Social network based applications have experienced exponential growth in recent years.
One of the reasons for this rise is that this application domain offers a particularly fertile …

The influence maximization problem has gained increasing attention in recent years.
Previous research focuses on the development of algorithms to analyze static social …

Community detection in social networks is an important field of research in data mining and
has an abundant literature. Time varying social networks require algorithms that can comply …

The expanded domain of expert system applications has risen the impact of modeling and
analysis of community evolution in social networks as an important part of the decision …

Community discovery is one of the most studied problems in network science. In recent
years, many works have focused on discovering communities in temporal networks, thus …

Graph mining is one of the significant tasks in the field of computer science. Most of the
applications generate a vast amount of data which is represented with the help of a graph …

In today's world scenario, many of the real-life problems and application data can be
represented with the help of the graphs. Nowadays technology grows day by day at a very …

Community detection is a popular approach to understand the organization of interactions in
static networks. For that purpose, the Clique Percolation Method (CPM), which involves the …

This paper presents an approach to community detection in complex networks by
simultaneously incorporating a connectivity-based metric and Max-Min Modularity. By …

Link streams offer a good model for representing interactions over time. They consist of links
$(b, e, u, v) $, where $ u $ and $ v $ are vertices interacting during the whole time interval …