easy-graph/Easy-Graph

What it solves

EasyGraph provides a high-performance toolkit for analyzing complex networks and hypergraphs. It addresses the need for efficient computation of network metrics and the ability to handle higher-order relationships (hypergraphs) that standard graph libraries may not support effectively, especially when dealing with large-scale datasets.

How it works

The library is structured into four main components:

• EasyGraph (Core): A Python-based library with key components implemented in C++ and multiprocessing for high performance. It supports directed and undirected networks.
• EasyHypergraph: An integrated module specifically for hypergraph computation and learning, bridging the gap between standard networks and higher-order relationships.
• EGGPU: A CUDA-native implementation that accelerates network analysis tasks (like betweenness centrality and shortest paths) using GPUs.
• EasyGNN: A component dedicated to graph neural networks (GNNs) for machine learning on graphs.

Who it’s for

It is designed for researchers and developers in interdisciplinary fields who need to perform network analysis, community detection, or hypergraph learning on large-scale data.

Highlights

• GPU Acceleration: Native CUDA implementation via EGGPU for significant speedups on large networks.
• Hypergraph Support: Dedicated tools for higher-order network analysis and learning via EasyHypergraph.
• Broad Algorithm Suite: Includes community detection, structural hole spanner detection, network embedding, and motif detection.
• Cross-Platform Performance: Combines Python's ease of use with C++ and OpenMP for optimized execution.