Jiakui/awesome-gcn
resources for graph convolutional networks （图卷积神经网络相关资源）
This resource provides a curated collection of materials related to Graph Convolutional Networks (GCNs) and Graph Attention Networks (GATs). It gathers implementations, tutorials, and examples of how these techniques are applied across various tasks. Developers and researchers working with graph-structured data will find codebases and academic resources for building and enhancing models.
911 stars. No commits in the last 6 months.
Use this if you are a machine learning practitioner or researcher looking for existing implementations and examples of GCNs and GATs to apply to problems like text classification, recommendation systems, or knowledge graph analysis.
Not ideal if you are looking for a simple, off-the-shelf tool to solve a specific problem without needing to dive into the underlying model implementations or research.
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Jul 12, 2019
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