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In this paper scalable and parallelized method for cluster analysis based on random walks is presented. The aim of the algorithm introduced in this paper is to detect dense sub graphs (clusters) and sparse sub graphs (bridges) which are responsible for information spreading among found clusters. The algorithm is sensitive to vertices assignment uncertainty. It distinguishes groups of nodes which form...
We propose a flow propagation algorithm (FlowPro) that finds the community surrounding a node in a complex network. In each iteration of the main process of FlowPro, the initial node propagates a flow that is shared among its neighbors. Each node is able to store, propagate to its neighbors, and return, part of the flow it receives to the initial node. When the algorithm converges, the flow stored...
The role division on social networks is an important technology, for its potential values in analyzing social communities, exploring information dissemination rules and improving directional marketing. This paper is focused on the research of role division from the perspective of information dissemination. We propose to measure users' secondary ability of information dissemination by weighting their...
We introduce a method for extracting the social network structure for the persons appearing in a set of video clips. Individuals are unknown, and are not matched against known enrollments. An identity cluster representing an individual is formed by grouping similar-appearing faces from different videos. Each identity cluster is represented by a node in the social network. Two nodes are linked if the...
This paper extends our previous effort in employing transitivity attributes of graphs for social network analysis. Specifically, here we focus on the problem of network community detection. We propose spectral analysis of the transitivity gradient matrix and compare our framework to the modularity based community detection that attracted many network researchers' attention recently. Previously, we...
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