Representation learning for information diffusion through social networks: An embedded cascade model

WSDM 2016 - Proceedings of the 9th ACM International Conference on Web Search and Data Mining

Volumn , Issue , 2016, Pages 573-582

  Bourigault, Simon ^a,b   Lamprier, Sylvain ^a,b   Gallinari, Patrick ^a,b  

^a SORBONNE UNIVERSITÉ   (France)

^b UNIVERSITÉ PIERRE ET MARIE CURIE   (France)

Author keywords

Information diffusion; Machine learning; Representation Learning

Indexed keywords

ARTIFICIAL INTELLIGENCE; DATA MINING; INFORMATION RETRIEVAL; LEARNING SYSTEMS; STREAM FLOW; WEBSITES; WORLD WIDE WEB;

CASCADE MODEL; GENERALIZATION ABILITY; INFLUENCE RELATIONSHIPS; INFORMATION DIFFUSION; LEARNING APPROACH; PROJECTION SPACE; REAL-WORLD DATASETS; REPRESENTATION LEARNING;

SOCIAL NETWORKING (ONLINE);

EID: 84964355216     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2835776.2835817     Document Type: Conference Paper

References (40)

1. A. Anagnostopoulos, R. Kumar, and M. Mahdian. Inuence and correlation in social networks. In KDD'08, pages 7-15. ACM, 2008.
2. E. Bakshy, I. Rosenn, C. Marlow, and L. Adamic. The role of social networks in information diffusion. In WWW'12, pages 519-528. ACM, 2012.
3. N. Barbieri, F. Bonchi, and G. Manco. Cascade-based community detection. In WSDM '13, pages 33-42, New York, NY, USA, 2013. ACM.
4. Y. Bengio, A. C. Courville, and P. Vincent. Representation learning: A review and new perspectives. IEEE Trans. Pattern Anal. Mach. Intell., 35(8):1798-1828, 2013.
5. A. Bordes, N. Usunier, A. García-Durán, J. Weston, and O. Yakhnenko. Translating embeddings for modeling multi-relational data. In NIPS'13, pages 2787-2795, 2013.
6. S. Bourigault, C. Lagnier, S. Lamprier, L. Denoyer, and P. Gallinari. Learning social network embeddings for predicting information diffusion. In WSDM '14, pages 393-402, New York, NY, USA, 2014. ACM.
7. K. Burton, A. Java, and I. Soboroff. The icwsm 2009 spinn3r dataset. In ICWSM'09, May 2009.
8. O. Celma. Music Recommendation and Discovery in the Long Tail. Springer, 2010.
9. S. Chen, J. L. Moore, D. Turnbull, and T. Joachims. Playlist prediction via metric embedding. In KDD'12, pages 714-722. ACM, 2012.
10. J. Cheng, L. Adamic, P. A. Dow, J. M. Kleinberg, and J. Leskovec. Can cascades be predicted? In WWW'14, pages 925-936, New York, NY, USA, 2014. ACM.
11. K. Cho, B. van Merrienboer, Ç. Gülçehre, D. Bahdanau, F. Bougares, H. Schwenk, and Y. Bengio. Learning phrase representations using RNN encoder-decoder for statistical machine translation. In EMNLP'14, pages 1724-1734, 2014.
12. N. Du, L. Song, M. Yuan, and A. J. Smola. Learning networks of heterogeneous influence. In NIPS'12, pages 2780-2788. Curran Associates, Inc., 2012.
13. J. Goldenberg, B. Libai, and E. Muller. Talk of the network: A complex systems look at the underlying process of word-of-mouth. Marketing letters, 12(3):211-223, 2001.
14. M. Gomez-Rodriguez, D. Balduzzi, and B. Schölkopf. Uncovering the temporal dynamics of diffusion networks. In ICML-11, pages 561-568. ACM, 2011.
15. M. Gomez Rodriguez, J. Leskovec, and A. Krause. Inferring networks of diffusion and influence. In KDD '10, New York, NY, USA, 2010. ACM.
16. M. Granovetter. Threshold Models of Collective Behavior. American Journal of Sociology, 83(6):1420-1143, 1978.
17. A. Graves, A. Mohamed, and G. E. Hinton. Speech recognition with deep recurrent neural networks. In ICASSP'13, pages 6645-6649, 2013.
18. D. Gruhl, R. Guha, D. Liben-Nowell, and A. Tomkins. Information diffusion through blogspace. In WWW '04, pages 491-501, New York, NY, USA, 2004. ACM.
19. A. Guille and H. Hacid. A predictive model for the temporal dynamics of information diffusion in online social networks. In WWW '12 Companion. ACM, 2012.
