Learning Across Temporal Graph Representation Silos

Abstract:Temporal graph data are recorded in different representations, but whether these differences also determine the scope of learned knowledge reuse remains unclear. This study investigates whether predictive structure learned from relational histories can be reused between continuous-time dynamic graphs (CTDGs) and temporal knowledge graphs (TKGs). Bidirectional reuse emerges while retaining distinct input and prediction interfaces and independent vocabularies, and sources with the same representation are not always more useful. Controlled reorganizations of source histories show that the organization contributing to reuse varies across source-target conditions. We show that learned aspects of Relational Dynamics, the predictive structure present in relational histories, can be reused across conventional representation boundaries. Independently pretrained TKG and CTDG learners can also be fused at the score level to improve prediction on a third dataset without entity or relation-semantic alignment. Two proofs of concept in recommendation and security detection illustrate that external temporal sources without direct alignment can also become useful pretraining resources.

Machine Learning (cs.LG); Artificial Intelligence (cs.AI)

arXiv:2610.19576 [cs.LG]

(or arXiv:2610.19576v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2610.19576 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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