Abstract
In order to address the problem of reconstruction and retraining time overhead in representation learning processing dynamic networks, this paper proposes an incremental inductive dynamic network community detection algorithm (IINDCD). First, the algorithm uses an attention mechanism to capture node neighborhood information and learn node representations by neighborhood aggregation induction while enhancing low-order structural representations. Second, the design uses random walking to capture high-order information and use it to construct node initial features for input into the attentional autoencoder, which effectively fuses high- and low-order structural features. Finally, the algorithm introduces the ideas of incremental update and model reuse for dynamic representation learning, constructs incremental node sets for updating the model, reduces training overhead, and quickly obtains node representation vectors for new moments of the network, then completing dynamic network community detection. IINDCD runs without reconstruction and with low retraining overhead.
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References
Wu, Z., Chen, J., Zhang, Y.: An incremental community detection method in social big data. In: Proceedings of the 5th IEEE/ACM International Conference on Big Data Computing Applications and Technologies, pp. 136–141 (2018)
Zhang, C., Zhang, Y., Wu, B.: A parallel community detection algorithm based on incremental clustering in dynamic network. In: Proceedings of the IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 946–953 (2018)
Xue, G., Zhong, M., Li, J., et al.: Dynamic network embedding survey. Neurocomputing 472, 212–223 (2022)
Wang, S., Tang, J., Morstatter, F., et al.: Paired restricted Boltzmann machine for linked data. In: Proceedings of the 25th ACM International Conference on Information and Knowledge Management, pp. 1753–1762 (2016)
Nguyen, N.P., Dinh, T.N., Tokala, S., et al.: Overlapping communities in dynamic networks: their detection and mobile applications. In: Proceedings of the 17th Annual International Conference on Mobile Computing and Networking, pp. 85–96 (2011)
Chong, W.H., Teow, L.N.: An incremental batch technique for community detection. In: Proceedings of the 16th International Conference on Information Fusion, pp. 750–757 (2013)
Chen, N., Hu, B., Rui, Y.: Dynamic network community detection with coherent neighborhood propinquity. IEEE Access 8, 27915–27926 (2020)
Long, H., Li, X., Liu, X., et al.: BBTA: detecting communities incrementally from dynamic networks based on tracking of backbones and bridges. Appl. Intell. 53(1), 1084–1100 (2023)
Pandhre, S., Mittal, H., Gupta, M., et al.: STwalk: learning trajectory representations in temporal graphs. In: Proceedings of the ACM India Joint International Conference on Data Science and Management of Data, pp. 210–219 (2018)
Mikolov, T., Chen, K., Corrado, G., et al.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013)
Sankar, A., Wu, Y., Gou, L., et al.: DySAT: deep neural representation learning on dynamic graphs via self-attention networks. In: Proceedings of the 13th International Conference on Web Search and Data Mining, pp. 519–527 (2020)
Li, D., Lin, Q., Ma, X.: Identification of dynamic community in temporal network via joint learning graph representation and nonnegative matrix factorization. Neurocomputing 435, 77–90 (2021)
Perozzi, B., Al-Rfou, R., Skiena, S.: Random Walk: online learning of social representations. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 701–710 (2014)
Klimt, B., Yang, Y.: The Enron corpus: a new dataset for email classification research. In: Boulicaut, J.-F., Esposito, F., Giannotti, F., Pedreschi, D. (eds.) ECML 2004. LNCS (LNAI), vol. 3201, pp. 217–226. Springer, Heidelberg (2004). https://doi.org/10.1007/978-3-540-30115-8_22
Leskovec, J., Kleinberg, J., Faloutsos, C.: Graphs over time: densification laws, shrinking diameters and possible explanations. In: Proceedings of the Eleventh ACM SIGKDD International Conference on Knowledge Detection and Data Mining, pp. 177–187 (2005)
Viswanath, B., Mislove, A., Cha, M., et al.: On the evolution of user interaction in Facebook. In: Proceedings of the 2nd ACM Workshop on Online Social Networks, pp. 37–42 (2009)
Zhuang, H., Sun, Y., Tang, J., et al.: Influence maximization in dynamic social networks. In: Proceedings of the 13th IEEE International Conference on Data Mining, pp. 1313–1318 (2013)
Greene, D., Doyle, D., Cunningham, P.: Tracking the evolution of communities in dynamic social networks. In: Proceedings of the International Conference on Advances in Social Networks Analysis and Mining, pp. 176–183 (2010)
Shang, J., Liu, L., Li, X., et al.: Targeted revision: a learning-based approach for incremental community detection in dynamic networks. Phys. A 443, 70–85 (2016)
Goyal, P., Kamra, N., He, X., et al.: DynGEM: deep embedding method for dynamic graphs. arXiv preprint arXiv:1805.11273 (2018)
Goyal, P., Chhetri, S.R., Canedo, A.: dyngraph2vec: capturing network dynamics using dynamic graph representation learning[J]. Knowl.-Based Syst. 187, 104816 (2020)
Grover, A., Leskovec, J.: node2vec: scalable feature learning for networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Detection and Data Mining, pp. 855–864 (2016)
Wang, C., Pan, S., Hu, R., et al.: Attributed graph clustering: a deep attentional embedding approach. In: Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, pp. 3670–3676 (2019)
Li, B., Pi, D.: Network representation learning: a systematic literature review. Neural Comput. Appl. 32(21), 16647–16679 (2020)
Wang, D., Cui, P., Zhu, W.: Structural deep network embedding. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Detection and Data Mining, pp. 1225–1234 (2016)
Acknowledgement
This work was supported in part by the National Natural Science Foundation of China under Grant 62002063, in part by the Fujian Natural Science Funds under Grant 2020J05112, in part by the Funds of Fujian Provincial Department of Education under Grant JAT190026, and in part by the Fuzhou University under Grant 510872/GXRC-20016, the National Natural Science Foundation of China under Grant No. 62002063 and No. U21A20472, in part by the National Key Research and Development Plan of China under Grant No. 2021YFB3600503, in part by the Fujian Collaborative Innovation Center for Big Data Applications in Governments, in part by the Fujian Industry-Academy Cooperation Project under Grant No. 2018H6010, in part by the Natural Science Foundation of Fujian Province under Grant No. 2020J05112, in part by the Fujian Provincial Department of Education under Grant No. JAT190026, in part by the Major Science and Technology Project of Fujian Province under Grant No. 2021HZ022007 and Haixi Government Big Data Application Cooperative Innovation Center and the China Scholarship Council under Grant 202006655008.
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Wu, L., Zhuang, J., Guo, K. (2024). Incremental Inductive Dynamic Network Community Detection. In: Sun, Y., Lu, T., Wang, T., Fan, H., Liu, D., Du, B. (eds) Computer Supported Cooperative Work and Social Computing. ChineseCSCW 2023. Communications in Computer and Information Science, vol 2012. Springer, Singapore. https://doi.org/10.1007/978-981-99-9637-7_7
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