RTG: a recursive realistic graph generator using random typing
Data Mining and Knowledge Discovery
RTG: A Recursive Realistic Graph Generator Using Random Typing
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part I
Analysis of large multi-modal social networks: patterns and a generator
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part I
Multi-scale dynamics in a massive online social network
Proceedings of the 2012 ACM conference on Internet measurement conference
Reachability analysis and modeling of dynamic event networks
ECML PKDD'12 Proceedings of the 2012 European conference on Machine Learning and Knowledge Discovery in Databases - Volume Part I
CopyCatch: stopping group attacks by spotting lockstep behavior in social networks
Proceedings of the 22nd international conference on World Wide Web
Discovering descriptive rules in relational dynamic graphs
Intelligent Data Analysis - Dynamic Networks and Knowledge Discovery
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How do real, weighted graphs change over time? What patterns, if any, do they obey? Earlier studies focus on unweighted graphs, and, with few exceptions, they focus on static snapshots. Here, we report patterns we discover on several real, weighted, time-evolving graphs. The reported patterns can help in detecting anomalies in natural graphs, in making link prediction and in providing more criteria for evaluation of synthetic graph generators. We further propose an intuitive and easy way to construct weighted, time-evolving graphs. In fact, we prove that our generator will produce graphs which obey many patterns and laws observed to date. We also provide empirical evidence to support our claims.