GraphNeural

GraphNeural is a software framework intended to support the development, training, and deployment of graph neural networks. It provides standardized data representations and a modular API for constructing models that operate on graph-structured data, with an emphasis on reproducibility and extensibility.

At its core, GraphNeural centers on graph data structures that include node and edge features, as well as graph-level readouts. It adopts a generic message-passing paradigm in which node representations are updated by aggregating information from neighboring nodes and edges. The framework offers a range of reusable layers, including graph convolution, attention-based mechanisms, and customizable aggregation schemes, and it supports both homogeneous and heterogeneous graphs.

Implementation-wise, GraphNeural aims for interoperability with common deep learning backends and data pipelines. It includes data loaders for standard graph datasets, support for batched graph processing, and optimization for hardware acceleration. The platform also provides training loops, evaluation utilities, and model persistence tools, with an emphasis on extensibility so researchers can define new layers, loss functions, and training protocols without extensive boilerplate.

Applications covered by GraphNeural span several domains, such as social network analysis, chemistry and materials science for molecular property prediction, transportation and traffic networks, knowledge graphs, and recommender systems. The framework is positioned as a unifying tool that helps researchers compare methods and reproduce experiments across graph-related tasks.

See also: Graph Neural Network, Graph Convolutional Network, GraphSAGE, DGL, PyTorch Geometric.