A Template for Parallelizing the Louvain Method for Modularity Maximization
نویسندگان
چکیده
Detecting communities using modularity maximization is an important operation in network analysis. As the size of the networks increase to petascales, it is important to design parallel algorithms to handle the large-scale data. In this paper we introduce a shared memory (OpenMP-based) implementation of the Louvain method, one of the most popular algorithms for maximizing modularity. We discuss the challenges in parallelizing this algorithm as well as metrics for evaluating the correctness of the results. Our results demonstrate that our implementation is highly scalable. We conclude with a discussion on how our template can be extended to time-varying networks.
منابع مشابه
Áñôöóúúòò Øøø Äóùúò Ððóööøøñ Óö Óññùòòøý Øø Blockinøøóò Ûûøø Ñóùððööøý Ññüüññþþøøóò
Abstra t. This paper presents an enhan ement of the well-known Louvain algorithm for ommunity dete tion with modularity maximization whi h was introdu ed in [16℄. The Louvain algorithm is a partial multilevel method whi h applies the vertex mover heuristi to a series of oarsened graphs. The Louvain+ algorithm proposed in this paper generalizes the Louvain algorithm by in luding a un oarsening p...
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