Large-Scale Learnable Graph Convolutional Networks
Open accessAbstract
Convolutional neural networks (CNNs) have achieved great success on grid-like data such as images, but face tremendous challenges in learning from more generic data such as graphs. In CNNs, the trainable local filters enable the automatic extraction of high-level features. The computation with filters requires a fixed number of ordered units in the receptive fields. However, the number of neighboring units is neither fixed nor are they ordered in generic graphs, thereby hindering the applications of convolutional operations. Here, we address these challenges by proposing the learnable graph convolutional layer (LGCL). LGCL automatically selects a fixed number of neighboring nodes for each feature based on value ranking in order to transform graph data into grid-like structures in 1-D format, thereby enabling the use of regular convolutional operations on generic graphs. To enable model training on large-scale graphs, we propose a sub-graph training method to reduce the excessive memory and computational resource requirements suffered by prior methods on graph convolutions. Our experimental results on node classification tasks in both transductive and inductive learning settings demonstrate that our methods can achieve consistently better performance on the Cora, Citeseer, Pubmed citation network, and protein-protein interaction network datasets. Our results also indicate that the proposed methods using sub-graph training strategy are more efficient as compared to prior approaches.
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Dynamic Dense Graph Convolutional Network for Skeleton-Based Human Motion Prediction
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FedGL: Federated graph learning framework with global self-supervision
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Representation Learning for Natural Language Processing
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Accelerating graph sampling for graph machine learning using GPUs
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K-Core Based Temporal Graph Convolutional Network for Dynamic Graphs
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Network representation learning: a systematic literature review
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Reliable Data Distillation on Graph Convolutional Network
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Graph Few-shot Learning with Attribute Matching
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Genetic-GNN: Evolutionary architecture search for Graph Neural Networks
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Adaptively Connected Neural Networks
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Self-citationGraph Representation Learning via Hard and Channel-Wise Attention Networks
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Self-citation
- Authors
- Gao, H., Wang, Z., & Ji, S.
- Affiliations
- Washington State University (US)
- Venue
- Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining
- Publisher
- ACM
- Published
- 2018-07-19
- Pages
- 1416-1424
- OpenAlex
- W2809418595
- Cited by
- 638
- References
- 9
- Citations by year
- 2026: 20 · 2025: 67 · 2024: 72 · 2023: 79 · 2022: 100 · 2021: 140 · 2020: 110 · 2019: 46 · 2018: 4
- FWCI
- 30.63
- Type
- conference paper
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Defense Advanced Research Projects Agency (N66001-17-2-4031)
- National Science Foundation (DBI-1641223, IIS-1633359)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Advanced Graph Neural Networks
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Graph Theory and Algorithms
- Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Complex Network Analysis Techniques
- Keywords
- graph convolutional networks, large-scale graphs, node classification, value ranking, memory and computational efficiency
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.
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