Prompt Learning on Temporal Interaction Graphs
Xi Chen, Siwei Zhang, Yun Xiong, Xixi Wu, Jiawei Zhang, Xiangguo Sun,, Yao Zhang, Feng Zhao, Yulin Kang

TL;DR
This paper introduces TIGPrompt, a novel prompt-based framework for temporal interaction graphs that addresses temporal and semantic gaps in pre-training, achieving state-of-the-art performance with high efficiency.
Contribution
The paper proposes TIGPrompt, a versatile, temporally-aware prompting framework for TIGs, with minimal supervision and flexible pre-train, prompt-based fine-tuning paradigms.
Findings
TIGPrompt achieves state-of-the-art results on multiple TIG benchmarks.
The framework demonstrates significant efficiency improvements over existing models.
Extensive experiments validate the effectiveness of temporally-aware prompts.
Abstract
Temporal Interaction Graphs (TIGs) are widely utilized to represent real-world systems. To facilitate representation learning on TIGs, researchers have proposed a series of TIG models. However, these models are still facing two tough gaps between the pre-training and downstream predictions in their ``pre-train, predict'' training paradigm. First, the temporal discrepancy between the pre-training and inference data severely undermines the models' applicability in distant future predictions on the dynamically evolving data. Second, the semantic divergence between pretext and downstream tasks hinders their practical applications, as they struggle to align with their learning and prediction capabilities across application scenarios. Recently, the ``pre-train, prompt'' paradigm has emerged as a lightweight mechanism for model generalization. Applying this paradigm is a potential solution…
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Taxonomy
TopicsTopic Modeling · Advanced Graph Neural Networks
MethodsALIGN
