|Title:||Predicting future participants of information propagation trees||Authors:||Huang, Hen Hsen
Chung, Hsing Huan
Chen, Hsin Hsi
|Keywords:||Graph convolutional network | Information diffusion | Social media||Issue Date:||14-Oct-2019||Source:||Proceedings - 2019 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2019||Abstract:||
© 2019 Association for Computing Machinery. Understanding how information propagates among social media users can allow researchers to provide interesting insights into online social networks and lead to applications such as precise advertising and misinformation management. In this work, we focus on information diffusion through post sharing. Given an information propagation tree, our goal is to predict a list of potential users of the tree. A framework based on graph convolutional network (GCN) is proposed to learn the latent representation of a propagation tree and match it with the latent representation of a user. A novel strategy for tree pruning is further investigated to improve the GCN. Experimental results show that our framework outperforms the existing methods for modeling information diffusion.
|Appears in Collections:||資訊工程學系|
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