20. A. Guille, H. Hacid, C. Favre, and D. A. Zighed. Information diffusion in online social networks: A survey. SIGMOD Rec., 42(2):17-28, July 2013.
21. D. Kempe, J. Kleinberg, and E. Tardos. Maximizing the spread of influence through a social network. In KDD '03, pages 137-146. ACM, 2003.
22. C. Lagnier, L. Denoyer, E. Gaussier, and P. Gallinari. Predicting information diffusion in social networks using content and user's profiles. In ECIR '13, 2013.
23. S. Lamprier, S. Bourigault, and P. Gallinari. Extracting diffusion channels from real-world social data: a delay-agnostic learning of transmission probabilities. In ASONAM'15, pages 178-185. IEEE Computer Society, 2015.
24. J. Leskovec, L. Backstrom, and J. Kleinberg. Meme-tracking and the dynamics of the news cycle. In KDD '09, pages 497-506, NY, USA, 2009. ACM.
25. M. Mathioudakis, F. Bonchi, C. Castillo, A. Gionis, and A. Ukkonen. Sparsification of influence networks. In KDD '11, pages 529-537, NY, USA, 2011. ACM.
26. T. Mikolov, I. Sutskever, K. Chen, G. S. Corrado, and J. Dean. Distributed representations of words and phrases and their compositionality. In NIPS'13, pages 3111-3119. Curran Associates, Inc., 2013.
27. A. Mislove, M. Marcon, K. P. Gummadi, P. Druschel, and B. Bhattacharjee. Measurement and analysis of online social networks. In IMC '07, pages 29-42, New York, NY, USA, 2007. ACM.
28. A. Najar, L. Denoyer, and P. Gallinari. Predicting information diffusion on social networks with partial knowledge. In WWW '12 Companion, pages 1197-1204, New York, NY, USA, 2012. ACM.
29. P. Netrapalli and S. Sanghavi. Learning the graph of epidemic cascades. SIGMETRICS Perform. Eval. Rev., 40(1):211-222, June 2012.
30. T. Opsahl and P. Panzarasa. Clustering in weighted networks. Social networks, 31(2):155-163, 2009.
31. M. Sachan, A. Dubey, S. Srivastava, E. P. Xing, and E. Hovy. Spatial compactness meets topical consistency: Jointly modeling links and content for community detection. In WSDM '14, pages 503-512, New York, NY, USA, 2014. ACM.
32. K. Saito, M. Kimura, K. Ohara, and H. Motoda. Learning continuous-time information diffusion model for social behavioral data analysis. In ACML '09, pages 322-337, Berlin, Heidelberg, 2009. Springer-Verlag.
33. K. Saito, M. Kimura, K. Ohara, and H. Motoda. Generative models of information diffusion with asynchronous timedelay. Journal of Machine Learning Research - Proceedings Track, 13:193-208, 2010.
34. K. Saito, R. Nakano, and M. Kimura. Prediction of information diffusion probabilities for independent cascade model. In KES '08, pages 67-75. Springer-Verlag, 2008.
35. K. Saito, K. Ohara, Y. Yamagishi, M. Kimura, and H. Motoda. Learning diffusion probability based on node attributes in social networks. In M. Kryszkiewicz, H. Rybinski, A. Skowron, and Z. W. Ras, editors, ISMIS, volume 6804 of LNCS, pages 153-162. Springer, 2011.
36. H. Su, A. Gionis, and J. Rousu. Structured prediction of network response. In ICML'14, pages 442-450. JMLR Workshop and Conference Proceedings, 2014.
37. G. Ver Steeg and A. Galstyan. Information-theoretic measures of influence based on content dynamics. In WSDM'13, pages 3-12, New York, NY, USA, 2013. ACM.
38. L. Wang, S. Ermon, and J. E. Hopcroft. Feature-enhanced probabilistic models for diffusion network inference. In ECML PKDD'12, pages 499-514. Springer-Verlag, 2012.
39. J. Yang and J. Leskovec. Modeling information diffusion in implicit networks. In ICDM'10, pages 599-608, Washington, DC, USA, 2010. IEEE Computer Society.
40. J. Yang, J. McAuley, and J. Leskovec. Detecting cohesive and 2-mode communities indirected and undirected networks. In WSDM '14, pages 323-332, New York, NY, USA, 2014. ACM.