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GeniePath: Graph Neural Networks with Adaptive Receptive Paths
Open accessAbstract
We present, GeniePath, a scalable approach for learning adaptive receptive fields of neural networks defined on permutation invariant graph data. In GeniePath, we propose an adaptive path layer consists of two complementary functions designed for breadth and depth exploration respectively, where the former learns the importance of different sized neighborhoods, while the latter extracts and filters signals aggregated from neighbors of different hops away. Our method works in both transductive and inductive settings, and extensive experiments compared with competitive methods show that our approaches yield state-of-the-art results on large graphs.
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Metadata
- Authors
- Liu, Z., Chen, C., Li, L., Zhou, J., Li, X., Song, L., & Qi, Y.
- Affiliations
- Ant Group (China) (CN)
- Venue
- Proceedings of the AAAI Conference on Artificial Intelligence
- Published
- 2019-07-17
- Volume
- 33
- Issue
- 01
- Pages
- 4424-4431
- ISSN
- 2159-5399 (ISSN-L)
- 2374-3468
- OpenAlex
- W2963415211
- Cited by
- 350
- References
- 24
- Citations by year
- 2026: 13 · 2025: 34 · 2024: 49 · 2023: 50 · 2022: 48 · 2021: 90 · 2020: 49 · 2019: 16
- FWCI
- 19.42
- Type
- conference paper
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- www.aaai.org/ (unspecified)
- Locations
- Proceedings of the AAAI Conference on Artificial Intelligence (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- AAAI Publications (The Association for the Advancement of Artificial Intelligence (AAAI)) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Advanced Graph Neural Networks
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Graph Theory and Algorithms
- Physical Sciences › Computer Science › Information Systems › Recommender Systems and Techniques
- Keywords
- graph neural networks, adaptive receptive field, neighborhood aggregation, transductive learning, inductive learning, large-scale graphs
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