{"type":"ScholarlyArticle","title":"Efficient Estimation of Influence Functions for SIS Model on Social Networks","authors":[{"name":"Masahiro Kimura","slug":"masahiro-kimura","given":"Masahiro","family":"Kimura","affiliation":""},{"name":"Kazumi Saito","slug":"kazumi-saito","given":"Kazumi","family":"Saito","affiliation":""},{"name":"Hiroshi Motoda","slug":"hiroshi-motoda","given":"Hiroshi","family":"Motoda","affiliation":""}],"abstract":"We address the problem of efficiently estimating the influence function of initially activated nodes in a social network under the susceptible / infected / susceptible (SIS) model, a diffusion model where nodes are allowed to be activated multiple times. The computational complexity drastically increases because of this multiple activation property. We solve this problem by constructing a layered graph from the original social network with each layer added on top as the time proceeds, and applying the bond percolation with a pruning strategy. We show that the computational complexity of the proposed method is much smaller than the conventional naive probabilistic simulation method by a theoretical analysis and confirm this by applying the proposed method to two real world networks.Masahiro Kimura, Kazumi Saito, Hiroshi Motoda","doi":null,"url":"https://ijcai.org/proceedings/2009/337","pdf":"https://ijcai.org/proceedings/2009/0337.pdf","markdown":"https://ijcai.org/proceedings/2009/337.md","bibtex":"https://ijcai.org/proceedings/2009/bibtex/337","volume":{"slug":"2009","title":"Proceedings of the Twenty-First International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2009","url":"https://ijcai.org/proceedings/2009/","year":2009,"location":"Pasadena, California, USA","dates":"","editors":"Hiroaki Kitano","isbn":"","publisher":"International Joint Conferences on Artificial Intelligence","doi_prefix":""},"section":null,"track":"","number":337,"pages":{"first":2046,"last":2050},"keywords":[],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}