Temporal Katz Centrality Estimation with Temporal Graph Neural Networks

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Zhengyi Yang

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Abstract

Temporal Katz Centrality (TKC) measures node importance by aggregating time-decayed contributions from all temporal walks, emphasizing recent interactions. This enables TKC to capture the evolving influence of vertices in dynamic networks, making it a valuable tool for ranking and identifying key entities. However, computing TKC is computationally intensive due to the need to traverse all time-respecting paths. To address this challenge, we propose a temporal graph neural network-based framework. Our model utilizes a temporal graph neural network to learn node representations. To enhance efficiency, it adopts a degree-based temporal neighbor sampling strategy, selectively targeting key temporal neighbors to effectively reduce computation. Additionally, a fused long short-term memory (LSTM) module is integrated into the framework to aggregate temporal neighbor information, mimicking the accumulation of weighted temporal walk contributions. Experimental results on six real-world datasets demonstrate the effectiveness and efficiency of the proposed method.

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@inproceedings{zhang2025temporal,
  title = {Temporal Katz Centrality Estimation with Temporal Graph Neural Networks},
  author = {Zhang, Heqi and Zhang, Tianming and Yang, Zhengyi and Wang, Weiyuan and Ju, Mingchen and Wen, Dong and Cao, Bin},
  editor = {Yoshikawa, Masatoshi and Meng, Xiaofeng and Cao, Yang and Xiao, Chuan and Chen, Weitong and Wang, Yanda},
  booktitle = {Advanced Data Mining and Applications},
  year = {2026},
  publisher = {Springer Nature Singapore},
  address = {Singapore},
  pages = {231--239},
  isbn = {978-981-95-3462-3}
}