{"type":"ScholarlyArticle","title":"Cascade Dynamics Modeling with Attention-based Recurrent Neural Network","authors":[{"name":"Yongqing Wang","slug":"yongqing-wang","given":"Yongqing","family":"Wang","affiliation":"CAS Key Laboratory of Network Data Science and Technology , Institute of Computing Technology , Chinese Academy of Sciences , Beijing , China; University of Chinese Academy of Sciences , Beijing , China"},{"name":"Huawei Shen","slug":"huawei-shen","given":"Huawei","family":"Shen","affiliation":"CAS Key Laboratory of Network Data Science and Technology , Institute of Computing Technology , Chinese Academy of Sciences , Beijing , China; University of Chinese Academy of Sciences , Beijing , China"},{"name":"Shenghua Liu","slug":"shenghua-liu","given":"Shenghua","family":"Liu","affiliation":"CAS Key Laboratory of Network Data Science and Technology , Institute of Computing Technology , Chinese Academy of Sciences , Beijing , China; University of Chinese Academy of Sciences , Beijing , China"},{"name":"Jinhua Gao","slug":"jinhua-gao","given":"Jinhua","family":"Gao","affiliation":"CAS Key Laboratory of Network Data Science and Technology , Institute of Computing Technology , Chinese Academy of Sciences , Beijing , China; University of Chinese Academy of Sciences , Beijing , China"},{"name":"Xueqi Cheng","slug":"xueqi-cheng","given":"Xueqi","family":"Cheng","affiliation":"CAS Key Laboratory of Network Data Science and Technology , Institute of Computing Technology , Chinese Academy of Sciences , Beijing , China; University of Chinese Academy of Sciences , Beijing , China"}],"abstract":"An ability of modeling and predicting the cascades of resharing is crucial to understanding information propagation and to launching campaign of viral marketing. Conventional methods for cascade prediction heavily depend on the hypothesis of diffusion models, e.g., independent cascade model and linear threshold model. Recently, researchers attempt to circumvent the problem of cascade prediction using sequential models (e.g., recurrent neural network, namely RNN) that do not require knowing the underlying diffusion model. Existing sequential models employ a chain structure to capture the memory effect. However, for cascade prediction, each cascade generally corresponds to a diffusion tree, causing cross-dependence in cascade---one sharing behavior could be triggered by its non-immediate predecessor in the memory chain. In this paper, we propose to an attention-based RNN to capture the cross-dependence in cascade. Furthermore, we introduce a \\emph{coverage} strategy to combat the misallocation of attention caused by the memoryless of traditional attention mechanism. Extensive experiments on both synthetic and real world datasets\r\ndemonstrate the proposed models outperform state-of-the-art models at both cascade prediction and inferring diffusion tree.","doi":"10.24963/ijcai.2017/416","url":"https://ijcai.org/proceedings/2017/416","pdf":"https://ijcai.org/proceedings/2017/0416.pdf","markdown":"https://ijcai.org/proceedings/2017/416.md","bibtex":"https://ijcai.org/proceedings/2017/bibtex/416","volume":{"slug":"2017","title":"Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2017","url":"https://ijcai.org/proceedings/2017/","year":2017,"location":"Melbourne, Australia","dates":"19-25 August 2017","editors":"Carles Sierra","isbn":"978-0-9992411-0-3","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2017"},"section":"Machine Learning S-Z","track":"Main track","number":416,"pages":{"first":2985,"last":2991},"keywords":["Machine Learning: Deep Learning","Machine Learning: Data Mining","Machine Learning: Machine Learning","Machine Learning: Time-series/Data Streams"],"videos":[],"references":[{"position":1,"raw":"[Cheng et al., 2013] Suqi Cheng, Huawei Shen, Junming Huang, Guoqing Zhang, and Xueqi Cheng. 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