{"type":"ScholarlyArticle","title":"Graph-based Semi-supervised Local Clustering with Few Labeled Nodes","authors":[{"name":"Zhaiming Shen","slug":"zhaiming-shen","given":"Zhaiming","family":"Shen","affiliation":"University of Georgia , Athens , GA , USA"},{"name":"Ming-Jun Lai","slug":"ming-jun-lai","given":"Ming-Jun","family":"Lai","affiliation":"University of Georgia , Athens , GA , USA"},{"name":"Sheng Li","slug":"sheng-li","given":"Sheng","family":"Li","affiliation":"University of Virginia , Charlottesville , VA , USA"}],"abstract":"Local clustering aims at extracting a local structure inside a graph without the necessity of knowing the entire graph structure. As the local structure is usually small in size compared to the entire graph, one can think of it as a compressive sensing problem where the indices of target cluster can be thought as a sparse solution to a linear system. In this paper, we apply this idea based on two pioneering works under the same framework and propose a new semi-supervised local clustering approach using only few labeled nodes. Our approach improves the existing works by making the initial cut to be the entire graph and hence overcomes a major limitation of the existing works, which is the low quality of initial cut. Extensive experimental results on various datasets demonstrate the effectiveness of our approach.","doi":"10.24963/ijcai.2023/466","url":"https://ijcai.org/proceedings/2023/466","pdf":"https://ijcai.org/proceedings/2023/0466.pdf","markdown":"https://ijcai.org/proceedings/2023/466.md","bibtex":"https://ijcai.org/proceedings/2023/bibtex/466","volume":{"slug":"2023","title":"Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2023","url":"https://ijcai.org/proceedings/2023/","year":2023,"location":"Macao, SAR","dates":"19-25 August 2023","editors":"Edith Elkind","isbn":"978-1-956792-03-4","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2023"},"section":"Machine Learning","track":"Main Track","number":466,"pages":{"first":4190,"last":4198},"keywords":["Data Mining: DM: Mining graphs","Machine Learning: ML: Clustering","Machine Learning: ML: Semi-supervised learning"],"videos":[],"references":[{"position":1,"raw":"[Abbe and Sandon, 2015] Emmanuel Abbe and Colin San- don. 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