Connectivity and inference problems for temporal networks
STOC '00 Proceedings of the thirty-second annual ACM symposium on Theory of computing
The link prediction problem for social networks
CIKM '03 Proceedings of the twelfth international conference on Information and knowledge management
Graphs over time: densification laws, shrinking diameters and possible explanations
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Leveraging Relational Autocorrelation with Latent Group Models
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
ACM SIGKDD Explorations Newsletter
Prediction and ranking algorithms for event-based network data
ACM SIGKDD Explorations Newsletter
The case for anomalous link discovery
ACM SIGKDD Explorations Newsletter
Influence and correlation in social networks
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Feedback effects between similarity and social influence in online communities
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Temporal-Relational Classifiers for Prediction in Evolving Domains
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
Generalizing plans to new environments in relational MDPs
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Discrete temporal models of social networks
ICML'06 Proceedings of the 2006 conference on Statistical network analysis
Discriminative probabilistic models for relational data
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
Link prediction applied to an open large-scale online social network
Proceedings of the 21st ACM conference on Hypertext and hypermedia
Social action tracking via noise tolerant time-varying factor graphs
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Mining topic-level influence in heterogeneous networks
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
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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.