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On the modularity improvement for community detection in overlapping social networks

机译:重叠社交网络中社区检测的模块化改进

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In recent years, community detection in overlapping weighted network became a research challenge. In real networks, a node can belong to two or more communities. Therefore, in this paper, we aim to address the above-mentioned problem by proposing a method to improve the modularity in overlapping weighted networks. The proposed method is based on optimizing a fitness function and fuzzy belonging degree of nodes. Experimental results on real networks, confirm a significant improvement of modularity in comparison with a similar algorithm.
机译:近年来,重叠加权网络中的社区检测已成为研究的挑战。在实际网络中,一个节点可以属于两个或多个社区。因此,在本文中,我们旨在通过提出一种改善重叠加权网络中的模块化的方法来解决上述问题。所提出的方法是基于对节点的适应度函数和模糊归属度的优化。实际网络上的实验结果证实,与类似算法相比,模块化显着提高。

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