Kazumi Saito
Abstract
We addressed the problem of detecting the change in behavior of information diffusion over a social network which is caused by an unknown external situation change using a small amount of observation data in a retrospective setting. The unknown change is assumed effectively reflected in changes in the parameter values in the probabilistic information diffusion model, and the problem is reduced to detecting where in time and how long this change persisted and how big this change is. We solved this problem by searching the change pattern that maximizes the likelihood of generating the observed information diffusion sequences, and in doing so we devised a very efficient general iterative search algorithm using the derivative of the likelihood which avoids parameter value optimization during each search step. This is in contrast to the naive learning algorithm in that it has to iteratively update the patten boundaries, each requiring the parameter value optimization and thus is very inefficient. We tested this algorithm for two instances of the probabilistic information diffusion model which has different characteristics. One is of information push style and the other is of information pull style. We chose Asynchronous Independent Cascade (AsIC) model as the former and Value-weighted Voter (VwV) model as the latter. The AsIC is the model for general information diffusion with binary states and the parameter to detect its change is diffusion probability and the VwV is the model for opinion formation with multiple states and the parameter to detect its change is opinion value. The results tested on these two models using four real-world network structures confirm that the algorithm is robust enough and can efficiently identify the correct change pattern of the parameter values. Comparison with the naive method that finds the best combination of change boundaries by an exhaustive search through a set of randomly selected boundary candidates shows that the proposed algorithm far outperforms the native method both in terms of accuracy and computation time.
- Castellano, C., Munoz, M. A., and Pastor-Satorras, R. 2009. Nonlinear q-voter model. Phys. Rev. E 80, 041129.Google Scholar
Cross Ref
- Chen, W., Wang, C., and Wang, Y. 2010a. Scalable influence maximization for prevalent viral marketing in large-scale social networks. In Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'10). 1029--1038. Google ScholarDigital Library
- Chen, W., Yuan, Y., and Zhang, L. 2010b. Scalable influence maximization in social networks under the linear threshold model. In Proceedings of the 10th IEEE International Conference on Data Mining (ICDM'10). 88--97. Google ScholarDigital Library
- Crandall, D., Cosley, D., Huttenlocner, D., Kleinberg, J., and Suri, S. 2008. Feedback effects between similarity and social influence in online communities. In Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'08). 160--168. Google ScholarDigital Library
- Domingos, P. 2005. Mining social networks for viral marketing. IEEE Intell. Syst. 20, 80--82.Google ScholarDigital Library
- Even-Dar, E. and Shapria, A. 2007. A note on maximizing the spread of influence in social networks. In Proceedings of the 3rd International Conference on Internet and Network Economics. 281--286. Google ScholarDigital Library
- Goldenberg, J., Libai, B., and Muller, E. 2001. Talk of the network: A complex systems look at the underlying process of word-of-mouth. Market. Lett. 12, 211--223.Google ScholarCross Ref
- Gomez-Rodriguez, M., Leskovec, J., and Krause, A. 2010. Inferring networks of diffusion and influence. In Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'10). 1019--1028. Google ScholarDigital Library
- Gruhl, D., Guha, R., Liben-Nowell, D., and Tomkins, A. 2004. Information diffusion through blogspace. SIGKDD Explor. 6, 43--52. Google ScholarDigital Library
- Holme, P. and Newman, M. E. J. 2006. Nonequilibrium phase transition in the coevolution of networks and opinions. Phys. Rev. E 74, 056108.Google ScholarCross Ref
- Kempe, D., Kleinberg, J., and Tardos, E. 2003. Maximizing the spread of influence through a social network. In Proceedings of the 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'03). 137--146. Google ScholarDigital Library
- Kimura, M., Saito, K., and Motoda, H. 2008. Minimizing the spread of contamination by blocking links in a network. In Proceedings of the 23rd AAAI Conference on Artificial Intelligence (AAAI'08). 1175--1180. Google ScholarDigital Library
- Kimura, M., Saito, K., and Motoda, H. 2009. Blocking links to minimize contamination spread in a social network. ACM Trans. Knowl. Discov. Data 3, 9:1--9:23. Google ScholarDigital Library
- Kimura, M., Saito, K., Nakano, R., and Motoda, H. 2010a. Extracting influential nodes on a social network for information diffusion. Data Min. Knowl. Disc. 20, 70--97. Google ScholarDigital Library
- Kimura, M., Saito, K., Ohara, K., and Motoda, H. 2010b. Learning to predict opinion share in social networks. In Proceedings of the 24th AAAI Conference on Artificial Intelligence (AAAI'10). 1364--1370.Google Scholar
