML (2016)
Wang, D., Cui, P., Zhu, W.: Structural deep network embedding. In: ACM SIGKDD (2016)
Xiong, Y., Zhang, Y., Fu, H., Wang, W., Zhu, Y., Yu, P.S.: DynGraphGAN: dynamic graph embedding via generative adversarial networks. In: Li, G., Yang, J., Gama, J., Natwichai, J., Tong, Y. (eds.) DASFAA 2019. LNCS, vol. 11446, pp. 536–552. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-18576-3_32
Xue, L., Luo, M., Peng, Z., Li, J., Chen, Y., Liu, J.: Anomaly detection in time-evolving attributed networks. In: Li, G., Yang, J., Gama, J., Natwichai, J., Tong, Y. (eds.) DASFAA 2019. LNCS, vol. 11448, pp. 235–239. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-18590-9_19
Zhang, J., Philip, S.Y.: Multiple anonymized social networks alignment. In: ICDM (2015)
Zhang, J., Yu, P.S.: PCT: partial co-alignment of social networks. In: WWW (2016)
Zhang, S., Tong, H.: Final: fast attributed network alignment. In: ACM SIGKDD (2017)
Zhang, S., Tong, H., Tang, J., Xu, J., Fan, W.: iNEAT: incomplete network alignment. In: ICDM (2017)
Zhang, Y., Tang, J., Yang, Z., Pei, J., Yu, P.S.: COSNET: connecting heterogeneous social networks with local and global consistency. In: ACM SIGKDD (2015)
Zhou, F., Liu, L., Zhang, K., Trajcevski, G., Wu, J., Zhong, T.: DeepLink: a deep learning approach for user identity linkage. In: INFOCOM (2018)
Acknowledgement
This work was supported by National Nature Science Foundation of China (No. 61872287, No. 61532015, and No. 61872446), Innovative Research Group of the National Natural Science Foundation of China (No. 61721002), Innovation Research Team of Ministry of Education (IRT_17R86), and Project of China Knowledge Center for Engineering Science and Technology. Besides, this research was funded by National Science and Technology Major Project of the Ministry of Science and Technology of China (No. 2018AAA0102900).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Wang, W., Luo, M., Yan, C., Wang, M., Zhao, X., Zheng, Q. (2020). Cross-Graph Representation Learning for Unsupervised Graph Alignment. In: Nah, Y., Cui, B., Lee, SW., Yu, J.X., Moon, YS., Whang, S.E. (eds) Database Systems for Advanced Applications. DASFAA 2020. Lecture Notes in Computer Science(), vol 12113. Springer, Cham. https://doi.org/10.1007/978-3-030-59416-9_22
Download citation
DOI: https://doi.org/10.1007/978-3-030-59416-9_22
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-59415-2
Online ISBN: 978-3-030-59416-9
eBook Packages: Computer ScienceComputer Science (R0)