Abstract
Transferable graph learning involves knowledge transferability from a source graph to a relevant target graph. The major challenge of transferable graph learning is the distribution shift between source and target graphs induced by individual node attributes and complex graph structures. To solve this problem, in this paper, we propose a generic graph-structured Gaussian process framework (GraphGP) for adaptively transferring knowledge across graphs with either homophily or heterophily assumptions. Specifically, GraphGP is derived from a novel graph structure-aware neural network in the limit on the layer width. The generalization analysis of GraphGP explicitly investigates the connection between knowledge transferability and graph domain similarity. Extensive experiments on several transferable graph learning benchmarks demonstrate the efficacy of GraphGP over state-of-the-art Gaussian process baselines.
Original language | English (US) |
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Pages (from-to) | 50879-50906 |
Number of pages | 28 |
Journal | Advances in Neural Information Processing Systems |
Volume | 36 |
State | Published - 2023 |
Event | 37th Conference on Neural Information Processing Systems, NeurIPS 2023 - New Orleans, United States Duration: Dec 10 2023 → Dec 16 2023 |
ASJC Scopus subject areas
- Computer Networks and Communications
- Information Systems
- Signal Processing