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arXiv · 1509.08089

Moss: A Scalable Tool for Efficiently Sampling and Counting 4- and 5-Node Graphlets

Pinghui Wang·Jing Tao·Junzhou Zhao·Xiaohong Guan
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Abstract

Counting the frequencies of 3-, 4-, and 5-node undirected motifs (also know as graphlets) is widely used for understanding complex networks such as social and biology networks. However, it is a great challenge to compute these metrics for a large graph due to the intensive computation. Despite recent efforts to count triangles (i.e., 3-node undirected motif counting), little attention has been given to developing scalable tools that can be used to characterize 4- and 5-node motifs. In this paper, we develop computational efficient methods to sample and count 4- and 5- node undirected motifs. Our methods provide unbiased estimators of motif frequencies, and we derive simple and exact formulas for the variances of the estimators. Moreover, our methods are designed to fit vertex centric programming models, so they can be easily applied to current graph computing systems such as Pregel and GraphLab. We conduct experiments on a variety of real-word datasets, and experimental results show that our methods are several orders of magnitude faster than the state-of-the-art methods under the same estimation errors.

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Pinghui Wang, Jing Tao, Junzhou Zhao, Xiaohong Guan. 2015-12-17. Moss: A Scalable Tool for Efficiently Sampling and Counting 4- and 5-Node Graphlets. https://arxiv.org/abs/1509.08089

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