GraphF

GraphF is a term used in computer science to describe a family of graph-based methods and software for feature extraction from graph-structured data. It emphasizes combining traditional graph algorithms with representation learning techniques to produce features suitable for machine learning tasks. The framework models data as nodes, edges, and attributes, and can handle both homogeneous graphs and heterogeneous graphs with multiple node and edge types.

Key capabilities include graph analytics such as centrality and shortest-path computations, embedding techniques based on random walks, spectral methods, and graph neural networks. GraphF tools typically provide pipelines to transform raw graph data into learned representations suitable for downstream predictions, clustering, or anomaly detection.

Architecture and design: a core graph data model, a library of algorithms, a neural-network interface, and an application programming interface that enables integration with other ML frameworks. The design emphasizes modularity, allowing plugins for additional algorithms, data sources, and hardware backends.

Data handling and performance: supports input in common formats such as edge lists, adjacency matrices, and GraphML; supports streaming updates and incremental computations; scales to large graphs through parallel execution and GPU acceleration.

Applications span social networks, bioinformatics, transportation, and recommendation systems, including node classification, link prediction, graph-level classification, and anomaly detection. GraphF serves as a conceptual framework for researchers and engineers building graph-powered analytics pipelines and deploying graph-based learning models across diverse domains.