Community detection is a fundamental problem in social network analysis consisting,
roughly speaking, in unsupervised dividing social actors (modeled as nodes in a social …

In this paper, we propose a novel approach to the analysis of collaborative learning. The
approach posits that different dimensions of collaborative learning emerging from social ties …

The flow of information through active users in online social networks (OSNs) plays a major
role in forming natural social groups, popularly known as communities. Although structural …

Graph clustering, also known as community detection, is a long-standing problem in data
mining. However, with the proliferation of rich attribute information available for objects in …

Clustering a graph, ie, assigning its nodes to groups, is an important operation whose best
known application is the discovery of communities in social networks. Graph clustering and …

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Community detection is an important research area in social networks analysis where we
are concerned with discovering the structure of the social network. Detecting communities is …

With the recent surge of location-based social networks (LBSNs), such as Foursquare and
Facebook Places, huge digital footprints of people's locations, profiles, and online social …

The field of market basket analysis, the search for meaningful associations in customer
purchase data, is one of the oldest areas of data mining. The typical solution involves the …

Modularity allows to estimate the quality of a partition into communities of a graph composed
of highly inter-connected vertices. In this article, we introduce a complementary measure …