Abstract
Community detection aims to discover the community structure in the graph. In many systems, community detection plays an important role in its analysis, design, and majorization. However, previous community detection methods underutilize the information between nodes and their neighbors, and usually learn node representation separately from community detection while they are closely related. Therefore, we propose Variational Graph Embedding for Community Detection (VGECD), a new variational graph embedding generation model. VGECD introduces graph attention networks to do aggregation operations on neighbor nodes and jointly learns node representation and the embedding of community detection. We apply the inference model to generate node embedding and community assignment. Then we use the generative model to combine node embedding and community assignment to reconstruct the graph. Experiments on real-world datasets show that VGECD outperforms other comparison methods.
The research work is supported by National Key R &D Program of China under Grant No. 2018YFC0831704, National Nature Science Foundation of China under Grant No. 61806105 and Natural Science Foundation of Shandong Province under Grant No. ZR2022MF243.
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References
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)
Yang, J., Leskovec, J.: Overlapping community detection at scale: a nonnegative matrix factorization approach. In: Proceedings of the Sixth ACM International Conference on Web Search and Data Mining, pp. 587–596 (2013)
Liu, H., Kou, H., Yan, C., Qi, L.: Link prediction in paper citation network to construct paper correlation graph. EURASIP J. Wirel. Commun. Netw. 2019(1), 1–12 (2019). https://doi.org/10.1186/s13638-019-1561-7
Li, Y., Sha, C., Huang, X.: Community detection in attributed graphs: an embedding approach. In: Thirty-Second AAAI Conference on Artificial Intelligence (2018)
Gopalan, P.K., Blei, D.M.: Efficient discovery of overlapping communities in massive networks. Proc. Natl. Acad. Sci. 110(36), 14534–14539 (2013). https://doi.org/10.1073/pnas.1221839110
Tang, J., Aggarwal, C., Liu, H.: Node classification in signed social networks. In: Proceedings of the 2016 SIAM International Conference on Data Mining, pp. 54–62. SIAM (2016). https://doi.org/10.1137/1.9781611974348.7
Kozdoba, M., Mannor, S.: Community detection via measure space embedding. In: Advances in Neural Information Processing Systems, vol. 28 (2015)
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)
Tu, C., et al.: A unified framework for community detection and network representation learning. IEEE Trans. Knowl. Data Eng. 31(6), 1051–1065 (2018)
Cavallari, S., Zheng, V.W., Cai, H., Chang, K.C.C., Cambria, E.: Learning community embedding with community detection and node embedding on graphs. In: Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, pp. 377–386 (2017). https://doi.org/10.1145/3132847.3132925
Kipf, T.N., Welling, M.: Variational graph auto-encoders. arXiv preprint arXiv:1611.07308 (2016). https://doi.org/10.48550/arXiv.1611.07308
Xie, J., Kelley, S., Szymanski, B.K.: Overlapping community detection in networks: the state-of-the-art and comparative study. ACM Comput. Surv. (CSUR) 45(4), 1–35 (2013). https://doi.org/10.1145/2501654.2501657
Yang, J., McAuley, J., Leskovec, J.: Community detection in networks with node attributes. In: 2013 IEEE 13th International Conference on Data Mining, pp. 1151–1156. IEEE (2013). https://doi.org/10.1109/ICDM.2013.167
Sun, F.Y., Qu, M., Hoffmann, J., Huang, C.W., Tang, J.: vgraph: A generative model for joint community detection and node representation learning. In: Advances in Neural Information Processing Systems, vol. 32 (2019)
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)
Tang, J., Qu, M., Wang, M., Zhang, M., Yan, J., Mei, Q.: Line: large-scale information network embedding. In: Proceedings of the 24th International Conference on World Wide Web, pp. 1067–1077 (2015)
Gilmer, J., Schoenholz, S.S., Riley, P.F., Vinyals, O., Dahl, G.E.: Neural message passing for quantum chemistry. In: International Conference on Machine Learning, pp. 1263–1272. PMLR (2017)
Khan, R.A., Anwaar, M.U., Kleinsteuber, M.: Epitomic variational graph autoencoder. In: 2020 25th International Conference on Pattern Recognition (ICPR), pp. 7203–7210. IEEE (2021). https://doi.org/10.1109/ICPR48806.2021.9412531
Wang, X., Cui, P., Wang, J., Pei, J., Zhu, W.: Community preserving network embedding. In: Thirty-First AAAI Conference on Artificial Intelligence (2017)
Khan, R.A., Anwaar, M.U., Kaddah, O., Han, Z., Kleinsteuber, M.: Unsupervised learning of joint embeddings for node representation and community detection. In: Oliver, N., Pérez-Cruz, F., Kramer, S., Read, J., Lozano, J.A. (eds.) ECML PKDD 2021. LNCS (LNAI), vol. 12976, pp. 19–35. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-86520-7_2
Velickovic, P., Fedus, W., Hamilton, W.L., Liò, P., Bengio, Y., Hjelm, R.D.: Deep graph infomax. ICLR (Poster) 2(3), 4 (2019)
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Sun, X., Zhang, W., Wang, Z., Lu, W. (2023). Variational Graph Embedding for Community Detection. In: Tanveer, M., Agarwal, S., Ozawa, S., Ekbal, A., Jatowt, A. (eds) Neural Information Processing. ICONIP 2022. Communications in Computer and Information Science, vol 1793. Springer, Singapore. https://doi.org/10.1007/978-981-99-1645-0_57
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