GraphEmbedding is a Python library that implements graph embedding algorithms.
The tool addresses the problem of learning low-dimensional vector representations for nodes in networks. It provides implementations of five established algorithms: DeepWalk, which uses random walks and skip-gram models; LINE, which preserves both first-order and second-order proximity; Node2Vec, which extends random walks with biased sampling; SDNE, which uses deep autoencoders to capture nonlinear structure; and Struc2Vec, which learns representations based on structural roles rather than proximity. The library follows a simple design principle of graph-in, embedding-out, accepting input as edge lists via NetworkX graph objects.
The tool suits researchers and practitioners working with network analysis who want to experiment with multiple embedding approaches without implementing algorithms from scratch. It is particularly useful for comparative studies across different embedding methods. The library does not position itself against alternatives in the README, so no comparative guidance is available.
The project shows consistent maintenance with regular updates to the codebase. Documentation is provided through detailed explanatory notes accompanying each algorithm implementation. The repository includes example scripts demonstrating how to run each embedding method, making it accessible for users new to graph embedding techniques.
