> ML_ARCHITECTURE // GRAPH-CONVOLUTIONAL-NETWORK-GCN_v1.0
Graph Convolutional Network (GCN / GAT / GraphSAGE)
Message-passing neural network iteratively aggregating localized neighbor representations across graph topologies with permutation equivariance, foundational for fraud detection, social networks, and biology.
Graph Neural Networksgraph
Back to All ArchitecturesArchitecture Overview
Message-passing neural network iteratively aggregating localized neighbor representations across graph topologies with permutation equivariance, foundational for fraud detection, social networks, and biology.
Implementing Libraries
PyTorchLinux Foundation / PyTorch Foundation · v2.4.1
View SpecSeminal Papers
Semi-Supervised Classification with Graph Convolutional Networks (GCN)Thomas N. Kipf, Max Welling (2017) · International Conference on Learning Representations (ICLR)
Graph Attention Networks (GAT)Petar Veličković, Guillem Cucurull (2018) · International Conference on Learning Representations (ICLR)
Inductive Representation Learning on Large Graphs (GraphSAGE)William L. Hamilton, Rex Ying (2017) · Advances in Neural Information Processing Systems (NeurIPS)
Architectural Limitations & Constraints
- Requires compatible deep learning framework and hardware acceleration for efficient execution.