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An Efficient Probabilistic Approach to Network Community Mining

机译:网络社区挖掘的一种有效概率方法

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A network community refers to a group of vertices within which the links are dense but between which they are sparse. A network community mining problem (NCMP) is the problem to find all such communities from a given network. A wide variety of applications can be formalized as NCMPs such as complex network analysis, Web pages clustering as well as image segmentation. How to solve a NCMP efficiently and accurately remains an open challenge. Distinct from other works, the paper addresses the problem from a probabilistic perspective and presents an efficient algorithm that can linearly scale to the size of networks based on a proposed Markov random walk model. The proposed algorithm is strictly tested against several benchmark networks including a semantic social network. The experimental results show its good performance with respect to both speed and accuracy.
机译:网络社区是指一组顶点,其中链接密集,但链接之间稀疏。网络社区挖掘问题(NCMP)是从给定网络中查找所有此类社区的问题。可以将各种应用程序形式化为NCMP,例如复杂的网络分析,Web页面群集以及图像分割。如何有效,准确地解决NCMP问题仍然是一个挑战。与其他工作不同,本文从概率角度解决了该问题,并提出了一种有效的算法,该算法可以基于提出的马尔可夫随机游走模型线性地缩放到网络的大小。该算法针对包括语义社交网络在内的多个基准网络进行了严格测试。实验结果表明,它在速度和准确性方面均表现出色。

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