Abstract
Neural networks for structured data like graphs have been studied extensively in recent years. To date, the bulk of research activity has focused mainly on static graphs. However, most real-world networks are dynamic since their topology tends to change over time. Predicting the evolution of dynamic graphs is a task of high significance in the area of graph mining. Despite its practical importance, the task has not been explored in depth so far, mainly due to its challenging nature. In this paper, we propose a model that predicts the evolution of dynamic graphs. Specifically, we use a graph neural network along with a recurrent architecture to capture the temporal evolution patterns of dynamic graphs. Then, we employ a generative model which predicts the topology of the graph at the next time step and constructs a graph instance that corresponds to that topology. We evaluate the proposed model on several artificial datasets following common network evolving dynamics, as well as on real-world datasets. Results demonstrate the effectiveness of the proposed model.
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Notes
- 1.
For simplicity, the ordering \(\pi \) will be omitted in what follows.
References
Airoldi, E.M., Blei, D.M., Fienberg, S.E., Xing, E.P.: Mixed membership stochastic blockmodels. J. Mach. Learn. Res. 9, 1981–2014 (2008)
Albert, R., Barabási, A.L.: Statistical mechanics of complex networks. Rev. Mod. Phys. 74(1), 47 (2002)
Bojchevski, A., Shchur, O., Zügner, D., Günnemann, S.: Netgan: generating graphs via random walks. arXiv preprint arXiv:1803.00816 (2018)
Chen, Z., Li, X., Bruna, J.: Supervised community detection with line graph neural networks. arXiv preprint arXiv:1705.08415 (2017)
Conte, D., Foggia, P., Sansone, C., Vento, M.: Thirty years of graph matching in pattern recognition. Int. J. Pattern Recogn. Artif. Intell. 18(03), 265–298 (2004)
Erdős, P., Rényi, A.: On the evolution of random graphs. Publ. Math. Inst. Hung. Acad. Sci 5(1), 17–60 (1960)
Gilmer, J., Schoenholz, S.S., Riley, P.F., Vinyals, O., Dahl, G.E.: Neural message passing for quantum chemistry. In: Proceedings of the 34th International Conference on Machine Learning-Volume 70, pp. 1263–1272. JMLR. org (2017)
Goyal, P., Kamra, N., He, X., Liu, Y.: Dyngem: Deep embedding method for dynamic graphs. arXiv preprint arXiv:1805.11273 (2018)
Grover, A., Zweig, A., Ermon, S.: Graphite: iterative generative modeling of graphs. arXiv preprint arXiv:1803.10459 (2018)
Leskovec, J., Chakrabarti, D., Kleinberg, J., Faloutsos, C., Ghahramani, Z.: Kronecker graphs: an approach to modeling networks. J. Mach. Learn. Res. 11, 985–1042 (2010)
Leskovec, J., Krevl, A.: SNAP datasets: stanford large network dataset collection, June 2014. http://snap.stanford.edu/data
Li, C., Guo, X., Mei, Q.: Deepgraph: graph structure predicts network growth. arXiv preprint arXiv:1610.06251 (2016)
Li, Y., Tarlow, D., Brockschmidt, M., Zemel, R.: Gated graph sequence neural networks. arXiv preprint arXiv:1511.05493 (2015)
Manessi, F., Rozza, A., Manzo, M.: Dynamic graph convolutional networks. arXiv preprint arXiv:1704.06199 (2017)
Meng, C., Mouli, S.C., Ribeiro, B., Neville, J.: Subgraph pattern neural networks for high-order graph evolution prediction. In: Thirty-Second AAAI Conference on Artificial Intelligence (2018)
Morris, C., et al.: Weisfeiler and leman go neural: higher-order graph neural networks. Proceedings of the AAAI Conference on Artificial Intelligence vol. 33, pp. 4602–4609 (2019)
Nguyen, G.H., Lee, J.B., Rossi, R.A., Ahmed, N.K., Koh, E., Kim, S.: Continuous-time dynamic network embeddings. In: Companion Proceedings of the The Web Conference 2018, pp. 969–976. International World Wide Web Conferences Steering Committee (2018)
Nikolentzos, G., Siglidis, G., Vazirgiannis, M.: Graph Kernels: a Survey. arXiv preprint arXiv:1904.12218 (2019)
Pareja, A., et al.: Evolvegcn: evolving graph convolutional networks for dynamic graphs. arXiv preprint arXiv:1902.10191 (2019)
Rossi, R.A., Ahmed, N.K.: The network data repository with interactive graph analytics and visualization. In: AAAI (2015). http://networkrepository.com
Seo, Y., Defferrard, M., Vandergheynst, P., Bresson, X.: Structured sequence modeling with graph convolutional recurrent networks. In: Cheng, L., Leung, A.C.S., Ozawa, S. (eds.) ICONIP 2018. LNCS, vol. 11301, pp. 362–373. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-04167-0_33
Shervashidze, N., Schweitzer, P., Leeuwen, E.J.V., Mehlhorn, K., Borgwardt, K.M.: Weisfeiler-lehman graph kernels. J. Mach. Learn. Res. 12, 2539–2561 (2011)
Vinyals, O., Bengio, S., Kudlur, M.: Order matters: sequence to sequence for sets. arXiv preprint arXiv:1511.06391 (2015)
Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Yu, P.S.: A comprehensive survey on graph neural networks. arXiv preprint arXiv:1901.00596 (2019)
Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018)
You, J., Ying, R., Ren, X., Hamilton, W.L., Leskovec, J.: Graphrnn: Generating realistic graphs with deep auto-regressive models. arXiv preprint arXiv:1802.08773 (2018)
Zhang, M., Chen, Y.: Link prediction based on graph neural networks. In: Advances in Neural Information Processing Systems, pp. 5165–5175 (2018)
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Wu, C., Nikolentzos, G., Vazirgiannis, M. (2020). EvoNet: A Neural Network for Predicting the Evolution of Dynamic Graphs. In: Farkaš, I., Masulli, P., Wermter, S. (eds) Artificial Neural Networks and Machine Learning – ICANN 2020. ICANN 2020. Lecture Notes in Computer Science(), vol 12396. Springer, Cham. https://doi.org/10.1007/978-3-030-61609-0_47
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