Algorithmic techniques for modeling and mining large graphs (AMAzING)

A. Frieze, A. Gionis, Charalampos E. Tsourakakis
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引用次数: 4

Abstract

Network science has emerged over the last years as an interdisciplinary area spanning traditional domains including mathematics, computer science, sociology, biology and economics. Since complexity in social, biological and economical systems, and more generally in complex systems, arises through pairwise interactions there exists a surging interest in understanding networks. In this tutorial, we will provide an in-depth presentation of the most popular random-graph models used for modeling real-world networks. We will then discuss efficient algorithmic techniques for mining large graphs, with emphasis on the problems of extracting graph sparsifiers, partitioning graphs into densely connected components, and finding dense subgraphs. We will motivate the problems we will discuss and the algorithms we will present with real-world applications. Our aim is to survey important results in the areas of modeling and mining large graphs, to uncover the intuition behind the key ideas, and to present future research directions.
建模和挖掘大型图的算法技术(AMAzING)
网络科学在过去的几年里已经成为一个跨学科的领域,跨越了数学、计算机科学、社会学、生物学和经济学等传统领域。由于社会、生物和经济系统的复杂性,以及更普遍的复杂系统的复杂性,是通过成对相互作用产生的,因此对理解网络的兴趣激增。在本教程中,我们将深入介绍用于建模现实世界网络的最流行的随机图模型。然后,我们将讨论挖掘大型图的有效算法技术,重点是提取图稀疏器,将图划分为密集连接的组件以及寻找密集子图的问题。我们将激励我们将讨论的问题和算法,我们将在现实世界的应用。我们的目标是调查大型图建模和挖掘领域的重要结果,揭示关键思想背后的直觉,并提出未来的研究方向。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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