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This research introduces GTGIB, a novel framework that combines graph structure learning with a temporal information bottleneck to address inductive representation learning in dynamic networks. By theoretically regularizing edges and features, the method effectively handles unseen nodes and noise while outperforming existing approaches on real-world link prediction tasks. The work advances the state of the art in handling evolving graph topologies where new entities continuously emerge.
Graph Structure Learning with Temporal Graph Information Bottleneck for Inductive Representation Learning
arXiv:2508.14859v2 Announce Type: replace Abstract: Temporal graph learning is crucial for dynamic networks where nodes and edges evolve over time and new nodes continuously join the system. Inductive representation learning in such settings faces two major challenges: effectively representing unseen nodes and mitigating noisy or redundant graph information. We propose GTGIB, a versatile framework that integrates Graph Structure Learning (GSL) with Temporal Graph Information Bottleneck (TGIB). We design a novel two-step GSL-based structural enhancer to enrich and optimize node neighborhoods and demonstrate its effectiveness and efficiency through theoretical proofs and experiments. The TGIB refines the optimized graph by extending the information bottleneck principle to temporal graphs, regularizing both edges and features based on our derived tractable TGIB objective function via variational approximation, enabling stable and efficient optimization. GTGIB-based models are evaluated to predict links on four real-world datasets; they outperform existing methods in all datasets under the inductive setting, with significant and consistent improvement in the transductive setting.
Abstract:Temporal graph learning is crucial for dynamic networks where nodes and edges evolve over time and new nodes continuously join the system. Inductive representation learning in such settings faces two major challenges: effectively representing unseen nodes and mitigating noisy or redundant graph information. We propose GTGIB, a versatile framework that integrates Graph Structure Learning (GSL) with Temporal Graph Information Bottleneck (TGIB). We design a novel two-step GSL-based structural enhancer to enrich and optimize node neighborhoods and demonstrate its effectiveness and efficiency through theoretical proofs and experiments. The TGIB refines the optimized graph by extending the information bottleneck principle to temporal graphs, regularizing both edges and features based on our derived tractable TGIB objective function via variational approximation, enabling stable and efficient optimization. GTGIB-based models are evaluated to predict links on four real-world datasets; they outperform existing methods in all datasets under the inductive setting, with significant and consistent improvement in the transductive setting.
Accepted in the 28th European Conference on Artificial Intelligence (ECAI), 2025 v2: corrects typographical errors in Eqs. (9) and (13), in Section 5.1, and in Table 2 and its discussion, and the sampling configuration stated in the implementation details; revises the proofs in Appendices A.2 and B
Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
arXiv:2508.14859 [cs.LG]
(or arXiv:2508.14859v2 [cs.LG] for this version)
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