KGSum Example

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.

KGSum Example


Qi Song, Yinghui Wu


  1. 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


Contact Qi Song


This project was done when Qi was an intern at NEC Labs America and is supported in part by NSF IIS-1633629.