- Kimura, M., Saito, K., Ohara, K., and Motoda, H. 2011. Detecting anti-majority opinionists using value-weighted mixture voter model. In Proceedings of the 14th International Conference on Discovery Science (DS'11). Lecture Notes in Artificial Intelligence, vol. 6926, Springer, 150--164. Google ScholarDigital Library
- Kleinberg, J. 2002. Bursty and hierarchical structure in streams. In Proceedings of the 8th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'02). 91--101. Google ScholarDigital Library
- Klimt, B. and Yang, Y. 2004. The enron corpus: A new dataset for email classification research. In Proceedings of the European Conference on Machine Learning (ECML'04). 217--226.Google Scholar
- Leskovec, J., Adamic, L. A., and Huberman, B. A. 2006. The dynamics of viral marketing. In Proceedings of the 7th ACM Conference on Electronic Commerce (EC'06). 228--237. Google ScholarDigital Library
- Liggett, T. M. 1999. Stochastic Interacting Systems: Contact, Voter, and Exclusion Processes. Springer, New York.Google Scholar
- Myers, S. A. and Leskovec, J. 2010. On the convexity of latent social network inference. In Proceedings of the 24th Conference on Neural Information Processing Systems (NIPS'10).Google Scholar
- Newman, M. E. J. 2003. The structure and function of complex networks. SIAM Rev. 45, 167--256.Google ScholarDigital Library
- Newman, M. E. J., Forrest, S., and Balthrop, J. 2002. Email networks and the spread of computer viruses. Phys. Rev. E 66, 035101.Google ScholarCross Ref
- Ohara, K., Saito, K., Kimura, M., and Motoda, H. 2011. Efficient detection of hot span in information diffusion from observation. In Proceedings of the IJCAI Workshop on Link Analysis in Heterogeneous Information Networks (HINA'11).Google Scholar
- Palla, G., Derenyi, I., Farkas, I., and Vicsek, T. 2005. Uncovering the overlapping community structure of complex networks in nature and society. Nature 435, 814--818.Google ScholarCross Ref
- Saito, K., Kimura, M., Nakano, R., and Motoda, H. 2009a. Finding influential nodes in a social network from information diffusion data. In Proceedings of the International Workshop on Social Computing and Behavioral Modeling (SBP'09). 138--145.Google Scholar
- Saito, K., Kimura, M., Ohara, K., and Motoda, H. 2009b. Learning continuous-time information diffusion model for social behavioral data analysis. In Proceedings of the 1st Asian Conference on Machine Learning (ACML'09). Lecture Notes in Artificial Intelligence, vol. 5828, 322--337. Google ScholarDigital Library
- Saito, K., Kimura, M., Ohara, K., and Motoda, H. 2010a. Behavioral analyses of information diffusion models by observed data of social network. In Proceedings of the International Conference on Social Computing and Behavioral Modeling (SBP'10). 149--158. Google ScholarDigital Library
- Saito, K., Kimura, M., Ohara, K., and Motoda, H. 2010b. Selecting information diffusion models over social networks for behavioral analysis. In Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD'10). Lecture Notes in Artificial Intelligence, vol. 6323, Springer, 180--195. Google ScholarDigital Library
- Saito, K., Kimura, M., Ohara, K., and Motoda, H. 2011a. Detecting changes in opinion value distribution for voter model. In Proceedings of the 4th International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction (SBP'11). Lecture Notes in Artificial Intelligence, vol. 6389, Springer, 89--96. Google ScholarDigital Library
- Saito, K., Ohara, K., Yamagishi, Y., Kimura, M., and Motoda, H. 2011b. Learning diffusion probability based on node attributes in social networks. In Proceedings of the 19th International Symposium on Methodologies for Intelligent Systems (ISMIS'11). Lecture Notes in Artificial Intelligence, vol. 6804, 153--162. Google ScholarDigital Library
- Sood, V. and Redner, S. 2005. Voter model on heterogeneous graphs. Phys. Rev. Lett. 94, 178701.Google ScholarCross Ref
- Swan, R. and Allan, J. 2000. Automatic generation of overview timelines. In Proceedings of the 23rd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR'00). 49--56. Google ScholarDigital Library
- Watts, D. J. 2002. A simple model of global cascades on random networks. Proc. Nat. Acad. Sci. 99, 5766--5771.Google ScholarCross Ref
- Watts, D. J. and Dodds, P. S. 2007. Influence, networks, and public opinion formation. J. Consumer Res. 34, 441--458.Google ScholarCross Ref
- Wu, F. and Huberman, B. A. 2008. How public opinion forms. In Proceedings of the Workshop on Internet and Network Economics (WINE'08). 334--341. Google ScholarDigital Library
- Yamagishi, Y., Saito, K., Ohara, K., Kimura, M., and Motoda, H. 2011. Learning attribute-weighted voter model over social networks. In Proceedings of the 3rd Asian Conference on Machine Learning (ACML'11).Google Scholar
- Yang, H., Wu, Z., Zhou, C., Zhou, T., and Wang, B. 2009. Effects of social diversity on the emergence of global consensus in opinion dynamics. Phys. Rev. E 80, 046108.Google ScholarCross Ref
Index Terms
- Detecting changes in information diffusion patterns over social networks
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