Abstract
Based on scale-free and density-based complex networks and numerical clustering algorithm, a graph clustering algorithm based on fast detection of central nodes is proposed. Through the calculation of local density and comprehensive clustering of nodes in the network, the clustering center in the network can be found quickly and noncentral nodes can be divided into the clustering center according to the nearest neighbor principle, thus avoiding parameter limitations such as the number of clustering to be set in advance using conventional classic social network detection algorithm. The experimental comparison and analysis in the real network indicate that the graph clustering algorithm based on fast detection of the central node is highly effective and efficient.
Highlights
Density-based clustering algorithm is the crucial direction of clustering analysis. e classic density-based clustering algorithm is DBSCAN algorithm, which divides high-density areas into multiple clusters
The CFSDP based on density clustering algorithm proposed by Alex et al is popular with the main idea of adhering to the following two principles in the selection of clustering center: firstly, the density of the clustering center itself is greater than the density of adjacent points; secondly, the distance between the clustering center and other clustering center is relatively long
Based on CFSDP algorithm in combination with scale free of complex networks, the clustering algorithm for fast detection of central nodes proposed in this paper has the advantages of Scientific Programming high efficiency of density-based numerical clustering algorithm and no need to set clustering quantitative parameters in advance [8]
Summary
Density-based clustering algorithm is the crucial direction of clustering analysis. e classic density-based clustering algorithm is DBSCAN algorithm, which divides high-density areas into multiple clusters. According to the central idea of graph clustering algorithm based on fast detection of center node, the network is divided into k disjoint subgraphs, with dense edge connections inside and sparse edge connections between each other.
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