14 Best Python Graph Libraries in 2024

Python Graph library offers features for creating, analyzing, and manipulating networks and graphs. Graph libraries offer tools for adding or removing edges or nodes, finding cycles, and computing shortest paths. These libraries offer tools for visualizing networks and graphs. It will have customizable styling options for edges and nodes.   

Graph libraries offer various algorithms to analyze graphs. It will help find communities, detect cliques, and calculate centrality measures. Many graph libraries are designed to work with other Python libraries for machine learning and data analysis. Graph libraries support various types like undirected, weighted, bipartite, and directed graphs. It can be easily extended to support custom graph algorithms or types. They are designed with optimized data structures and algorithms for large-scale graph analysis. These libraries help in visualizing and analyzing complex data structures. They help with transportation, biological, and social networks.   

Here are the 14 best Python Graph Libraries handpicked to help developers: 

algorithms: 

• Is a Python library that offers a collection of data structures and algorithms.  
• Will help solve common problems in mathematics and computer science.   
• Is a comprehensive library that includes implementations of algorithms. 
• Includes dynamic programming, search, graph traversal, and sorting.  
• Are implemented with efficiency in mind using optimization methods for ensuring fast performance.  

networkx: 

• Is a Python library to create, analyze, and manipulate networks and graphs.  
• Offers a simple and intuitive interface to work with graphs, including various graph algorithms.  
• Offers functions to create and manipulate graphs, like adding or removing edges or nodes.  
• Offers computing graph properties like centrality, shortest path lengths, and degree. 

gcn: 

• Is a deep learning framework for graph data that is used for applications. 
• Supports applications like link prediction, community detection, and node classification.  
• Offers implementation of GCN like DGL (Deep Graph Library), Spektral, and PyTorch Geometric.  
• Offers an efficient implementation of GCN using GPU acceleration. 

darkflow: 

• Is a Python which offers a seamless interface to the Darknet Neural Network framework. 
• Is a popular open source neural network framework for recognition tasks and object detection.  
• Allows users to use the functionality of Darknet within Python, making it more accessible for a wider user range.   
• Offers object detection capabilities to detect and localize objects within videos and images.   

pygcn: 

• Is a Python library for Graph Convolutional Networks, a type of neural network designed for graph data.  
• Offers various graph convolutional layers like Chebyshv, GraphSAGE, and GCN layers.  
• Offers the ability to customize the convolutional graph layers. 
• Customizes the number of filters, activation functions, and layers. 
• Offers an easy-to-use API to load and preprocess graph data like node features and adjacency matrices. 

GraphEmbedding: 

• Is a Python library for graph embedding to represent graph data in a low-dimensional vector space. 
• Is a useful method for many graph-based machine learning tasks like link prediction and node classification. 
• Offers a range of graph embedding algorithms like LINE, GraphSAGE, DeepWalk, and node2vec. 
• Offers the ability to customize random walk length, number of negative lengths, and embedding dimensions. 

gprof2dot: 

• Offers a graphical visualization of profiling data generated by the GNU Profiler.  
• Generates statistical profiling information about a program’s execution.  
• Includes information about the time spent in every function and the number of times every function is called.   
• Offers the ability to customize the layout of the call graph, including the node colors, edge styles, and positions.   

rdflib: 

• Is a Python library for working with Resource Description Framework RDF data.  
• Offers a way to describe and link data across various applications and domains. 
• Includes parsing RDF data from various serialization formats like Turtle, N-Triples, and RDF/XML. 
• Supports the SPARQL query language, allowing you to query RDF data and retrieve subsets of the data. 

pygraphistry: 

• Is a Python library for visualizing large-scale graph data. 
• Offers a way to transform and visualize graph data in a browser-based interface.  
• Offers visualization options, including matrix views, scatter plots, and node-link diagrams.  
• Allows interactive exploration with features like panning, filtering, and zooming. 

asciichart: 

• Is a Python library for creating ASCII plots and charts.  
• Offers an easy-to-use API to generate simple plots and charts in a console or terminal with various chart types. 
• Supports various chart types like a bar, scatter plots, and line charts.   
• Offers the ability to customize the chart’s appearance, including size, font, and color. 

PGL: 

• Is a Python library for building and training graph neural networks. 
• Offers an easy-to-use API to work with large-scale graph data and implement state-of-the-art GNN models. 
• Supports both supervised and unsupervised learning tasks.  
• Supports GPU acceleration for efficient interface and training on large-scale graph data. 

Graphormer: 

• Helps build and train GNNs, which use transformers to process graph data.  
• Offers a framework to train GNN models which use transformers to process graph data.  
• Implements GNN models which use the transformer architecture like Graph-T5 and Graphormer.  
• Allowing easy integration with other deep and machine learning tools. 

python-o365: 

• Is a Python wrapper for the Microsoft Graph API to interact with different Microsoft services. 
• Supports OneDrive, Microsoft Teams, Office 365, and SharePoint. 
• Supports authentication methods like Microsoft’s own authentication schema and OAuth2.  
• Is a useful tool for developers who want to interact with different Microsoft services. 

GEM: 

• Is the process of representing nodes in a graph as low-dimensional vectors in a continuous vector space.  
• Is useful for many applications like node classification, graph visualization, and link prediction. 
• Supports various graph embedding methods like DeepWalk, LINE, and Node2vec. 
• Allows users to customize various parameters, like the number of walks, walk length, and dimensions.