Temporal Graph Neural Network-Powered Paper Recommendation on Dynamic Citation Networks
<|reference_start|>Temporal Graph Neural Network-Powered Paper Recommendation on Dynamic Citation Networks: Due to the rapid growth of scientific publications, identifying all related reference articles in the literature has become increasingly challenging yet highly demanding. Existing methods primarily assess candidate publications from a static perspective, focusing on the content of articles and their structural information, such as citation relationships. There is a lack of research regarding how to account for the evolving impact among papers on their embeddings. Toward this goal, this paper introduces a temporal dimension to paper recommendation strategies. The core idea is to continuously update a paper's embedding when new citation relationships appear, enhancing its relevance for future recommendations. Whenever a citation relationship is added to the literature upon the publication of a paper, the embeddings of the two related papers are updated through a Temporal Graph Neural Network (TGN). A learnable memory update module based on a Recurrent Neural Network (RNN) is utilized to study the evolution of the embedding of a paper in order to predict its reference impact in a future timestamp. Such a TGN-based model learns a pattern of how people's views of the paper may evolve, aiming to guide paper recommendations more precisely. Extensive experiments on an open citation network dataset, including 313,278 articles from https://paperswithcode.com/about PaperWithCode, have demonstrated the effectiveness of the proposed approach.<|reference_end|>
@article{shen2024temporal,
    title={Temporal Graph Neural Network-Powered Paper Recommendation on Dynamic
  Citation Networks},
    author={Junhao Shen, Mohammad Ausaf Ali Haqqani, Beichen Hu, Cheng Huang,
  Xihao Xie, Tsengdar Lee and Jia Zhang},
    journal={arXiv preprint arXiv:2408.15371},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.15371},
    primaryClass={cs.IR cs.LG}
}