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- W2897941366 abstract "Community structures in networks are helpful to understand the network structure and analyze the network properties. Majority of the studies in this area tend to discover communities by analyzing the linkages in the networks, which may result in communities with diversified attributes. In this paper, we model community detection as an optimization problem. This paper proposes a novel community detection approach by leveraging the concepts of particle swarm optimization with dynamic neighborhood topology by analyzing the similarity between the node attributes. The process starts by dividing the particles into several sub swarms, and the particles in each sub swarm iteratively form communities through information exchange and integrating the nodes which satisfy a threshold similarity score. The presented method does not require any prior knowledge about the communities. Experiments are carried out on real-life datasets, and the process is executed in parallel, asynchronous environments. We evaluated our method with the evaluation properties namely Omega index, Silhouette coefficient and Tanimoto coefficient to evaluate the performance and quality of the clusters. We also compared our approach with existing approaches, and the results demonstrate that our method performed better in terms of measured metrics." @default.
- W2897941366 created "2018-10-26" @default.
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- W2897941366 date "2018-07-01" @default.
- W2897941366 modified "2023-10-14" @default.
- W2897941366 title "A PSO Based Community Detection in Social Networks with Node Attributes" @default.
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- W2897941366 doi "https://doi.org/10.1109/cec.2018.8477659" @default.
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