| Information networks represent relations in data, relationships typically ignored in iid (independent and identically distributed) data. Such networks abound, like co-authorships in bibliometrics, cellphone call graphs in telecommunication, students interactions in Education, etc. A large body of work has been devoted to the analysis of these networks and the discovery of their underlying structure, specifically, finding the communities in them. Communities are groups of nodes in the network that are relatively cohesive within the set compared to the outside.;Moreover, it proposes Meerkat-ED, a specific and practical toolbox for analyzing students' interactions in online courses. It applies social network analysis techniques including community mining to evaluate participation of students in asynchronous discussion forums.;This thesis proposes Top Leaders, a fast and accurate community mining approach for both weighted and unweighted networks. Top Leaders regards a community as a set of followers congregating around a potential leader and works based on a novel measure of closeness inspired by the theory of diffusion of innovations. |
