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Detecting Link Communities in Massive Networks

机译:在大规模网络中检测链接社区

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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:现有的大多数文献都完全集中在大型网络中的群集节点上。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,该算法基于边缘标记的思想来检测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得网络的节点分区。之后,我们可以通过节点的标签在线性时间内找到链接社区。它的时间复杂度接近线性,空间复杂度是线性的。通过在许多计算机生成的人工图和真实世界的网络上进行的广泛实验,证明了我们算法的有效性。结果表明,该算法速度快,可靠性高。与新提出的链路分区算法相比,在真实和人工网络上的测试也给出了出色的结果。

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