{"type":"ScholarlyArticle","title":"Optimal Region Search with Submodular Maximization","authors":[{"name":"Xuefeng Chen","slug":"xuefeng-chen","given":"Xuefeng","family":"Chen","affiliation":"School of Computer Science and Engineering , University of New South Wales , Australia,"},{"name":"Xin Cao","slug":"xin-cao","given":"Xin","family":"Cao","affiliation":"School of Computer Science and Engineering , University of New South Wales , Australia,"},{"name":"Yifeng Zeng","slug":"yifeng-zeng","given":"Yifeng","family":"Zeng","affiliation":"Department of Computer & Information Sciences , Northumbria University , UK,"},{"name":"Yixiang Fang","slug":"yixiang-fang","given":"Yixiang","family":"Fang","affiliation":"School of Computer Science and Engineering , University of New South Wales , Australia,"},{"name":"Bin Yao","slug":"bin-yao","given":"Bin","family":"Yao","affiliation":"Department of Computer Science and Engineering , Shanghai Jiaotong University , China,"}],"abstract":"Region search is an important problem in location-based services due to its wide applications. In this paper, we study the problem of optimal region search with submodular maximization (ORS-SM). This problem considers a region as a connected subgraph. We compute an objective value over the locations in the region using a submodular function and a budget value by summing up the costs of edges in the region, and aim to search the region with the largest objective score under a budget value constraint. ORS-SM supports many applications such as the most diversified region search. We prove that the problem is NP-hard and develop two approximation algorithms with guaranteed error bounds. We conduct experiments on two applications using three real-world datasets. The results demonstrate that our algorithms can achieve high-quality solutions and are faster than a state-of-the-art method by orders of magnitude.","doi":"10.24963/ijcai.2020/169","url":"https://ijcai.org/proceedings/2020/169","pdf":"https://ijcai.org/proceedings/2020/0169.pdf","markdown":"https://ijcai.org/proceedings/2020/169.md","bibtex":"https://ijcai.org/proceedings/2020/bibtex/169","volume":{"slug":"2020","title":"Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2020","url":"https://ijcai.org/proceedings/2020/","year":2020,"location":"Yokohama","dates":"January 2021 (postponed from 11-17 July 2020)","editors":"Christian Bessiere","isbn":"978-0-9992411-6-5","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2020"},"section":"Data Mining","track":"Main track","number":169,"pages":{"first":1216,"last":1222},"keywords":["Data Mining: Mining Spatial, Temporal Data","Heuristic Search and Game Playing: Combinatorial Search and Optimisation"],"videos":[{"label":"Video","url":"https://videos.ijcai20.org/25400/file_25400.m3u8","length_sec":281,"provider":"hls"}],"references":[{"position":1,"raw":"[Cao et al., 2014] Xin Cao, Gao Cong, Christian S Jensen, and Man Lung Yiu. 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