Vendredi 01 avril 2016 à 11h, salle 24-25/405
Networks have become ubiquitous as data from diverse disciplines can naturally be mapped to graph structures. The problem of extracting meaningful information from large scale graph data in an efficient and effective way has become crucial and challenging with several important applications and towards this end, graph mining and analysis methods constitute prominent tools. In this talk, I will present part of my work that builts upon computationally efficient graph mining methods in order to: (i) design models for analyzing the structure and dynamics of real-world networks towards unraveling properties that can further be used in practical applications; (ii) develop algorithmic tools for large-scale analytics on data with inherent (e.g., social networks) or without inherent (e.g., text) graph structure. Our approaches rely on the concepts of graph degeneracy and core decomposition in graphs. In particular, for the former point I will show how to model the engagement dynamics of large social networks and how to assess their vulnerability with respect to user departures from the network. In both cases, by unraveling the dynamics of real social networks, regularities and patterns about their structure and formation can be identified; such knowledge can further be used in various applications including churn prediction and anomaly detection. For the latter, I will present a core decomposition-based approach for locating influential nodes in complex networks, with direct applications to epidemic control and viral marketing.