Fully Exploiting Cascade Graphs for Real-time Forwarding Prediction

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.

Authors

• Xiangyun Tang
• Dongliang Liao
• Weijie Huang Fujian Key Laboratory of Sensing and Computing for Smart Cities, Xiamen University, China Key Laboratory of Multimedia Trusted Perception and Efficient Computing, Ministry of Education of China, Xiamen University, China,
• Jin Xu Pazhou Lab, Guangzhou South China University of Technology
• Liehuang Zhu Beijing Institute of Technology
• Meng Shen School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China

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