Random walk closeness centrality

About: Random walk closeness centrality is a research topic. Over the lifetime, 547 publications have been published within this topic receiving 57559 citations.

Peer Effects and Social Networks in Education

Abstract: This paper studies whether structural properties of friendship networks affect individual outcomes in education. We first develop a model that shows that, at the Nash equilibrium, the outcome of each individual embedded in a network is proportional to her Katz-Bonacich centrality measure. This measure takes into account both direct and indirect friends of each individual but puts less weight to her distant friends. We then bring the model to the data by using a very detailed dataset of adolescent friendship networks. We show that, after controlling for observable individual characteristics and unobservable network specific factors, the individual's position in a network (as measured by her Katz-Bonacich centrality) is a key determinant of her level of activity. A standard deviation increase in the Katz-Bonacich centrality increases the pupil school performance by more than 7 percent of one standard deviation.

A measure of centrality based on network efficiency

Abstract: We introduce delta centralities, a new class of measures of structural centrality for networks. In particular, we focus on a measure in this class, the information centrality C I , which is based on the concept of efficient propagation of information over the network. C I is defined for both valued and non-valued graphs, and applies to groups as well as individuals. The measure is illustrated and compared with respect to the standard centrality measures by using a classic network data set. The statistical distribution of information centrality is investigated by considering large computer generated graphs and two networks from the real world.

Centrality Estimation in Large Networks

Abstract: Centrality indices are an essential concept in network analysis. For those based on shortest-path distances the computation is at least quadratic in the number of nodes, since it usually involves solving the single-source shortest-paths (SSSP) problem from every node. Therefore, exact computation is infeasible for many large networks of interest today. Centrality scores can be estimated, however, from a limited number of SSSP computations. We present results from an experimental study of the quality of such estimates under various selection strategies for the source vertices.

Centrality measures based on current flow

Abstract: We consider variations of two well-known centrality measures, betweenness and closeness, with a different model of information spread. Rather than along shortest paths only, it is assumed that information spreads efficiently like an electrical current. We prove that the current-flow variant of closeness centrality is identical with another known measure, information centrality, and give improved algorithms for computing both measures exactly. Since running times and space requirements are prohibitive for large networks, we also present a randomized approximation scheme for current-flow betweenness.