This paper presents a centrality measurement and analysis of the social networks for tracking online community. The tracking of single community in social networks is commonly done using some of the centrality measures employed in social network community tracking. The ability that centrality measures have to determine the relative position of a node within a network has been used in previous r...

In many real-life networks, such as urban structures, protein interactions and social networks, one of the key issues is to measure the centrality of nodes, i.e. to determine which nodes and edges are more central to the functioning of the entire network than others. In this paper we focus on betweenness centrality — a metric based on which the centrality of a node is related to the number of s...

We analyze whether preferential attachment in scientific coauthorship networks is different for authors with different forms of centrality. Using a complete database for the scientific specialty of research about ―steel structures,‖ we show that betweenness centrality of an existing node is a significantly better predictor of preferential attachment by new entrants than degree or closeness cent...

Degree centrality is considered to be one of the most basic measures of social network analysis, which has been used extensively in diverse research domains for measuring network positions of actors in respect of the connections with their immediate neighbors. In network analysis, it emphasises the number of connections that an actor has with others. However, it does not accommodate the value o...

We study the lobby index ( l for short) as a local node centrality measure for complex networks. The l is compared with degree (a local measure), betweenness and Eigenvector centralities (two global measures) in the case of a biological network (Yeast interaction protein-protein network) and a linguistic network (Moby Thesaurus II ). In both networks, the l has poor correlation with betweenness...

Network structure analysis plays an important role in characterizing complex systems. Different from previous network centrality measures, this article proposes the topological centrality measure reflecting the topological positions of nodes and edges as well as influence between nodes and edges in general network. Experiments on different networks show distinguished features of the topological...

The betweenness and closeness metrics are widely used metrics in many network analysis applications. Yet, they are expensive to compute. For that reason, making the betweenness and closeness centrality computations faster is an important and well-studied problem. In this work, we propose the framework BADIOS which manipulates the graph by compressing it and splitting into pieces so that the cen...

Who is more important in a network? Who controls the flow between the nodes or whose contribution is significant for connections? Centrality metrics play an important role while answering these questions. The betweenness metric is useful for network analysis and implemented in various tools. Since it is one of the most computationally expensive kernels in graph mining, several techniques have b...