Cloud Service Community Detection for Real World Service Networks Based on Parallel Graph Computing

Yu Lei, Philip S. Yu
2019 IEEE Access  
Heterogeneous information networks (e.g. cloud service relation networks and social networks), where multiple-typed objects are interconnected, can be structured by big graphs. A major challenge for clustering in such big graphs is the complex structures that can generate different results, carrying many diverse semantic meanings. In order to generate desired clustering, we propose a parallel clustering method for the heterogeneous information net-works on an efficient graph computation system
more » ... Spark). We use a multi-relation and path-based method to create similarity matrices, and implement our method based on graph computation model. It is inefficient to directly use existing data-parallel tools (e.g. Hadoop) for graph computation tasks, and some graph-parallel tools (e.g. Pregel) do not effectively address the challenges of graph construction and transformation. Therefore, we implemented our parallel method on the Spark system. The experiment results of clustering show our method is more accuracy. INDEX TERMS Heterogeneous information networks, cluster, parallel computing, service mashup, community detection. VOLUME 7, 2019 This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see http://creativecommons.org/licenses/by/4.0/
doi:10.1109/access.2019.2910804 fatcat:4itx266235bavpmirqg55ipigi