University of Jean-Monnet at St. Etienne, France
Title: Community detection in attributed networks
A wide variety of applications requires data modeled by a graph of interconnected nodes, known as social networks or information networks. When nodes are described by attributes, this is an attributed network. Examples of such networks are citation or collaboration networks or social media.
One fundamental property of these networks is that they tend to naturally form communities and, many mining methods have been proposed to identify these communities but these methods are typically based solely on relationships between nodes in the graph.
With the proliferation of additional information available to describe the nodes, a new challenge consists in combining the both types of data. Recently, several works have attempted to tackle this problem of hybrid clustering. We detail the main ones. We also come back on the strategies that can be used for evaluating the obtained partition.
Christine Largeron received a Ph.D in Computer Science from Claude Bernard University (Lyon – France) in 1991. She is Professor at Jean Monnet University (France) since 2006 and, she is the head of the Data Mining and Information Retrieval group of the Hubert Curien Laboratory.
Her research interests focus on machine learning, data mining, information retrieval, text mining, social mining, network analysis.