seongjunyun/Graph_Transformer_Networks
Graph Transformer Networks
Graph_Transformer_Networks is a graph neural network framework implemented in PyTorch for learning structural representations and performing classification tasks on complex heterogeneous graphs and relational networks. The project provides automated preprocessing pipelines to transform raw graph datasets into standardized formats, alongside model training, forward passes, and gradient backpropagation executed through dynamic tensor operations.
The architecture incorporates self-attention mechanisms applied directly to graph structures to learn contextual representations of nodes and edges across various relational types. It constructs specialized feature matrices by traversing predefined multi-hop relational paths to capture semantic interactions, and learns latent continuous vectors for individual graph entities by aggregating neighborhood structural information through stacked transformer layers.
The capability surface covers automated graph preprocessing, heterogeneous network analysis, node classification, and graph-level classification tasks.
Features
- Self-Attention Mechanisms - Applies self-attention mechanisms directly to graph structures to learn rich contextual representations across relational types.
- Graph Transformer Frameworks - Implements a PyTorch framework for learning structural representations on complex heterogeneous graphs and networks.
- Graph Transformer Architectures - Applies self-attention mechanisms directly to graph structures to learn rich contextual representations of nodes and edges.
- PyTorch Tensor Operations - Executes model training and forward passes using PyTorch tensor operations accelerated on hardware backends.
- Neighborhood Aggregators - Aggregates neighborhood structural information through stacked transformer layers to learn latent continuous vectors for graph entities.
- Heterogeneous Graph Modeling - Applies graph transformer models to learn structural representations and perform classification tasks on heterogeneous networks.
- Graph Representation Learning - Learns latent continuous vectors for individual graph entities by aggregating neighborhood structural information.
- Meta-Path Embeddings - Constructs specialized feature matrices by traversing predefined multi-hop relational paths to capture semantic interactions within complex networks.
- Graph Dataset Pipelines - Transforms raw graph datasets into standardized formats suitable for network training using automated preprocessing pipelines.
- Relational Preprocessing - Transforms raw relational graph datasets into standardized formats using automated preprocessing pipelines.
- Tensor Computation Backends - Executes model training, forward passes, and gradient backpropagation via hardware-accelerated dynamic tensor operations.
- Graph Classification - Transforms raw relational datasets through automated preprocessing and embedding generation stages to output unified categorical predictions.
- Node Classification Models - Assigns categorical labels to individual nodes by leveraging transformer architectures to learn structural network representations.
- Heterogeneous Graph Preprocessing Pipelines - Transforms raw graph datasets into standardized formats suitable for network training using automated preprocessing pipelines.
- Multi-Hop Traversals - Traverses predefined multi-hop relational paths to capture diverse semantic interactions and construct specialized feature matrices.
Star history
How this analysis was created: This summary and feature list were written by an AI model that read the project's README and public documentation pages. Each feature links to the documentation it came from; stars, license and language come straight from the GitHub API. The model does not read the source code, and the analysis is refreshed when the project is re-analysed. Learn more on our About page.
