Learning Distributed Representations of Users for Source Detection in Online Social Networks
Conference paper
First Online:
Abstract
In this paper, we study the problem of source detection in the context of information diffusion through online social networks. We propose a representation learning approach that leads to a robust model able to deal with the sparsity of the data. From learned continuous projections of the users, our approach is able to efficiently predict the source of any newly observed diffusion episode. Our model does not rely neither on a known diffusion graph nor on a hypothetical probabilistic diffusion law, but directly infers the source from diffusion episodes. It is also less complex than alternative state of the art models. It showed good performances on artificial and real-world datasets, compared with various state of the art baselines.
Keywords
Latent Space Online Social Network Source User Infected Node Artificial Dataset
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
Notes
Acknowledgments
This work has been partially supported by the following projects: Xu Guangqi 2016 Deep learning for Large Scale Dynamic and Spatio-Temporal Data; REQUEST Investissement d’Avenir 2014 and LOCUST ANR 2015 (ANR-15-CE23-0027-01).
References
- 1.Barbieri, N., Bonchi, F., Manco, G.: Cascade-based community detection. In: Proceedings of the Sixth ACM International Conference on Web Search and Data Mining, WSDM 2013, NY, USA, pp. 33–42. ACM, New York (2013)Google Scholar
- 2.Bourigault, S., Lagnier, C., Lamprier, S., Denoyer, L., Gallinari, P.: Learning social network embeddings for predicting information diffusion. In: WSDM 2014, NY, USA, pp. 393–402. ACM, New York (2014)Google Scholar
- 3.Chen, S., Moore, J.L., Turnbull, D., Joachims, T.: Playlist prediction via metric embedding. In: KDD 2012, pp. 714–722. ACM (2012)Google Scholar
- 4.Dong, W., Zhang, W., Tan, C.W.: Rooting out the rumor culprit from suspects. In: ISIT 2013, pp. 2671–2675. IEEE (2013)Google Scholar
- 5.Farajtabar, M., Gomez-Rodriguez, M., Du, N., Zamani, M., Zha, H., Song, L.: Back to the past: source identification in diffusion networks from partially observed cascades. arXiv preprint (2015). arXiv:1501.06582
- 6.Fu, K., Chan, C.h., Chau, M.: Assessing censorship on microblogs in China: discriminatory keyword analysis and the real-name registration policy. IEEE Internet Comput. 17(3), 42–50 (2013)Google Scholar
- 7.Gomez-Rodriguez, M., Balduzzi, D., Schölkopf, B.: Uncovering the temporal dynamics of diffusion networks. In: ICML 2011, pp. 561–568. ACM (2011)Google Scholar
- 8.Guille, A., Hacid, H., Favre, C., Zighed, D.A.: Information diffusion in online social networks: a survey. SIGMOD Rec. 42(2), 17–28 (2013)CrossRefGoogle Scholar
- 9.Kempe, D., Kleinberg, J., Tardos, E.: Maximizing the spread of influence through a social network. In: KDD 2003, pp. 137–146. ACM (2003)Google Scholar
- 10.Lamprier, S., Bourigault, S., Gallinari, P.: Extracting diffusion channels from real-world social data: a delay-agnostic learning of transmission probabilities. In: ASONAM 2015. IEEE Computer Society (2015)Google Scholar
- 11.Lappas, T., Terzi, E., Gunopulos, D., Mannila, H.: Finding effectors in social networks. In: KDD 2010, pp. 1059–1068. ACM (2010)Google Scholar
- 12.Leskovec, J., Backstrom, L., Kleinberg, J.: Meme-tracking and the dynamics of the news cycle. In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2009, NY, USA, pp. 497–506. ACM, New York (2009)Google Scholar
- 13.Luo, W., Tay, W.P., Leng, M.: Rumor spreading and source identification: a hide and seek game. arXiv preprint (2015). arXiv:1504.04796
- 14.Luo, W., Tay, W.P., Leng, M., Guevara, M.: On the universality of the Jordan center for estimating the rumor source in a social network. In: DSP 2015, pp. 760–764, July 2015Google Scholar
- 15.Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint (2013). arXiv:1301.3781
- 16.Pinto, P.C., Thiran, P., Vetterli, M.: Locating the source of diffusion in large-scale networks. Phys. Rev. Lett. 109(6), 068702 (2012)CrossRefGoogle Scholar
- 17.Prakash, B.A., Vreeken, J., Faloutsos, C.: Spotting culprits in epidemics: how many and which ones? In: ICDM 2012, pp. 11–20. IEEE (2012)Google Scholar
- 18.Saito, K., Nakano, R., Kimura, M.: Prediction of information diffusion probabilities for independent cascade model. In: Lovrek, I., Howlett, R.J., Jain, L.C. (eds.) KES 2008. Lecture Notes in Artificial Intelligence (LNAI), vol. 5179, pp. 67–75. Springer, Heidelberg (2008). doi: 10.1007/978-3-540-85567-5_9 CrossRefGoogle Scholar
- 19.Seo, E., Mohapatra, P., Abdelzaher, T.: Identifying rumors and their sources in social networks. In: SPIE Defense, Security, and Sensing, pp. 83891I–83891I. International Society for Optics and Photonics (2012)Google Scholar
- 20.Shah, D., Zaman, T.: Detecting sources of computer viruses in networks: theory and experiment. In: ACM SIGMETRICS Performance Evaluation Review, vol. 38, pp. 203–214. ACM (2010)Google Scholar
- 21.Shah, D., Zaman, T.: Rumors in a network: who’s the culprit? IEEE Trans. Inf. Theory 57(8), 5163–5181 (2011)MathSciNetCrossRefGoogle Scholar
- 22.Shah, D., Zaman, T.: Rumor centrality: a universal source detector. In: ACM SIGMETRICS Performance Evaluation Review, vol. 40, pp. 199–210. ACM (2012)Google Scholar
- 23.Tsur, O., Rappoport, A.: What’s in a hashtag? Content based prediction of the spread of ideas in microblogging communities. In: Proceedings of the Fifth ACM International Conference on Web Search and Data Mining, pp. 643–652. ACM (2012)Google Scholar
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