MOSER: Scalable Network Motif Discovery Using Serial Test

Abstract

Given a graph G , a motif (e.g., 3-node clique) is a fundamental building block for G. Recently, motif-based graph analysis has attracted much attention due to its efficacy in tasks such as clustering, ranking, and link prediction. These tasks require Network Motif Discovery (NMD) at the early stage to identify the motifs of G. However, existing NMD solutions have two drawbacks: (1) Lack of theoretical guarantees on the quality of the samples generated using the existing methods, and (2) inefficient algorithms, which are not scalable for large graphs. These limitations hinder the exploration of motifs for analyzing large graphs. To address the above issues, we propose a novel solution named MOSER ( MO tif Discovery using SER ial Test). This novel NMD framework leverages a significance testing method known as the serial test, which differs from the existing solutions. We further propose two fast incremental subgraph counting algorithms, allowing MOSER to scale to larger graphs than ever possible before. Extensive experimental results show that using MOSER can improve the state-of-the-art up to 5 orders of magnitude in efficiency and that the motifs found by MOSER facilitate downstream tasks such as link prediction.