{"type":"ScholarlyArticle","title":"Reconstructing Diffusion Networks from Incomplete Data","authors":[{"name":"Hao Huang","slug":"hao-huang","given":"Hao","family":"Huang","affiliation":"School of Computer Science , Wuhan University , China"},{"name":"Keqi Han","slug":"keqi-han","given":"Keqi","family":"Han","affiliation":"School of Computer Science , Wuhan University , China"},{"name":"Beicheng Xu","slug":"beicheng-xu","given":"Beicheng","family":"Xu","affiliation":"School of Computer Science , Wuhan University , China"},{"name":"Ting Gan","slug":"ting-gan","given":"Ting","family":"Gan","affiliation":"School of Computer Science , Wuhan University , China"}],"abstract":"To reconstruct the topology of a diffusion network, existing approaches customarily demand not only eventual infection statuses of nodes, but also the exact times when infections occur. In real-world settings, such as the spread of epidemics, tracing the exact infection times is often infeasible; even obtaining the eventual infection statuses of all nodes is a challenging task. In this work, we study topology reconstruction of a diffusion network with incomplete observations of the node infection statuses. To this end, we iteratively infer the network topology based on observed infection statuses and estimated values for unobserved infection statuses by investigating the correlation of node infections, and learn the most probable probabilities of the infection propagations among nodes w.r.t. current inferred topology, as well as the corresponding probability distribution of each unobserved infection status, which in turn helps update the estimate of unobserved data. Extensive experimental results on both synthetic and real-world networks verify the effectiveness and efficiency of our approach.","doi":"10.24963/ijcai.2022/428","url":"https://ijcai.org/proceedings/2022/428","pdf":"https://ijcai.org/proceedings/2022/0428.pdf","markdown":"https://ijcai.org/proceedings/2022/428.md","bibtex":"https://ijcai.org/proceedings/2022/bibtex/428","volume":{"slug":"2022","title":"Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2022","url":"https://ijcai.org/proceedings/2022/","year":2022,"location":"Vienna","dates":"23-29 July 2022","editors":"Luc De Raedt","isbn":"978-1-956792-00-3","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2022"},"section":"Machine Learning","track":"Main Track","number":428,"pages":{"first":3085,"last":3091},"keywords":["Machine Learning: Relational Learning","Machine Learning: Learning Graphical Models"],"videos":[{"label":"Video #1","url":"https://slideslive.com/38986559","length_sec":74,"provider":"slideslive"},{"label":"Video #2","url":"https://slideslive.com/38984702","length_sec":350,"provider":"slideslive"}],"references":[{"position":1,"raw":"[Amin et al., 2014] Kareem Amin, Hoda Heidari, and Michael Kearns. 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