Katz centrality

About: Katz centrality is a research topic. Over the lifetime, 601 publications have been published within this topic receiving 77858 citations.

Degree centrality, eigenvector centrality and the relation between them in Twitter

Abstract: In Social Media the directed links formed between the users, are used for the transfer of information. Based on previous research, the rate of information transfer in a social network depends on the strength of connections of the user in the network, which is measured by the centrality value. In this paper, based on data collected from Twitter, we perform an analysis of eigenvector centrality approach of finding the influential users. We investigate the variation in indegree and eigenvector centrality of users participating in a hashtag in Twitter, with respect to change in the amount of interactions. Here interactions are: tweets, mentions and replies. We also investigate the relationship between indegree and eigenvector centrality in a given hashtag. We make the following interesting observations. First, in Twitter, users with high eigenvector centrality need not be influential users. Second, in a given hashtag, there is an increase in users with both high indegree and eigenvector centrality when there are more user interactions. Here interactions are: tweets, mentions and replies, indicating both indegree and eigenvector centrality should be considered when finding influential users. Third, there is a positive correlation between indegree and eigenvector centrality.

A centrality measure for communication ability in weighted network

Abstract: This paper proposes a new node centrality measurement in a weighted network, the communication centrality, which is inspired by Hirsch’s h -index. We investigated the properties of the communication centrality, and proved that the distribution of the communication centrality has the power-law upper tail in weighted scale-free networks. Relevant measures for node and network are discussed as extensions. A case study of a scientific collaboration network indicates that the communication centrality is different from other common centrality measures and other h -type indexes. Communication centrality displays moderate correlation with other indexes, and contains a well-balanced mix of other centrality measures and cannot be replaced by any of them.

Computing Influential Nodes Using the Nearest Neighborhood Trust Value and PageRank in Complex Networks

Abstract: Computing influential nodes gets a lot of attention from many researchers for information spreading in complex networks. It has vast applications, such as viral marketing, social leader creation, rumor control, and opinion monitoring. The information-spreading ability of influential nodes is greater compared with other nodes in the network. Several researchers proposed centrality measures to compute the influential nodes in a complex network, such as degree, betweenness, closeness, semi-local centralities, and PageRank. These centrality methods are defined based on the local and/or global information of nodes in the network. However, due to their high time complexity, centrality measures based on the global information of nodes have become unsuitable for large-scale networks. Very few centrality measures exist that are based on the attributes between nodes and the structure of the network. We propose the nearest neighborhood trust PageRank (NTPR) based on the structural attributes of neighbors and nearest neighbors of nodes. We define the measure based on the degree ratio, the similarity between nodes, the trust values of neighbors, and the nearest neighbors. We computed the influential nodes in various real-world networks using the proposed centrality method. We found the maximum influence by using influential nodes with SIR and independent cascade methods. We also compare the maximum influence of our centrality measure with the existing basic centrality measures.

Efficient Algorithm for Destabilization of Terrorist Networks

Abstract: The advisory feasibility of Social Network Analysis (SNA) to study social networks have encouraged the law enforcement and security agencies to investigate the terrorist network and its behavior along with key players hidden in the web. The study of the terrorist network, utilizing SNA approach and Graph Theory where the network is visualized as a graph, is termed as Investigative Data Mining or in general Terrorist Network Mining. The SNA defined centrality measures have been successfully incorporated in the destabilization of terrorist network by deterring the dominating role(s) from the network. The destabilizing of the terrorist group involves uncovering of network behavior through the defined hierarchy of algorithms. This paper concerning the destabilization of terrorist network proposes a pioneer algorithm which seems to replace the already available hierarchy of algorithms. This paper also suggests use of the two influential centralities, PageRank Centrality and Katz Centrality, for effectively neutralizing of the network.

Local Community Detection in Dynamic Graphs Using Personalized Centrality

Abstract: Analyzing massive graphs poses challenges due to the vast amount of data available. Extracting smaller relevant subgraphs allows for further visualization and analysis that would otherwise be too computationally intensive. Furthermore, many real data sets are constantly changing, and require algorithms to update as the graph evolves. This work addresses the topic of local community detection, or seed set expansion, using personalized centrality measures, specifically PageRank and Katz centrality. We present a method to efficiently update local communities in dynamic graphs. By updating the personalized ranking vectors, we can incrementally update the corresponding local community. Applying our methods to real-world graphs, we are able to obtain speedups of up to 60× compared to static recomputation while maintaining an average recall of 0.94 of the highly ranked vertices returned. Next, we investigate how approximations of a centrality vector affect the resulting local community. Specifically, our method guarantees that the vertices returned in the community are the highly ranked vertices from a personalized centrality metric.