Fully Exploiting Cascade Graphs for Real-time Forwarding Prediction

Authors

  • Xiangyun Tang School of Cyberspace Security, Beijing Institute of Technology, China
  • Dongliang Liao Data Quality Team, WeChat, Tencent Inc., China
  • Weijie Huang Data Quality Team, WeChat, Tencent Inc., China
  • Jin Xu Data Quality Team, WeChat, Tencent Inc., China
  • Liehuang Zhu School of Cyberspace Security, Beijing Institute of Technology, China
  • Meng Shen School of Cyberspace Security, Beijing Institute of Technology, China Cyberspace Security Research Center, Peng Cheng Laboratory, China

Keywords:

Social Networks, Graph-based Machine Learning, Time-Series/Data Streams, Recommender Systems & Collaborative Filtering

Abstract

Real-time forwarding prediction for predicting online contents' popularity is beneficial to various social applications for enhancing interactive social behaviors. Cascade graphs, formed by online contents' propagation, play a vital role in real-time forwarding prediction. Existing cascade graph modeling methods are inadequate to embed cascade graphs that have hub structures and deep cascade paths, or they fail to handle the short-term outbreak of forwarding amount. To this end, we propose a novel real-time forwarding prediction method that includes an effective approach for cascade graph embedding and a short-term variation sensitive method for time-series modeling, making the best of cascade graph features. Using two real world datasets, we demonstrate the significant superiority of the proposed method compared with the state-of-the-art. Our experiments also reveal interesting implications hidden in the performance differences between cascade graph embedding and time-series modeling.

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Published

2021-05-18

How to Cite

Tang, X., Liao, D., Huang, W., Xu, J., Zhu, L., & Shen, M. (2021). Fully Exploiting Cascade Graphs for Real-time Forwarding Prediction. Proceedings of the AAAI Conference on Artificial Intelligence, 35(1), 582-590. Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/16137

Issue

Section

AAAI Technical Track on Application Domains