Abstract
Dynamic networks are ubiquitous. Detecting dynamic network changes is helpful to understand the network development trend and discover network anomalies in time. It is a research hotspot at present. The structure of the network in the real world is very complex, the current feature learning method is difficult to capture a variety of network connectivity patterns, and the definition of efficient network features requires a large number of neighborhood knowledge and computational costs. In order to overcome this limitation, this paper presents a method of dynamic network change detection using network embedding, which automates the whole process by using feature extraction as a embedding problem, and carries out dynamic network change detection by analyzing the distribution of nodes in space after network embedding processing. We use this method to simulate dynamic network and real dynamic network datasets to prove the validity of this method.
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References
Carley, K.M.: Dynamic Network Analysis. Alphascript Publishing (2003)
Perozzi, B., Al-Rfou, R., Skiena, S. (eds.): DeepWalk: online learning of social representations. In: ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2014)
Goyal, P., Ferrara, E.: Graph embedding techniques, applications, and performance: a survey (2017)
McCulloh, I.A., Carley, K.M.: Social network change detection (2008)
Carley, K.M.: ORA: a toolkit for dynamic network analysis and visualization. In: Alhajj, R., Rokne, J. (eds.) Encyclopedia of Social Network Analysis and Mining, pp. 1219–1228. Springer, New York (2014). https://doi.org/10.1007/978-1-4614-6170-8
Yu, W., Aggarwal, C.C., Wang, W. (eds.): Temporally factorized network modeling for evolutionary network analysis. In: Tenth ACM International Conference on Web Search and Data Mining (2017)
Chen, F., Neill, D.B.: Non-parametric scan statistics for event detection and forecasting in heterogeneous social media graphs. J. Biol. Chem. 268, 1166–1175 (2014)
Tang, J., Qu, M., Wang, M., Zhang, M., Yan, J., Mei, Q. (eds.): LINE: large-scale information network embedding. In: International Conference on World Wide Web (2015)
Grover, A., Leskovec, J.: node2vec: scalable feature learning for networks. In: KDD 2016, pp. 855–864 (2016)
Tu, K., Cui, P., Wang, X., Wang, F., Zhu, W.: Structural deep embedding for hyper-networks (2017)
Goldberg, Y., Levy, O.: Word2vec explained: deriving Mikolov et al.’s negative-sampling word-embedding method. Eprint Arxiv (2014)
Watts, D.J., Strogatz, S.H.: Collectivedynamics of ‘small-world’ networks. Nature 393, 440 (1998)
Priebe, C.E., Conroy, J.M., Marchette, D.J., Park, Y.: Scan statistics on Enron graphs. Comput. Math. Organ. Theory 11(3), 229–247 (2005)
Healy, P.M., Palepu, K.G.: The fall of Enron. J. Econ. Perspect. 17(2), 3–26 (2003)
Acknowledgments
This research was supported by the National Natural Science Foundation of China (Grant No. 61309007, U1636219) and the National Key R&D Program of China (Grant No. 2016YFB0801303, 2016QY01W0105).
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Sun, T., Liu, Y. (2018). A Dynamic Network Change Detection Method Using Network Embedding. In: Sun, X., Pan, Z., Bertino, E. (eds) Cloud Computing and Security. ICCCS 2018. Lecture Notes in Computer Science(), vol 11063. Springer, Cham. https://doi.org/10.1007/978-3-030-00006-6_6
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DOI: https://doi.org/10.1007/978-3-030-00006-6_6
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