Community discovery and role assignment have been recently integrated into an
unsupervised approach for the exploratory analysis of overlapping communities and inner …

With the development of W eb2. 0, social tagging systems in which users can freely choose
tags to annotate resources according to their interests have attracted much attention. In …

Hierarchical probabilistic modeling of discrete data has emerged as a powerful tool for text
analysis. Posterior inference in such models is intractable, and practitioners rely on …

In this paper we introduce a novel and efficient approach to detect topics in a large corpus of
research papers. With rapidly growing size of academic literature, the problem of topic …

The emerging trend of social information processing has resulted in Web users' increased
reliance on user-generated content contributed by others for information searching and …

This paper proposes an unsupervised method for automatic identification of spammers in a
social network. In our approach, we first investigate the link structure of the network in order …

We present a new probabilistic approach to modeling social interactions, that seamlessly
integrates community discovery and role assignment for a deeper understanding of …

In this paper, we address the problem of discovering topically meaningful, yet compact
(densely connected) communities in a social network. Assuming the social network to be an …

Community detection in semantic social networks is a crucial issue in online social network
analysis, and has received extensive attention from researchers in various fields. Different …

Criminal web data provide unknown and valuable information for Law enforcement
agencies continuously. The digital data which is applied in forensics analysis includes …