Exact and Estimation of Local Edge-centric Graphlet Counts

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Nesreen K. Ahmed, Theodore L. Willke, Ryan A. Rossi ;
Proceedings of the 5th International Workshop on Big Data, Streams and Heterogeneous Source Mining: Algorithms, Systems, Programming Models and Applications at KDD 2016, PMLR 53:1-17, 2016.

Abstract

Graphlets represent small induced subgraphs and are becoming increasingly important for a variety of applications. Despite the importance of the local graphlet problem, existing work focuses mainly on counting graphlets globally over the entire graph. These global counts have been used for tasks such as graph classification as well as for understanding and summarizing the fundamental structural patterns in graphs. In contrast, this work proposes a flexible, efficient, and scalable parallel framework for the more challenging problem of counting graphlets locally for a given edge or set of edges.The local graphlet counts provide a topologically rigorous characterization of the local structure surrounding an edge. The aim of this work is to obtain the count of every graphlet of size k ∈{3, 4 } for each edge. The framework gives rise to efficient, parallel, and accurate unbiased estimation methods as well as exact graphlet algorithms for counting graphlets locally. Experiments demonstrate the effectiveness of the proposed exact and estimation methods.

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