Dynamic Link Prediction for New Nodes in Temporal Graph Networks
Xiaobo Zhu Yan Wu Qinhu Zhang Zhanheng Chen Ying He
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.