Abstract
A network community refers to a group of vertices within which the links are dense but between which they are sparse. A network community mining problem (NCMP) is the problem to find all such communities from a given network. A wide variety of applications can be formalized as NCMPs such as complex network analysis, Web pages clustering as well as image segmentation. How to solve a NCMP efficiently and accurately remains an open challenge. Distinct from other works, the paper addresses the problem from a probabilistic perspective and presents an efficient algorithm that can linearly scale to the size of networks based on a proposed Markov random walk model. The proposed algorithm is strictly tested against several benchmark networks including a semantic social network. The experimental results show its good performance with respect to both speed and accuracy.
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Yang, B., Liu, J. (2007). An Efficient Probabilistic Approach to Network Community Mining. In: Yao, J., Lingras, P., Wu, WZ., Szczuka, M., Cercone, N.J., Ślȩzak, D. (eds) Rough Sets and Knowledge Technology. RSKT 2007. Lecture Notes in Computer Science(), vol 4481. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72458-2_33
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DOI: https://doi.org/10.1007/978-3-540-72458-2_33
Publisher Name: Springer, Berlin, Heidelberg
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