Fully Exploiting Cascade Graphs for Real-time Forwarding Prediction
Keywords:Social Networks, Graph-based Machine Learning, Time-Series/Data Streams, Recommender Systems & Collaborative Filtering
AbstractReal-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.
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. https://doi.org/10.1609/aaai.v35i1.16137
AAAI Technical Track on Application Domains