Katz centrality

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

Social Network Analysis and Time Varying Graphs

Abstract: The thesis focuses on the social web and on the analysis of social networks with particular emphasis on their temporal aspects. Social networks are represented here by Time Varying Graphs (TVG), a general model for dynamic graphs borrowed from distributed computing. In the first part of the thesis we focus on the temporal aspects of social networks. We develop various temporal centrality measures for TVGs including betweenness, closeness, and eigenvector centralities, which are well known in the context of static graphs. Unfortunately the computational complexity of these temporal centrality metrics are not comparable with their static counterparts. For example, the computation of betweenness becomes intractable in the dynamic setting. For this reason, approximation techniques will also be considered. We apply these temporal measures to two very different datasets, one in the context of knowledge mobilization in a small community of university researchers, the other in the context of Facebook commenting activities among a large number of web users. In both settings, we perform a temporal analysis so to understand the importance of the temporal factors in the dynamics of those networks and to detect nodes that act as “accelerators”. In the second part of the thesis, we focus on a more standard static graph representation. We conduct a propagation study on YouTube datasets to understand and compare the propagation dynamics of two different types of users: subscribers and friends. Finally, we conclude the thesis with the proposal of a general framework to present, in a comprehensive model, the influence of the social web on e-commerce decision making.

Improved closeness centrality using arithmetic mean approach

Abstract: In this paper, we improved the mathematical formulation of closeness centrality measure for weighted network. The proposed measure is used arithmetic mean approach and the performance is successfully better than the existing closeness centrality. This measure can be used as a measure of influential nodes.

Visualizing Workow-Supported Social Networks and Their Degree Centrality Measures

Abstract: The purpose of this paper1 is to implement a visualization framework and system for workflow-supported social networking knowledge and its analysis measures of degree centrality. The workflow-supported social networking knowledge is formally represented by the workflow-supported social network model, and it is discovered from a XPDL2-based workflow model or a group of workflow models in a workflow-supported organization. The visualization framework is pipe-lining from the XPDL-formatted workflow model to the GraphML3-formatted graph, and it is eventually implemented by adopting the open sources of information visualization toolkits, such as Prefuse, JFreeChart, and Log4j. Additionally, the system is able to visualize the degree-centrality measurements for each of the workflow performers making up of a workflow-supported social network.

Sampling Based Katz Centrality Estimation for Large-Scale Social Networks

Abstract: Katz centrality is a fundamental concept to measure the influence of a vertex in a social network. However, existing approaches to calculating Katz centrality in a large-scale network is unpractical and computationally expensive. In this paper, we propose a novel method to estimate Katz centrality based on graph sampling techniques. Specifically, we develop an unbiased estimator for Katz centrality using a multi-round sampling approach. We further propose SAKE, a Sampling based Algorithm for fast Katz centrality Estimation. We prove that the estimator calculated by SAKE is probabilistically guaranteed to be within an additive error from the exact value. The computational complexity of SAKE is much lower than the state-of-the-arts. Extensive evaluation experiments based on four real world networks show that the proposed algorithm achieves low mean relative error with low sampling rate, and it works well in identifying high influence vertices in social networks.

Dominating Centrality Set: A New Measure on the Network Coverage of Influential Center Nodes

Abstract: There is a list of traditional measures on the centrality of a network, but they may provide misleading suggestions when there are more than one center in a network. Even a sequential selection on the centers are performed, the revelation of a truly influential set of the center nodes is difficult to achieve due to the curse of double counting. In this work, we introduce a new measure on the network coverage of the influential set of center nodes via a new graph-theoretic tool called the dominating centrality set (DCS). To calculate the DCS efficiently, we derive the exact formulas for counting the number of cycles that contains a specific node. We demonstrate this method to the analysis of Amazon product network and the Web of Science citation network.