Topic
Betweenness centrality
About: Betweenness centrality is a research topic. Over the lifetime, 4114 publications have been published within this topic receiving 147091 citations.
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TL;DR: A key claim made in this paper is that centrality measures can be regarded as generating expected values for certain kinds of node outcomes given implicit models of how traffic flows, and that this provides a new and useful way of thinking about centrality.
2,834 citations
TL;DR: A novel Cytoscape plugin cytoHubba is introduced for ranking nodes in a network by their network features and the new proposed method, MCC, has a better performance on the precision of predicting essential proteins from the yeast PPI network.
Abstract: Network is a useful way for presenting many types of biological data including protein-protein interactions, gene regulations, cellular pathways, and signal transductions. We can measure nodes by their network features to infer their importance in the network, and it can help us identify central elements of biological networks. We introduce a novel Cytoscape plugin cytoHubba for ranking nodes in a network by their network features. CytoHubba provides 11 topological analysis methods including Degree, Edge Percolated Component, Maximum Neighborhood Component, Density of Maximum Neighborhood Component, Maximal Clique Centrality and six centralities (Bottleneck, EcCentricity, Closeness, Radiality, Betweenness, and Stress) based on shortest paths. Among the eleven methods, the new proposed method, MCC, has a better performance on the precision of predicting essential proteins from the yeast PPI network. CytoHubba provide a user-friendly interface to explore important nodes in biological networks. It computes all eleven methods in one stop shopping way. Besides, researchers are able to combine cytoHubba with and other plugins into a novel analysis scheme. The network and sub-networks caught by this topological analysis strategy will lead to new insights on essential regulatory networks and protein drug targets for experimental biologists. According to cytoscape plugin download statistics, the accumulated number of cytoHubba is around 6,700 times since 2010.
2,726 citations
TL;DR: This paper proposes generalizations that combine tie strength and node centrality, and illustrates the benefits of this approach by applying one of them to Freeman’s EIES dataset.
2,713 citations
TL;DR: In this article, the authors constructed networks of collaboration between scientists in each of these disciplines and proposed a measure of collaboration strength based on the number of papers coauthored by pairs of scientists, and the number other scientists with whom they coauthored those papers.
Abstract: Using computer databases of scientific papers in physics, biomedical research, and computer science, we have constructed networks of collaboration between scientists in each of these disciplines. In these networks two scientists are considered connected if they have coauthored one or more papers together. Here we study a variety of nonlocal statistics for these networks, such as typical distances between scientists through the network, and measures of centrality such as closeness and betweenness. We further argue that simple networks such as these cannot capture variation in the strength of collaborative ties and propose a measure of collaboration strength based on the number of papers coauthored by pairs of scientists, and the number of other scientists with whom they coauthored those papers.
2,528 citations
TL;DR: In this paper, the authors propose a measure of betweenness based on random walks, counting how often a node is traversed by a random walk between two other nodes, not just the shortest paths.
2,261 citations