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首页> 外文期刊>Internet Mathematics >FAST ALGORITHMS FOR THE MAXIMUM CLIQUE PROBLEM ON MASSIVE GRAPHS WITH APPLICATIONS TO OVERLAPPING COMMUNITY DETECTION
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FAST ALGORITHMS FOR THE MAXIMUM CLIQUE PROBLEM ON MASSIVE GRAPHS WITH APPLICATIONS TO OVERLAPPING COMMUNITY DETECTION

机译:大规模图形最大问题的快速算法及其在重叠社区检测中的应用

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摘要

The maximum clique problem is a well-known NP-hard problem with applications in data mining, network analysis, information retrieval, and many other areas related to the World Wide Web. There exist several algorithms for the problem, with acceptable runtimes for certain classes of graphs, but many of them are infeasible for massive graphs. We present a new exact algorithm that employs novel pruning techniques and is able to find maximum cliques in very large, sparse graphs quickly. Extensive experiments on different kinds of synthetic and real-world graphs show that our new algorithm can be orders of magnitude faster than existing algorithms. We also present a heuristic that runs orders of magnitude faster than the exact algorithm while providing optimal or near-optimal solutions. We illustrate a simple application of the algorithms in developing methods for detection of overlapping communities in networks.
机译:最大派系问题是一个众所周知的NP难题,它在数据挖掘,网络分析,信息检索以及许多其他与万维网有关的领域中都有应用。存在用于该问题的几种算法,对于某些类的图具有可接受的运行时,但是对于大量图而言,其中许多是不可行的。我们提出了一种新的精确算法,该算法采用了新颖的修剪技术,能够在非常大的稀疏图中快速找到最大团。在不同种类的合成图和真实图上进行的大量实验表明,我们的新算法比现有算法的速度快几个数量级。我们还提出了一种启发式算法,其运行速度比精确算法快了几个数量级,同时提供了最佳或接近最佳的解决方案。我们说明了该算法在开发用于检测网络中重叠社区的方法中的简单应用。

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