Many of today's applications can benefit from the discovery of the most central entities in real-
world networks. This paper presents a new technique that efficiently finds the k most central …

Network motifs are small over represented patterns that have been used successfully to
characterize complex networks. Current algorithmic approaches focus essentially on pure …

Graph analytics is an important big data discovery technique. Applications include
identifying influential employees for retention, detecting fraud in a complex interaction …

Fostered by Web 2.0, ubiquitous computing, and corresponding technological
advancements, social media have become massively popular during the last decade. The …

As an important metric in graphs, group closeness centrality measures how close a group of
vertices is to all other vertices in a graph, and it is used in numerous graph applications such …

Abstract Social Network Analysis is a non-conventional Data Mining technique which
analyzes social networks on web. The technique is used frequently for studying network …

Several approaches have been proposed to solve the well-studied problem of detecting
community structure in networks. However, many existing algorithms encounter difficulties …

In our paper we compare two centrality measures of networks, namely betweenness and
Linerank. Betweenness is a popular, widely used measure, however, its computation is …

Discovering topologies in a social network targets various business applications such as
finding key influencers in a network, recommending music movies in virtual communities …

Analyzing the underlying social network is very important for the development of online
applications. Owing to the increasingly growing size of these networks, parallel techniques …