Abstract
One of the most useful measurements of community detection quality is the modularity, which evaluates how a given division deviates from an expected random graph. This article demonstrates that (i) modularity maximization can be transformed into versions of the standard minimum-cut graph partitioning, and (ii) normalized version of modularity maximization is identical to normalized cut graph partitioning. Meanwhile, we innovatively combine the modularity theory with popular statistical inference method in two aspects: (i) transforming such statistical model into null model in modularity maximization; (ii) adapting the objective function of statistical inference method for our optimization. Based on the demonstrations above, this paper proposes an efficient algorithm for community detection by adapting the Laplacian spectral partitioning algorithm. The experiments, in both real-world and synthetic networks, show that both the quality and the running time of the proposed algorithm rival the previous best algorithms.
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Wang, Y., Huang, H., Feng, C., Liu, Z. (2015). Community Detection Based on Minimum-Cut Graph Partitioning. In: Dong, X., Yu, X., Li, J., Sun, Y. (eds) Web-Age Information Management. WAIM 2015. Lecture Notes in Computer Science(), vol 9098. Springer, Cham. https://doi.org/10.1007/978-3-319-21042-1_5
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DOI: https://doi.org/10.1007/978-3-319-21042-1_5
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