The community is the dominant structure that exhibits different features and multifold functions of complex networks from different levels; accordingly, multiresolution community detection is of critical importance in network science. In this paper, inspired by the ideas of the network flow, we propose an intensity-based community detection algorithm, i.e. ICDA, to detect multiresolution communities in weighted networks. First, the edge intensity is defined to quantify the relationship between each pair of connected nodes, and the vertices connected by the edges with higher intensities are denoted as core nodes, while the others are denoted as marginal nodes. Second, by applying the expansion strategy, the algorithm merges the closely connected core nodes as the initial communities and attaches marginal nodes to the nearest initial communities. To guarantee a higher internal density for the ultimate communities, the captured communities are further adjusted according to their densities. Experimental results of real and synthetic networks illustrate that our approach has higher performance and better accuracy. Meanwhile, a multiresolution investigation of some real networks shows that the algorithm can provide hierarchical details of complex networks with different thresholds.
A community is the basic component structure of complex networks and is important for network analysis. In recent decades, researchers from different fields have witnessed a boom of community detection, and many algorithms were proposed to retrieve disjoint or overlapping communities. In this paper, a unified expansion approach is proposed to obtain two different network partitions, which can provide divisions with higher accuracies and have high scalability in large-scale networks. First, we define the edge intensity to quantify the densities of network edges, a higher edge intensity indicates a more compact pair of nodes. Second, vertices of higher density edges are extracted out and denoted as core nodes, whereas other vertices are treated as margin nodes; finally we apply an expansion strategy to form disjoint communities: closely connected core nodes are combined as disjoint skeleton communities, and margin nodes are gradually attached to the nearest skeleton communities. To detect overlapping communities, extra steps are adopted: potential overlapping nodes are identified from the existing disjoint communities and replicated; and communities that bear replicas are further partitioned into smaller clusters. Because replicas of potential overlapping nodes might remain in different communities, overlapping communities can be acquired. Experimental results on real and synthetic networks illustrate higher accuracy and better performance of our method.
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