Katz centrality

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

Central Author Mining from Co-authorship Network

Abstract: Most researches on co-authorship network analyze the author's information globally according to the overall network topology structure, instead of analyzing the author's local network. Therefore, this paper presents a community mining algorithm and divides big co-authorship network into small communities, in which entities' relationship is closer. Then we mine central authors in community by three different centrality standards including closeness centrality, eigenvector centrality and a new proposed measure termed extensity degree centrality. We choose the SIGMOD data as datasets and measure the centrality from different views. And experiments in co-authorship network achieve many interesting results, which indicate our technique is efficient and feasible, and also have reference value for scientific evaluation.

Random Graph Standard Network Metrics Distributions in ORA

Abstract: : Networks, and the nodes within them, are often characterized using a series of metrics. Illustrative graph level metrics are the characteristic path length and the clustering co-efficient. Illustrative node level metrics are degree centrality, betweenness centrality, closeness centrality, and eigenvector centrality. A key issue in using these metrics is how to interpret the values; e.g., is a degree centrality of .2 high? With normalized values, we know that these metrics go between 0 and 1, and while 0 is low and 1 is high, we don't have much other interpretive information. Here we ask, are these values different than what we would expect in a random graph. We report the distributions of these metrics against the behavior of random graphs and we present the 95% most probable range for each of these metrics. We find that a normal distribution well approximating most metrics, for large slightly dense networks, and that the ranges are centered at the expected mean and the endpoints are two (sample) standard deviations apart from the center.

Dithering and betweenness centrality in weighted graphs

Abstract: This paper applies dithering to design a node centrality measure for weighted graphs. The construction is an improvement on the stable betweenness centrality measure which, in turn, was introduced as a robust alternative to the well-known betweenness centrality. We interpret any given graph as the mean representation of a distribution of graphs and define the dithered centrality value as the expected centrality value across all graphs in the distribution. We show that the dithered stable betweenness centrality measure preserves robustness in the presence of noise while improving the behavior of stable betweenness. Numerical experiments demonstrate the advantages of dithering by comparing the performance of betweenness, stable betweenness and dithered stable betweenness centralities in terms of robustness to noise, dependence on the number and quality of alternative paths, and distribution of centrality values across the graph.

Node Centrality on Individual Microblog User Network

Abstract: A following and followed directed network was established based on Sina individual microblog.By analyzing social network centrality indicators applied to the microblog directed network,such as the node degree,closeness,betweenness and K-shell,the node centrality of the network was found.The results point out the important users and their roles in dissemination of information.Besides,the characteristics of the users on the microblog network were analyzed in order to reflect the personal interests and hobbies.The correlation between the social networks index and the degree of network was studied to reflect the relationship among the indicators.The results will help us to identify individual key nodes,and then analyze the information dissemination on individual microblog user network.

Centrality for nodes in social networks

Abstract: In social networks, investigating node influence and influence maximization is an important issue and attracts great interest in the research community. In order to analyze its personal influence and potential influence, it proposes a Personal-Potential Influence(PPI)algorithm, which evaluates the weight of its k-shell, closeness centrality and betweenness centrality by considering the strength of relationship between nodes. The experimental results show that PPI has the higher accuracy in node influence and outperforms other algorithms.