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Identifying overlapping communities in networks using evolutionary method

机译:使用进化方法识别网络中的重叠社区

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Community structure is a typical property of real-world networks, and has been recognized as a key to understand the dynamics of the networked systems. In most of the networks overwhelming nodes apparently live in a community while there often exists a few nodes straddling several communities. Hence, an ideal algorithm for community detection is that which can identify the overlapping communities in these networks. We present an evolutionary method for detecting overlapping community structure in the network. To represent an overlapping division of a network, we develop an encoding scheme composed of two segments, the first one represents a disjoint partition and the second one represents an extension of the partition that allows of multiple memberships. We give two measures for the informativeness of a node, and present a coevolutionary scheme between two segments over the population for solving the overlapping partition of the network. Experimental results show this method can give a better solution to a network. It is also revealed that a best overlapping partition of the network might not be rooted from a best disjoint partition. (C) 2015 Elsevier B.V. All rights reserved.
机译:社区结构是现实世界网络的典型属性,并且已被认为是理解网络系统动态的关键。在大多数网络中,压倒性的节点显然生活在一个社区中,而通常存在跨几个社区的几个节点。因此,用于社区检测的理想算法是可以识别这些网络中重叠社区的算法。我们提出了一种进化的方法来检测网络中重叠的社区结构。为了表示网络的重叠划分,我们开发了一种由两部分组成的编码方案,第一个代表不相交的分区,第二个代表允许多个成员资格的分区的扩展。我们针对节点的信息量提供了两种措施,并提出了总体上两个网段之间的协同进化方案,以解决网络的重叠分区。实验结果表明,该方法可以为网络提供更好的解决方案。还揭示出,网络的最佳重叠分区可能不会源自最佳不相交分区。 (C)2015 Elsevier B.V.保留所有权利。

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