Vendredi 27 novembre 2015 à 11h, salle 24-25/405
Finding dense subgraphs in large graphs is a key primitive in a variety of real-world application domains, encompassing social network analytics, event detection, biology, and finance. In most such applications, one typically aims at finding several (possibly overlapping) dense subgraphs which might correspond to communities in social networks or interesting events. In this talk we present a natural generalization of the densest subgraph problem, where the main goal is to find at most k subgraphs with maximum total aggregate density, while satisfying an upper bound on the pairwise Jaccard coefficient between the sets of nodes of the subgraphs. We will also illustrate how finding dense subgraphs can be an important subroutine for event detection in social media. Social media has great potential, as apart from the traditional media sources, many users post updates on different events. The highly dynamic nature of social networks gives the benefit of timely updates and the huge amount of content the benefit of diversity and large coverage. However, finding events presents also non-trivial challenges given the large amount of noisy and irrelevant data present in social media.