Parallelizing and optimizing overlapping community detection with speaker-listener Label Propagation Algorithm on multi-core architecture

Abstract

Scaling up algorithms or applications in data mining studies for massive datasets to improve the computing performance is becoming crucial since we've stepped into Big Data era. For community detection, which is one of the most important topics in data mining, numerous algorithms were proposed for a better exploration of the complex networks in real world and researchers keep working on improving their computing performance by parallel programming. Speaker-listener Label Propagation Algorithm (SLPA) is a sequential linear time algorithm for overlapping community detection. In this work, we proposed a new approach to parallelize and optimize SLPA. We make it more efficient through reducing the computational complexity of one of its computing kernels and providing with a better way to get memory access during parallel execution. Evaluation of abundant experiments demonstrates our implementation's better scalability on multi-core CPUs than prior work.