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• W3175173046 abstract "Community structure detection is one of the fundamental problems in complex network analysis towards understanding the topology structure and function of the network. Modularity is a criterion to evaluate the quality of community structures, and optimization of this quality function over the possible divisions of a network is a sensitive detection method for community structure. However, the direct application of this method is computationally costly. Nonnegative matrix factorization (NMF) is a widely used method for community detection. In this paper, we show that modularity maximization can be approximately reformulated under the framework of NMF with Frobenius norm, especially when [Formula: see text] is large. A new algorithm for detecting community structure is proposed based on the above finding. The new method is compared with four state-of-the-art methods on both synthetic and real-world networks, showing its higher clustering quality over the existing methods." @default.
• W3175173046 created "2021-07-05" @default.
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• W3175173046 date "2021-06-17" @default.
• W3175173046 modified "2023-09-25" @default.
• W3175173046 title "Optimizing modularity with nonnegative matrix factorization" @default.
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• W3175173046 doi "https://doi.org/10.1142/s0129183121501424" @default.
• W3175173046 hasPublicationYear "2021" @default.
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