{"type":"ScholarlyArticle","title":"Scaling Fine-grained Modularity Clustering for Massive Graphs","authors":[{"name":"Hiroaki Shiokawa","slug":"hiroaki-shiokawa","given":"Hiroaki","family":"Shiokawa","affiliation":"Center for Computational Sciences , University of Tsukuba , Japan"},{"name":"Toshiyuki Amagasa","slug":"toshiyuki-amagasa","given":"Toshiyuki","family":"Amagasa","affiliation":"Center for Computational Sciences , University of Tsukuba , Japan"},{"name":"Hiroyuki Kitagawa","slug":"hiroyuki-kitagawa","given":"Hiroyuki","family":"Kitagawa","affiliation":"Center for Computational Sciences , University of Tsukuba , Japan"}],"abstract":"Modularity clustering is an essential tool to understand complicated graphs. However, existing methods are not applicable to massive graphs due to two serious weaknesses. (1) It is difficult to fully reproduce ground-truth clusters due to the resolution limit problem. (2) They are computationally expensive because all nodes and edges must be computed iteratively. This paper proposes gScarf, which outputs fine-grained clusters within a short running time. To overcome the aforementioned weaknesses, gScarf dynamically prunes unnecessary nodes and edges, ensuring that it captures fine-grained clusters. Experiments show that gScarf outperforms existing methods in terms of running time while finding clusters with high accuracy.","doi":"10.24963/ijcai.2019/639","url":"https://ijcai.org/proceedings/2019/639","pdf":"https://ijcai.org/proceedings/2019/0639.pdf","markdown":"https://ijcai.org/proceedings/2019/639.md","bibtex":"https://ijcai.org/proceedings/2019/bibtex/639","volume":{"slug":"2019","title":"Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2019","url":"https://ijcai.org/proceedings/2019/","year":2019,"location":"Macao","dates":"10-16 August 2019","editors":"Sarit Kraus","isbn":"978-0-9992411-4-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2019"},"section":"Machine Learning Applications","track":"Main track","number":639,"pages":{"first":4597,"last":4604},"keywords":["Machine Learning Applications: Networks","Machine Learning Applications: Big data ; Scalability","Machine Learning: Clustering"],"videos":[],"references":[{"position":1,"raw":"[Blondel et al., 2008] V.D. 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