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GeniePath: Graph Neural Networks with Adaptive Receptive Paths
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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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Haihong, E., Zeng, D., & Song, M. (2019) · International Conference on Computational Intelligence and Intelligent Systems (CIIS) · doi:10.1145/3372422.3372442
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Duan, J., Xu, H., Lin, X., Zhu, S., & Du, Y. (2020) · Image and Vision Computing · doi:10.1016/j.imavis.2020.103988
Qiu, J. (2020) · The Sydney eScholarship Repository (The University of Sydney)
EasyGML: A Fully-functional and Easy-to-use Platform for Industrial Graph Machine Learning
Zhang, Z.-Q., Zhou, J., & Shi, C. (2020) · International Conference on Information and Knowledge Management · doi:10.1145/3340531.3417423
Self-citationGraph Neural Network with Automorphic Equivalence Filters.Preprint
Xu, F., Yao, Q., Hui, P., & Li, Y. (2020) · arXiv (Cornell University)
Ma, Y., & Tang, J. (2021) · Cambridge University Press eBooks · doi:10.1017/9781108924184.002
Ma, Y., & Tang, J. (2021) · Cambridge University Press eBooks · doi:10.1017/9781108924184.001
Advanced Applications in Graph Neural Networks
Ma, Y., & Tang, J. (2021) · Cambridge University Press eBooks · doi:10.1017/9781108924184.021
Ma, Y., & Tang, J. (2021) · Cambridge University Press eBooks · doi:10.1017/9781108924184.014
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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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