Learning Across Temporal Graph Representation Silos
Temporal graph data are recorded in different representations, but whether these differences also determine the scope of learned knowledge reuse remains unclear.
ProofPaper ↗
Key points
- 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.
- We show that learned aspects of Relational Dynamics, the predictive structure present in relational histories, can be reused across conventional representation boundaries.
- Two proofs of concept in recommendation and security detection illustrate that external temporal sources without direct alignment can also become useful pretraining resources.
Sources (1)
- [1]Learning Across Temporal Graph Representation SilosarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 14, 04:42 AM
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).
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