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Detecting Link Communities in Massive Networks

机译:检测大规模网络中的链接社区

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摘要

Most of the existing literature which has entirely focused on clustering nodes in large-scale networks. To discover multi-scale overlapping communities quickly, we propose a highly efficient multi-resolution link community detection algorithm to detect the link communities in massive networks based on the idea of edge labeling. First, we will get the node partition of the network based on a new multi-resolution node detection algorithm. After that, we can find the link community in a linear time by the labels of nodes. Its time complexity is near linear and its space complexity is linear. The effectiveness of our algorithm is demonstrated by extensive experiments on lots of computer generated artificial graphs and real-world networks. The results show that our algorithm is very fast and highly reliable. Tests on real and artificial networks also give excellent results comparing with the newly proposed link partition algorithm.
机译:大多数现有文献完全专注于大型网络中的聚类节点。为了快速发现多尺度重叠社区,我们提出了一种高效的多分辨率链接社区检测算法,以检测基于边缘标签的思想的大规模网络中的链接社区。首先,我们将基于新的多分辨率节点检测算法获取网络的节点分区。之后,我们可以通过节点标签在线性时间内找到链接社区。其时间复杂性接近线性,其空间复杂性是线性的。我们的算法的有效性是通过大量计算机生成的人工图和现实网络的实验来证明。结果表明,我们的算法非常快,非常可靠。与新增的链接分区算法相比,实际网络的测试还提供了优异的结果。

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