{"type":"ScholarlyArticle","title":"Fast Algorithm for K-Truss Discovery on Public-Private Graphs","authors":[{"name":"Soroush Ebadian","slug":"soroush-ebadian","given":"Soroush","family":"Ebadian","affiliation":"Sharif University of Technology; Hong Kong Baptist University"},{"name":"Xin Huang","slug":"xin-huang","given":"Xin","family":"Huang","affiliation":"Hong Kong Baptist University"}],"abstract":"In public-private graphs, users share one public graph and have their own private graphs. A private graph consists of personal private contacts that only can be visible to its owner, e.g., hidden friend lists on Facebook and secret following on Sina Weibo. However, existing public-private analytic algorithms have not yet investigated the dense subgraph discovery of k-truss, where each edge is contained in at least k-2 triangles. This paper aims at finding k-truss efficiently in public-private graphs. The core of our solution is a novel algorithm to update k-truss with node insertions. We develop a classification-based hybrid strategy of node insertions and edge insertions to incrementally compute k-truss in public-private graphs. Extensive experiments validate the superiority of our proposed algorithms against state-of-the-art methods on real-world datasets.","doi":"10.24963/ijcai.2019/313","url":"https://ijcai.org/proceedings/2019/313","pdf":"https://ijcai.org/proceedings/2019/0313.pdf","markdown":"https://ijcai.org/proceedings/2019/313.md","bibtex":"https://ijcai.org/proceedings/2019/bibtex/313","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 A-L","track":"Main track","number":313,"pages":{"first":2258,"last":2264},"keywords":["Machine Learning Applications: Networks","Heuristic Search and Game Playing: Heuristic Search","Machine Learning: Data Mining","Multidisciplinary Topics and Applications: Social Sciences"],"videos":[],"references":[{"position":1,"raw":"[Archer et al., 2017] Aaron Archer, Silvio Lattanzi, Peter Likarish, and Sergei Vassilvitskii. 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