CASS: A distributed network clustering algorithm based on structure similarity for large-scale network

Jungrim Kim, Mincheol Shin, Jeongwoo Kim, Chihyun Park, Sujin Lee, Jaemin Woo, Hyerim Kim, Dongmin Seo, Seokjong Yu, Sanghyun Park

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)


As the size of networks increases, it is becoming important to analyze large-scale network data. A network clustering algorithm is useful for analysis of network data. Conventional network clustering algorithms in a single machine environment rather than a parallel machine environment are actively being researched. However, these algorithms cannot analyze large-scale network data because of memory size issues. As a solution, we propose a network clustering algorithm for large-scale network data analysis using Apache Spark by changing the paradigm of the conventional clustering algorithm to improve its efficiency in the Apache Spark environment. We also apply optimization approaches such as Bloom filter and shuffle selection to reduce memory usage and execution time. By evaluating our proposed algorithm based on an average normalized cut, we confirmed that the algorithm can analyze diverse large-scale network datasets such as biological, co-authorship, internet topology and social networks. Experimental results show that the proposed algorithm can develop more accurate clusters than comparative algorithms with less memory usage. Furthermore, we confirm the proposed optimization approaches and the scalability of the proposed algorithm.

Original languageEnglish
Article numbere0203670
JournalPloS one
Issue number10
Publication statusPublished - 2018 Oct

Bibliographical note

Publisher Copyright:
© 2018 EMH Swiss Medical Publishers Ltd. All rights reserved.

All Science Journal Classification (ASJC) codes

  • General


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