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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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Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474
Hochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318
Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946
Semi-supervised classification with graph convolutional networks
Kipf, T. N., & Welling, M. (2016) · arXiv · doi:10.48550/arxiv.1609.02907 · Cited by 24,853
Graph Attention NetworksPreprint
Veličković, P., Casanova, A., Liò, P., Cucurull, G., Romero, A., & Bengio, Y. (2017) · arXiv (Cornell University) · doi:10.17863/cam.48429 · Cited by 19,745
TensorFlow: A system for large-scale machine learningPreprint
Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., Devin, M., Ghemawat, S., Irving, G., Isard, M., et al. (2016) · arXiv (Cornell University) · doi:10.5555/3026877.3026899 · Cited by 16,548
Veličković, P., Cucurull, G., Casanova, A., Romero, A., Liò, P., & Bengio, Y. (2017) · arXiv · doi:10.48550/arxiv.1710.10903 · Cited by 12,107
node2vec: Scalable Feature Learning for Networks
Grover, A., & Leskovec, J. (2016) · Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/2939672.2939754 · Cited by 10,926
Identity mappings in deep residual networks
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46493-0_38 · Cited by 10,741
Inductive Representation Learning on Large Graphs
Hamilton, W. L., Ying, R., & Leskovec, J. (2017) · arXiv · doi:10.48550/arxiv.1706.02216 · Cited by 10,639
Research works printed but not linked: 3
- Defferrard, M.; Bresson, X.; and Vandergheynst, P. 2016. Convolutional neural networks on graphs with fast localized spectral filtering. In Advances in Neural Information Processing Systems , 3844-3852.
- Liu, Z.; Chen, C.; Zhou, J.; Li, X.; Xu, F.; Chen, T.; and Song, L. 2017. Poster: Neural network-based graph embedding for malicious accounts detection. In Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security , CCS '17, 2543-2545. New York, NY, USA: ACM.
- Nvidia, C. 2008. Cublas library. NVIDIA Corporation, Santa Clara, California 15(27):31.
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Privacy-Preserving Graph Neural Network for Node Classification.Preprint
Zhou, J., Chen, C., Zheng, L., Zheng, X., Wu, B., Liu, Z., & Wang, L. (2020) · arXiv (Cornell University) · Cited by 27
Self-citationSEFraud: Graph-based Self-Explainable Fraud Detection via Interpretative Mask Learning
Li, K. C., Yang, T., Zhou, M., Meng, J., Wang, S., Wu, Y., Tan, B., Song, H., Pan, L., Yu, F., et al. (2024) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3637528.3671534 · Cited by 37
Bin2vec: learning representations of binary executable programs for security tasks
Arakelyan, S., Arasteh, S., Hauser, C., Kline, E., & Galstyan, A. (2021) · Cybersecurity · doi:10.1186/s42400-021-00088-4 · Cited by 26
Lightweight Visual Question Answering using Scene Graphs
Nuthalapati, S. V., Chandradevan, R., Giunchiglia, E., Li, B., Kayser, M., Lukasiewicz, T., & Yang, C. (2021) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3459637.3482218 · Cited by 26
Auto-STGCN: Autonomous Spatial-Temporal Graph Convolutional Network Search
Wang, C., Zhang, K., Wang, H., & Chen, B. (2022) · ACM Transactions on Knowledge Discovery from Data · doi:10.1145/3571285 · Cited by 36
Towards Learning to Discover Money Laundering Sub-network in Massive Transaction Network
Chai, Z., Yang, Y., Dan, J., Tian, S., Meng, C., Wang, W., & Sun, Y. (2023) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v37i12.26656 · Cited by 26
Federated Graph Learning -- A Position PaperPreprint
Zhang, H., Shen, T., Wu, F. Q., Yin, M., Yang, H., & Wu, C. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2105.11099 · Cited by 71
ClusterSCL: Cluster-Aware Supervised Contrastive Learning on Graphs
Wang, Y., Zhang, J., Li, H., Dong, Y., Yin, H., Li, C., & Chen, H. (2022) · International World Wide Web Conference (WWW) · doi:10.1145/3485447.3512207 · Cited by 25
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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