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GeniePath: graph neural networks with adaptive receptive paths

AUTHORs:

Yuan QiAuthors Info & Claims

AAAI'19/IAAI'19/EAAI'19: Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence and Thirty-First Innovative Applications of Artificial Intelligence Conference and Ninth AAAI Symposium on Educational Advances in Artificial Intelligence

Article No.: 543, Pages 4424 - 4431

https://doi.org/10.1609/aaai.v33i01.33014424

Published: 27 January 2019 Publication History

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Abstract

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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Zhang SChen XXiong YWu XZhang YFu YZhao YZhang JBaeza-Yates RBonchi F(2024)Towards Adaptive Neighborhood for Advancing Temporal Interaction Graph ModelingProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671877(4290-4301)Online publication date: 25-Aug-2024
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cover image Guide Proceedings

AAAI'19/IAAI'19/EAAI'19: Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence and Thirty-First Innovative Applications of Artificial Intelligence Conference and Ninth AAAI Symposium on Educational Advances in Artificial Intelligence

January 2019

10088 pages

ISBN:978-1-57735-809-1

Copyright © 2019 Association for the Advancement of Artificial Intelligence.

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Association for the Advancement of Artificial Intelligence

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AAAI Press

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Published: 27 January 2019

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Cited By

Zhang SChen XXiong YWu XZhang YFu YZhao YZhang JBaeza-Yates RBonchi F(2024)Towards Adaptive Neighborhood for Advancing Temporal Interaction Graph ModelingProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671877(4290-4301)Online publication date: 25-Aug-2024
https://dl.acm.org/doi/10.1145/3637528.3671877
Li KYang TZhou MMeng JWang SWu YTan BSong HPan LYu FSheng ZTong YBaeza-Yates RBonchi F(2024)SEFraud: Graph-based Self-Explainable Fraud Detection via Interpretative Mask LearningProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671534(5329-5338)Online publication date: 25-Aug-2024
https://dl.acm.org/doi/10.1145/3637528.3671534
Xiao FCai SChen GJagadish HOoi BZhang MBaeza-Yates RBonchi F(2024)VecAug: Unveiling Camouflaged Frauds with Cohort Augmentation for Enhanced DetectionProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671527(6025-6036)Online publication date: 25-Aug-2024
https://dl.acm.org/doi/10.1145/3637528.3671527
Hu BWang WWang SLiu ZShen BHe YChen JSerra ESpezzano F(2024)The Devil is in the Sources! Knowledge Enhanced Cross-Domain Recommendation in an Information Bottleneck PerspectiveProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3679595(880-889)Online publication date: 21-Oct-2024
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Zheng XWu BLiang XLi WChua TNgo CKa-Wei Lee RKumar RLauw H(2024)Friend or Foe? Mining Suspicious Behavior via Graph Capsule Infomax Detector against FraudstersProceedings of the ACM Web Conference 202410.1145/3589334.3645706(2684-2693)Online publication date: 13-May-2024
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Tang JHua FGao ZZhao PLi JOh ANaumann TGloberson ASaenko KHardt MLevine S(2023)GADBenchProceedings of the 37th International Conference on Neural Information Processing Systems10.5555/3666122.3667411(29628-29653)Online publication date: 10-Dec-2023
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Yang LWang JZhang THe DWang CGuo YCao XNiu BWang ZEl Saddik AMei TCucchiara RBertini MTobon Vallejo DAtrey PHossain M(2023)Long Short-Term Graph Memory Against Class-imbalanced Over-smoothingProceedings of the 31st ACM International Conference on Multimedia10.1145/3581783.3612566(2955-2963)Online publication date: 26-Oct-2023
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Wang ZWei ZLi YKuang WDing BZhang ARangwala H(2022)Graph Neural Networks with Node-wise ArchitectureProceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3534678.3539387(1949-1958)Online publication date: 14-Aug-2022
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