ABSTRACT
Social networks have become a major focus of research in recent years, initially directed towards static networks but increasingly, towards dynamic ones. In this paper, we investigate how different pre-processing decisions and different network forces such as selection and influence affect the modeling of dynamic networks. We also present empirical justification for some of the modeling assumptions made in dynamic network analysis (e.g., first-order Markovian assumption) and develop metrics to measure the alignment between links and attributes under different strategies of using the historical network data. We also demonstrate the effect of attribute drift, that is, the importance of individual attributes in forming links change over time.
Supplemental Material
- E. M. Airoldi and K. M. Carley. Sampling algorithms for pure network topologies. SIGKDD Explorations, Dec 2005. Google ScholarDigital Library
- A. Anagnostopousos, R. Kumar, and M. Mahdian. Influence and correlation in social networks. In Proceedings of the Fourteenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2008. Google ScholarDigital Library
- A.-L. Barabasi and E. Bonabeau. Scale-free networks. Scientific American, 288:50--59, May 2003.Google ScholarCross Ref
- S. P. Borgatti and M. G. Everett. Models of core / periphery structures. Social Networks, 21:375--395, 1999.Google ScholarCross Ref
- D. Crandall, D. Cosley, D. Huttenlocher, J. Kleinberg, and S. Suri. Feedback effects between similarity and social influence in online communities. In Proceedings of the Fourteenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2008. Google ScholarDigital Library
- P. ErdÄos and A. Renyi. On the evolution of random graphs. Publications of the Mathematical Institute of the Hungarian Academy of Sciences, 5:17--61, 1960.Google Scholar
- T. Frantz and K. M. Carley. A formal characterization of cellular networks. Technical Report CMU-ISRI-05-109, Carnegie Mellon University, 2005.Google ScholarCross Ref
- C. Guestrin, D. Koller, C. Gearhart, and Neal Kanodia. Generalizing plans to new environments in relational mdps. In International Joint Conference on Artificial Intelligence, 2003. Google ScholarDigital Library
- S. Hanneke and E. Xing. Discrete temporal models of social networks. In Proceedings of the 23rd International Conference on Machine Learning Workshop on Statistical Network Analysis, 2006. Google ScholarDigital Library
- M. Al Hasan, V. Chaoji, S. Salem, and M. Zaki. Link prediction using supervised learning. In Proceedings of SDM'06: SIAM Data Mining Conference Workshop on Link Analysis, Counter-terrorism and Security, 2006.Google Scholar
- Kansas event data system. http://web.ku.edu/keds.Google Scholar
- D. Kempe, J. Kleinberg, and A. Kumar. Connectivity and inference problems for temporal networks. In Proceedings of the thirty-second annual ACM symposium on Theory of computing, 1999. Google ScholarDigital Library
- M. Lahiri and T. Y. Berger-Wolf. Structure prediction in temporal networks using frequent subgraphs. In IEEE Symposium on Computational Intelligence and Data Mining (CIDM), 2007.Google ScholarCross Ref
- J. Leskovec, J. Kleinberg, and C. Faloutsos. Graphs over time: densification laws, shrinking diameters and possible explanations. In Proceedings of the Eleventh ACM SIGKDD International Conference on Knowledge Discovery in Data Mining, 2005. Google ScholarDigital Library
- D. Liben-Nowell and J. Kleinberg. The link prediction problem for social networks. In Proceedings of the 12th International Conference on Information and Knowledge Management, 2003. Google ScholarDigital Library
- J. McPherson, L. Smith-Lovin, and J. Cook. Birds of a feather: Homophily in social networks. Annual Review of Sociology, 27:415--444, 2001.Google ScholarCross Ref
- J. Neville and D. Jensen. Leveraging relational autocorrelation with latent group models. In Proceedings of the Fifth IEEE International Conference on Data Mining, 2005. Google ScholarDigital Library
- J. O'Madadhain, J. Hutchins, and P. Smyth. Prediction and ranking algorithms for event-based network data. SIGKDD Explorations, 7:23--30, Dec 2005. Google ScholarDigital Library
- M. Pearson, C. Steglich, and T. Snijders. Homophily and assimilation among sport-active adolescent substance users. Connections, 27:47--63, 2006.Google Scholar
- A. Popescul and L. H. Ungar. Statistical relational learning for link prediction. In Proceedings of the IJCAI Workshop on Learning Statistical Models from Relational Data, 2003.Google Scholar
- M. Rattigan and D. Jensen. The case for anomalous link discovery. SIGKDD Explorations, 7, 2005. Google ScholarDigital Library
- J. Scripps, P. N. Tan, and A-H Esfahanian. A matrix alignment approach for link prediction. In Proceedings of the Nineteenth international conference on pattern recognition, 2008.Google ScholarCross Ref
- U. Sharan and J. Neville. Temporal-relational classifiers for prediction in evolving domains. In Proceedings of the 8th IEEE International Conference on Data Mining, 2008. Google ScholarDigital Library
- T. Snijders. Models for longitudinal network data. In P. Carrinton, J. Scott, and S. Wasserman, editors, Models and methods in social network analysis. Cambridge University Press, 2004.Google Scholar
- B. Taskar, P. Abbeel, and D. Koller. Discriminative probabilistic models for relational data. In Proceedings of the Eighteenth Conference on Uncertainty in Artificial Intelligence (UAI02), 2002. Google ScholarDigital Library
- B. Taskar, M. F. Wong, P. Abbeel, and D. Koller. Link prediction in relational data. In Neural Information Processing Systems Conference (NIPS03), 2003.Google Scholar
- Siena network statistical analysis program. http://stat.gamma.rug.nl/snijders/siena.html.Google Scholar
- S. Wasserman and P. Pattison. Logit models and logistic regression for social networks: I an introduction to markov graphs and p*. Psychometrika, 61:401--425, 1996.Google ScholarCross Ref
- D. J. Watts and S. H. Strogatz. Collective dynamics of small-world networks. Nature, pages 440--442, Jun 1998.Google Scholar
Index Terms
- Measuring the effects of preprocessing decisions and network forces in dynamic network analysis
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