Yu Rong
ABSTRACT
In nowadays social networks, a huge volume of content containing rich information, such as reviews, ratings, microblogs, etc., is being generated, consumed and diffused by users all the time. Given the temporal information, we can obtain the event cascade which indicates the time sequence of the arrival of information to users. Many models have been proposed to explain how information diffuses. However, most existing models cannot give a clear explanation why every specific event happens in the event cascade. Such explanation is essential for us to have a deeper understanding of information diffusion as well as a better prediction of future event cascade.
In order to uncover the mechanism of the happening of social events, we analyze the rating event data crawled from Douban.com, a Chinese social network, from year 2006 to 2011. We distinguish three factors: social, external and intrinsic influence which can explain the emergence of every specific event. Then we use the mixed Poisson process to model event cascade generated by different factors respectively and integrate different Poisson processes with shared parameters. The proposed model, called Combinational Mixed Poisson Process (CMPP) model, can explain not only how information diffuses in social networks, but also why a specific event happens. This model can help us to understand information diffusion from both macroscopic and microscopic perspectives. We develop an efficient Classification EM algorithm to infer the model parameters. The explanatory and predictive power of the proposed model has been demonstrated by the experiments on large real data sets.
Supplemental Material
- A. Anagnostopoulos, R. Kumar, and M. Mahdian. Influence and correlation in social networks. In KDD, pages 7--15, 2008. Google Scholar
Digital Library
- S. Bourigault, C. Lagnier, S. Lamprier, L. Denoyer, and P. Gallinari. Learning social network embeddings for predicting information diffusion. In WSDM, pages 393--402, 2014. Google ScholarDigital Library
- G. Celeux and G. Govaert. A classification em algorithm for clustering and two stochastic versions. Computational Statistics and Data Analysis, 14(3):315--332, 1992. Google ScholarDigital Library
- R. Crane and D. Sornette. Robust dynamic classes revealed by measuring the response function of a social system. PNAS, 105(41):15649--15653, 2008.Google ScholarCross Ref
- D. J. Daley and D. Vere-Jones. An Introduction to the Theory of Point Processes, Volume I. Springer, New York, 2003.Google ScholarCross Ref
- 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.Google ScholarCross Ref
- M. Gomez-Rodriguez, D. Balduzzi, and B. Schölkopf. Uncovering the temporal dynamics of diffusion networks. In ICML, pages 561--568, 2011.Google Scholar
- M. Gomez-Rodriguez, J. Leskovec, and A. Krause. Inferring networks of diffusion and influence. In KDD, pages 1019--1028, 2010. Google ScholarDigital Library
- M. Gomez-Rodriguez, J. Leskovec, and B. Schölkopf. Modeling information propagation with survival theory. In ICML, pages 666--674, 2013.Google Scholar
- M. Gomez-Rodriguez, J. Leskovec, and B. Schölkopf. Structure and dynamics of information pathways in online media. In WSDM, pages 23--32, 2013. Google ScholarDigital Library
- A. Guille and H. Hacid. A predictive model for the temporal dynamics of information diffusion in online social networks. In WWW, pages 1145--1152, 2012. Google ScholarDigital Library
- T.-A. Hoang and E.-P. Lim. Retweeting: An act of viral users, susceptible users, or viral topics? In SDM, pages 569--577, 2013.Google ScholarCross Ref
- T. Iwata, A. Shah, and Z. Ghahramani. Discovering latent influence in online social activities via shared cascade poisson processes. In KDD, pages 266--274, 2013. Google ScholarDigital Library
- S. J. Karau and K. D. Williams. Social loafing: Research findings, implications, and future directions. Current Directions in Psychological Science, 4(5):134--140, 1995.Google ScholarCross Ref
- D. Kempe, J. Kleinberg, and É. Tardos. Maximizing the spread of influence through a social network. In KDD, pages 137--146, 2003. Google ScholarDigital Library
- C. Lagnier, L. Denoyer, E. Gaussier, and P. Gallinari. Predicting information diffusion in social networks using content and user's profiles. In ECIR, pages 74--85, 2013. Google ScholarDigital Library
- Y. Li, W. Chen, Y. Wang, and Z.-L. Zhang. Influence diffusion dynamics and influence maximization in social networks with friend and foe relationships. In WSDM, pages 657--666, 2013. Google ScholarDigital Library
- H. Ma, H. Yang, M. R. Lyu, and I. King. Mining social networks using heat diffusion processes for marketing candidates selection. In CIKM, pages 233--242, 2008. Google ScholarDigital Library
- S. A. Myers, C. Zhu, and J. Leskovec. Information diffusion and external influence in networks. In KDD, pages 33--41, 2012. Google ScholarDigital Library
- K. Saito, M. Kimura, K. Ohara, and H. Motoda. Generative models of information diffusion with asynchronous time-delay. In ACML, pages 193--208, 2010.Google Scholar
- K. Saito, R. Nakano, and M. Kimura. Prediction of information diffusion probabilities for independent cascade model. In KES, pages 67--75. Springer, 2008. Google ScholarDigital Library
- K. Saito, K. Ohara, Y. Yamagishi, M. Kimura, and H. Motoda. Learning diffusion probability based on node attributes in social networks. In FIS, pages 153--162, 2011. Google ScholarDigital Library
- A. Silva, S. Guimarães, W. Meira Jr, and M. Zaki. Profilerank: finding relevant content and influential users based on information diffusion. In SNA-KDD, page 2, 2013. Google ScholarDigital Library
- A. Simma and M. I. Jordan. Modeling events with cascades of poisson processes. In UAI, pages 546--555, 2010.Google Scholar
- E. Zhong, W. Fan, J. Wang, L. Xiao, and Y. Li. Comsoc: Adaptive transfer of user behaviors over composite social network. In KDD, pages 696--704, 2012. Google ScholarDigital Library
Index Terms
- Why It Happened: Identifying and Modeling the Reasons of the Happening of Social Events
Recommendations
Event Prediction in the Big Data Era: A Systematic Survey
Events are occurrences in specific locations, time, and semantics that nontrivially impact either our society or the nature, such as earthquakes, civil unrest, system failures, pandemics, and crimes. It is highly desirable to be able to anticipate the ...
Learning Dynamic Context Graphs for Predicting Social Events
KDD '19: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data MiningEvent forecasting with an aim at modeling contextual information is an important task for applications such as automated analysis generation and resource allocation. Captured contextual information for an event of interest can aid human analysts in ...
Multi-level Hyperedge Distillation for Social Linking Prediction on Sparsely Observed Networks
WWW '21: Proceedings of the Web Conference 2021Social linking prediction is one of the most fundamental problems in online social networks and has attracted researchers’ persistent attention. Most of the existing works predict unobserved links using graph neural networks (GNNs) to learn node ...
Comments