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Local triangle-densest subgraphs

机译:本地三角形 - 最浓度的子图

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摘要

Finding dense subgraphs in large graphs is a key primitive in a variety of real-world application domains, encompassing social network analysis, event detection, problems arising in biology and many others. Several recent works have studied some variants of the classical densest subgraph problem, considering alternative quality measures such as the average number of triangles in the subgraphs and their compactness. Those are desirable properties when the task is to find communities or interesting events in social networks. In our work, we capitalize on previous works and study a variant of the problem where we aim at finding subgraphs which are both compact and contain a large number of triangles. We provide a formal definition for our problem, while developing efficient algorithms with strong theoretical guarantees. Our experimental evaluation on large real-world networks shows the effectiveness of our approach.
机译:在大图中寻找密集子图是各种现实世界应用领域的关键原始,包括社会网络分析,事件检测,生物学中出现的问题和许多其他人。最近的几项作品研究了经典密度最大的子图问题的一些变体,考虑了替代质量措施,例如子图中的平均三角形数量及其紧凑性。当任务是在社交网络中找到社区或有趣的事件时,那些是理想的属性。在我们的工作中,我们利用以前的作品,研究了一个问题的变体,我们的目标是找到既紧凑的副本,含有大量三角形。我们为我们的问题提供了一个正式的定义,同时开发出具有强烈理论保证的高效算法。我们对大型现实网络的实验评估显示了我们方法的有效性。

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