NetworkX is a Python library for the creation, manipulation, and study of the structure, dynamics, and functions of complex networks. It offers tools for analyzing graphs, generating various types of graphs, and performing graph algorithms.

igraph is a collection of network analysis tools with interfaces for Python. It provides functions for creating, manipulating, and visualizing graphs, as well as performing graph analytics and community detection.

Graph-tool is a Python library for manipulation and statistical analysis of graphs. It offers efficient algorithms for graph operations and supports graph drawing and visualization.

StellarGraph is a library for machine learning on graph-structured data. It extends the capabilities of NetworkX and provides integration with popular machine learning frameworks like TensorFlow and Keras.

DeepGraphLibrary (DGL) is a Python library designed for deep learning on graphs and graph-structured data. It provides a flexible and efficient framework for building graph neural networks (GNNs) and performing graph analytics tasks. DGL integrates seamlessly with popular deep learning frameworks like PyTorch and TensorFlow, allowing users to leverage the power of neural networks for graph-based problems.

PyTorch Geometric (PyG) is a library for deep learning on irregular structures, including graphs and point clouds. It extends PyTorch to support graph neural networks (GNNs) and provides a variety of tools for graph representation learning, node classification, and graph classification tasks. PyG is widely used for tasks involving graph-structured data in diverse domains.