Community detection problems are one of the most important topics in social network
analysis. Most of the algorithms and techniques that find communities in a network, model …

In social network research, one of the most important analysis is community detection. Fuzzy
uncertainty appears clearly when modeling real situations by means of networks …

In this work we introduce the notion of the weighted graph associated with a fuzzy measure.
Having a finite set of elements between which there exists an affinity fuzzy relation, we …

In this paper we propose a new framework for community detection problems. The starting
point is a n-vector which defines some evidence about the elements of a finite set. This …

Community and cluster detection is a popular field of social network analysis. Most
algorithms focus on static graphs or series of snapshots. In this paper we present an …

Community detection problems are one of the hottest disciplines in social network analysis.
Nevertheless, most of the related algorithms are specific for non-directed networks, or are …

Community detection aims to partition a set of nodes with more similarities in the set than out
of it based on different criteria like neighborhood similarity or vertex connectivity. Most …

Studying behavior of systems through networks is important because it allows to understand
them and make decisions based on this knowledge. Community detection is one of the tools …

Cluster structure is one of the main features of social graphs. Many algorithms have been
proposed in recent years that are capable of revealing fuzzy communities. But a lot of them …

We consider the problem of fuzzy community detection in networks, which complements and
expands the concept of overlapping community structure. Our approach allows each vertex …