We propose two approximation algorithms for identifying communities in dynamic social networks. Communities are intuitively characterized as "unusually densely knit" sub...
Understanding how the structure of a network evolves over time is one of the most interesting and complex topics in the field of social networks. In our attempt to model the dynam...
Often the selfish and strong are believed to be favored by natural selection, even though cooperative interactions thrive at all levels of organization in living systems. Recent em...
Jorge M. Pacheco, Tom Lenaerts, Francisco C. Santo...
Abstract: Structure learning of dynamic Bayesian networks provide a principled mechanism for identifying conditional dependencies in time-series data. This learning procedure assum...
Graph-theoretic abstractions are extensively used to analyze massive data sets. Temporal data streams from socioeconomic interactions, social networking web sites, communication t...