{"type":"ScholarlyArticle","title":"Multi-scale Information Diffusion Prediction with Reinforced Recurrent Networks","authors":[{"name":"Cheng Yang","slug":"cheng-yang","given":"Cheng","family":"Yang","affiliation":"Department of Computer Science and Technology , Tsinghua University , Beijing , China; Institute for Artificial Intelligence , Tsinghua University , Beijing , China; State Key Lab on Intelligent Technology and Systems , Tsinghua University , Beijing , China"},{"name":"Jian Tang","slug":"jian-tang","given":"Jian","family":"Tang","affiliation":"Mila-Quebec Institute for Learning Algorithms , Canada; HEC Montréal , Canada; Canadian Institute for Advanced Research (CIFAR)"},{"name":"Maosong Sun","slug":"maosong-sun","given":"Maosong","family":"Sun","affiliation":"Department of Computer Science and Technology , Tsinghua University , Beijing , China; Institute for Artificial Intelligence , Tsinghua University , Beijing , China; State Key Lab on Intelligent Technology and Systems , Tsinghua University , Beijing , China"},{"name":"Ganqu Cui","slug":"ganqu-cui","given":"Ganqu","family":"Cui","affiliation":"Department of Computer Science and Technology , Tsinghua University , Beijing , China; Institute for Artificial Intelligence , Tsinghua University , Beijing , China; State Key Lab on Intelligent Technology and Systems , Tsinghua University , Beijing , China"},{"name":"Zhiyuan Liu","slug":"zhiyuan-liu","given":"Zhiyuan","family":"Liu","affiliation":"Department of Computer Science and Technology , Tsinghua University , Beijing , China; Institute for Artificial Intelligence , Tsinghua University , Beijing , China; State Key Lab on Intelligent Technology and Systems , Tsinghua University , Beijing , China"}],"abstract":"Information diffusion prediction is an important task which studies how information items spread among users. With the success of deep learning techniques, recurrent neural networks (RNNs) have shown their powerful capability in modeling information diffusion as sequential data. However, previous works focused on either microscopic diffusion prediction which aims at guessing the next influenced user or macroscopic diffusion prediction which estimates the total numbers of influenced users during the diffusion process. To the best of our knowledge, no previous works have suggested a unified model for both microscopic and macroscopic scales. In this paper, we propose a novel multi-scale diffusion prediction model based on reinforcement learning (RL). RL incorporates the macroscopic diffusion size information into the RNN-based microscopic diffusion model by addressing the non-differentiable problem. We also employ an effective structural context extraction strategy to utilize the underlying social graph information. Experimental results show that our proposed model outperforms state-of-the-art baseline models on both microscopic and macroscopic diffusion predictions on three real-world datasets.","doi":"10.24963/ijcai.2019/560","url":"https://ijcai.org/proceedings/2019/560","pdf":"https://ijcai.org/proceedings/2019/0560.pdf","markdown":"https://ijcai.org/proceedings/2019/560.md","bibtex":"https://ijcai.org/proceedings/2019/bibtex/560","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 M-Z","track":"Main track","number":560,"pages":{"first":4033,"last":4039},"keywords":["Machine Learning: Transfer, Adaptation, Multi-task Learning","Machine Learning: Deep Learning","Humans and AI: Personalization and User Modeling"],"videos":[],"references":[{"position":1,"raw":"[Aral and Walker, 2012] Sinan Aral and Dylan Walker. 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