Detecting Communities with Different Sizes for Social Network Analysis

Detecting Overlapping Communities in Social Networks using Deep Learning

In network analysis, a community is typically considered of as a group of nodes with a great density of edges among themselves and a low density of edges relative to other network parts. Detecting a community structure is important in any network analysis task, especially for revealing patterns between specified nodes. There is a variety of approaches presented in the literature for overlapping...

Using E-Mail Social Network Analysis for Detecting Unauthorized Accounts

In this paper we detail the use of e-mail social network analysis for the detection of security policy violations on computer systems. We begin by formalizing basic policies that derive from the expected social behavior of computer users. We then extract the social networks of three organizations by analyzing e-mail server logs collected over several months and apply the policies to the resulta...

Detecting Network Communities: An Application to Phylogenetic Analysis

This paper proposes a new method to identify communities in generally weighted complex networks and apply it to phylogenetic analysis. In this case, weights correspond to the similarity indexes among protein sequences, which can be used for network construction so that the network structure can be analyzed to recover phylogenetically useful information from its properties. The analyses discusse...

Detecting communities in online social networks

A community can be defined as a subset of the users in a social network that is more tightly interconnected than the overall network. Communities are useful, for instance, to guide information dissemination and acquisition, to recommend or introduce people who would likely benefit from direct interaction, and to express access control policies. In this paper, we study algorithms for automatical...

Detecting and Tracking Communities in Social Networks