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Due to the growing presence of large-scale and streaming graphs such as social networks, graph sampling and clustering play an important role in many real-world applications. One key aspect of graph clustering is the evaluation of cluster quality. However, little attention has been paid to evaluation measures for clustering quality on samples of graphs. As first steps towards appropriate evaluation...
The conventional algorithm (COPRA -- Community Overlap PRopagation Algorithm) proposed by Steve Gregory is efficient and useful in Complex Networks, but it is a challenge to select a suitable parameter "thr" as the input of the algorithm. In this paper, we put forward a threshold based label propagation algorithm, in which each vertex in the network is identified with a threshold respectively,...
In this paper we propose two algorithms for overlapping community detection based on neighborhood vector propagation algorithm(NVPA), a community detection algorithm which can detect disjoint communities with high accuracy. The first algorithm is named Link Partition of Overlapping Communities (LPOC). In this algorithm, we first convert a node graph to a link graph, then we use NVPA to find the communities...
Community detection and influence analysis are significant notions in social networks. We exploit the implicit knowledge of influence-based connectivity and proximity encoded in the network topology, and propose a novel algorithm for both community detection and influence ranking. Using a new influence cascade model, the algorithm generates an influence vector for each node, which captures in detail...
In this paper, we propose an algorithm that detects overlapping communities in networks (graphs) based on a simple node behavior model. The key idea in the proposed algorithm is to find communities in an agglomerative manner such that every detected community S has the following property: For each node i ∈ S, we have (i) the fraction of nodes in S \ {i} that are neighbors of node i is greater than...
In study of complex networks, valuable insights can be obtained by mining structural and functional sub-units of networks, usually called communities, modules, or clusters. One of the approaches to community detection is Clique Percolation Method (CPM) which is the most popular overlapping community detection method and in recent years has been used in analysis of different kinds of networks. However,...
This paper introduces an agglomerative method for detecting cohesive subgroups in networks based on geodesic distance. The algorithm starts with a set of nodes as "seed". Beginning with the seed nodes as initial clusters, the clusters grow by incorporating more nodes successively based on minimal average distance to the current members of the cluster as a criterion for cluster extension...
Research has shown that many social networks come into being hierarchically based on some basic building blocks called communities, within which the social interactions are very intensive, but between which they are very weak. Network community mining algorithms aim at efficiently and effectively discovering all such communities from a given network. Many related methods have been proposed and applied...
This paper introduces a new dynamic neighborhood network for particle swarm optimization. In Club-based Particle Swarm Optimization (C-PSO) algorithm, each particle initially joins a default number of social groups (clubs). Each particle is affected by its own experience and the experience of the best performing member of the social groups it is a member of. In the proposed Adaptive membership C-PSO...
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