Dynamic Link Prediction for New Nodes in Temporal Graph Networks

• URL: http://arxiv.org/abs/2310.09787v1
• Date: Sun, 15 Oct 2023 09:54:18 GMT
• Title: Dynamic Link Prediction for New Nodes in Temporal Graph Networks
• Authors: Xiaobo Zhu, Yan Wu, Qinhu Zhang, Zhanheng Chen, Ying He
• Abstract summary: Modelling temporal networks for dynamic link prediction of new nodes has many real-world applications. New nodes have few historical links, which poses a challenge for the dynamic link prediction task. Most existing dynamic models treat all nodes equally and are not specialized for new nodes, resulting in suboptimal performances.
• Score: 6.13245948813717
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Modelling temporal networks for dynamic link prediction of new nodes has many real-world applications, such as providing relevant item recommendations to new customers in recommender systems and suggesting appropriate posts to new users on social platforms. Unlike old nodes, new nodes have few historical links, which poses a challenge for the dynamic link prediction task. Most existing dynamic models treat all nodes equally and are not specialized for new nodes, resulting in suboptimal performances. In this paper, we consider dynamic link prediction of new nodes as a few-shot problem and propose a novel model based on the meta-learning principle to effectively mitigate this problem. Specifically, we develop a temporal encoder with a node-level span memory to obtain a new node embedding, and then we use a predictor to determine whether the new node generates a link. To overcome the few-shot challenge, we incorporate the encoder-predictor into the meta-learning paradigm, which can learn two types of implicit information during the formation of the temporal network through span adaptation and node adaptation. The acquired implicit information can serve as model initialisation and facilitate rapid adaptation to new nodes through a fine-tuning process on just a few links. Experiments on three publicly available datasets demonstrate the superior performance of our model compared to existing state-of-the-art methods.

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