Abstract
Temporal Link Prediction (TLP), as one of the highly concerned tasks in graph mining, requires predicting the future link probability based on historical interactions. On the one hand, traditional methods based on node metrics, such as Common Neighbor, achieve satisfactory performance in the TLP task. On the other hand, node metrics overly focus on the global impact of nodes while neglecting the personalization of different node pairs, which can sometimes mislead link prediction results. However, mainstream TLP methods follow the standard paradigm of learning node embedding, entangling favorable and harmful node metric factors in the representation, reducing the model’s robustness. In this paper, we propose a plug-and-play plugin called Node Metric Disentanglement, which can apply to most TLP methods and boost their performance. It explicitly accounts for node metrics and disentangles them from the embedding representations generated by TLP methods. We adopt the attention mechanism to reasonably select information conducive to the TLP task and integrate it into the node embedding. Experiments on various state-of-the-art methods and dynamic graphs verify the effectiveness and universality of our NMD plugin.
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Acknowledgements
This work is funded by the National Key Research and Development Project (Grant No: 2022YFB2703100), the Starry Night Science Fund of Zhejiang University Shanghai Institute for Advanced Study (Grant No. SN-ZJU-SIAS-001), and the Fundamental Research Funds for the Central Universities (2021FZZX001-23, 226-2023-00048).
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Zhang, T., Zheng, T., Wan, Y., Li, Y., Huang, W. (2024). Disentangling Node Metric Factors for Temporal Link Prediction. In: Luo, B., Cheng, L., Wu, ZG., Li, H., Li, C. (eds) Neural Information Processing. ICONIP 2023. Lecture Notes in Computer Science, vol 14448. Springer, Singapore. https://doi.org/10.1007/978-981-99-8082-6_27
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DOI: https://doi.org/10.1007/978-981-99-8082-6_27
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