Abstract
In this paper, we give a new definition of community which is composed of two parts: community core and the periphery. Community core consists of highly densely connected nodes. And we propose LGSM (Local Greedy Search Method) for discovering community structures in social networks. LGSM sorts node according to weighted degree. For each node, LGSM derives a maximal weighted clique as a seed cluster. Then, LGSM adds new nodes into the seed cluster until the weighted edge density is smaller than the threshold value. After all community cores are detected, LGSM allots isolated nodes to the detected cores, and optimizes the community structure based on modularity. Our method is an integrative method, which is applicable not only to discovering overlapping communities, but also to discovering non-overlapping community. Experiments illustrate that LGSM can achieve good community structure on synthetic and real-world networks and the time complexity is O(|E|lg(|V|)).
This research is supported by NSFC Projects (61070011 and 61272275).
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Liu, B., Qian, T. (2013). A Local Greedy Search Method for Detecting Community Structure in Weighted Social Networks. In: Motoda, H., Wu, Z., Cao, L., Zaiane, O., Yao, M., Wang, W. (eds) Advanced Data Mining and Applications. ADMA 2013. Lecture Notes in Computer Science(), vol 8346. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-53914-5_31
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DOI: https://doi.org/10.1007/978-3-642-53914-5_31
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