Learning to Rank Nodes in Temporal Graphs
About TGNet
Node ranking in temporal networks are often impacted by heterogeneous context from node content, temporal, and structural di- mensions. This project introduces TGNet, a deep learning framework for node ranking in heterogeneous temporal graphs. TGNet utilizes a variant of Recurrent Neural Network to adapt context evolution and extract context features for nodes. It incorporates a novel influence network to dynamically estimate temporal and structural influence among nodes over time. To cope with label sparsity, it integrates graph smoothness constraints as a weak form of supervision. We show that the application of TGNet is feasible for large-scale networks by developing efficient learning and inference algorithms with optimization techniques. Using real-life data, we experimentally verify the effectiveness and efficiency of TGNet techniques. We also show that TGNet yields intuitive explanations for applications such as alert detection and academic impact rank- ing, as verified by our case study.
People
Qi Song, Yinghui Wu
Publications
- TGNet: Learning to Rank Nodes in Temporal Graphs. [Paper][Slide]
ACM International Conference on Information and Knowledge Management(CIKM), 2018.
Qi Song, Bo Zong, Yinghui Wu, Lu-An Tang, Hui Zhang, Guofei Jiang and Haifeng Chen
Questions?
Acknowledgements
This project was done when Qi was an intern at NEC Labs America and is supported in part by NSF IIS-1633629.