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A GitHub collection of graph classification papers with reference implementations, covering embedding, deep learning, graph kernel and factorization methods. It also links to graph classification benchmark datasets and similar curated collections.
Little Ball of Fur is a graph sampling extension library for Python and a graph sampling extension library for NetworKit and NetworkX. It consists of methods that can sample from graph structured data, covering vertex, edge, and exploration sampling techniques with a unified application public interface.
LynxKite is a GPU-accelerated platform for graph analytics and drug discovery. It combines graph reasoning, machine learning and molecule analysis in one workflow, and offers 600+ graph operations with 2D and 3D molecule visualization. It is free and open source with a commercial tier for production teams.
Gadfly is a Julia package for creating statistical graphics. Developed by GiovineItalia, it provides a grammar of graphics for generating visualizations in Julia. The repository includes documentation, examples, and installation instructions, and is open source.
Graph layout algorithms in pure Julia.
Graphlit is a context layer for AI agents. It provides real-time synchronization across services including Slack, GitHub, and Jira, operates via a single API with no operations overhead, and includes built-in semantic search.
GraphPipe is a protocol and collection of software designed to simplify machine learning model deployment. It decouples deployment from framework-specific model implementations, allowing models to be served in a consistent manner across different environments.
MatlabBGL is a Matlab package for working with graphs that uses the Boost Graph Library to implement graph algorithms. It is designed for large sparse graphs with hundreds of thousands of nodes and was written by David Gleich. The page lists version 4.0 as the latest release, with downloads hosted on the Matlab File Exchange.
nngraph is an open source GitHub repository that provides graph computation capabilities for Torch's nn neural network package. It enables users to define and work with neural network graph structures for their machine learning projects.
ONNX-MLIR provides compiler technology that transforms a valid Open Neural Network Exchange (ONNX) graph into code implementing the graph with minimum runtime support. It is based on LLVM/MLIR compiler technology and contributes an ONNX Dialect and a driver that lower ONNX graphs into MLIR files or LLVM bytecodes.
Teaching page by John M. Zelle, Ph.D., for an introductory computer science textbook written in Python and published by Franklin, Beedle & Associates. It links edition-specific public resources for four editions and offers graphics.py, a simple graphics library released under the GPL for introducing object-oriented concepts. Instructor resources including problem solutions and exam questions are available from the publisher.
TensorFlow Fold is a library for creating TensorFlow models that consume structured data, where the computation graph depends on the structure of the input data. It implements dynamic batching, transforming batches of arbitrarily shaped computation graphs into a static computation graph. TensorFlow Fold is not an official Google product.
AgentPilot is a workflow automation platform for creating, organizing and executing AI workflows, ranging from a single LLM to complex AI-driven workflows. It offers an interface for designing workflows and chatting with them in real time, with branching chats and graph workflows supporting parallel execution. Workflows can be scheduled using natural language expressions of time.
Agents is an open-source framework for data-centric, self-evolving autonomous language agents. It introduces agent symbolic learning, which treats an agent pipeline like a computational graph and uses prompt-based loss, back-propagation and weight updates to train agents. The project also supports optimizing multi-agent systems and was updated to Agents 2.0 with agent learning and evaluation support.
Aito.ai is a predictive database that delivers instant, calibrated predictions from a user's live data through single queries. It supports use cases including predictive accounting, ERP, e-commerce, and application functionality, with capabilities for predictions, recommendations, classification, smart search, knowledge graphs, and AI agents. It offers a free tier for development and production licensing scaled to usage.
AIToolbox is a toolbox of AI modules written in Swift, covering graphs and trees, support vector machines, neural networks, PCA, K-Means and genetic algorithms. The framework uses the Accelerate library to speed up computations, except the Linux package versions. A Swift Package is available for Mac and Linux, described as a sub-set of the full framework.
anneal is a machine learning compiler written in Go. It is a from-scratch port of tinygrad, and its features include a graph-rewrite intermediate representation, autodiff implemented as a compiler pass, and a zero-CGO WebGPU backend.
AnyChart is a JavaScript charting library with an API and documentation. It offers a large number of chart types, and the site lists related products for stock charts, HTML5 maps and Gantt charts. Version 9.0.0 added chart types including arc diagram, dependency wheel, org chart, stream graph, waffle chart, lollipop and dumbbell. The library is described as working with any development stack.
Apache Hama is a retired Apache Software Foundation Top-Level Project established in 2012. It is a framework for Big Data analytics that uses the Bulk Synchronous Parallel (BSP) computing model. It also provides vertex-centric and neuron-centric programming models inspired by Google's Pregel and DistBelief. Its components include core BSP, Apache MRQL for query processing, a graph computing package, and Apache Horn for deep learning, and it integrates with Hadoop HDFS.
Apache MXNet is a deep learning framework mixing symbolic and imperative programming. It includes a dynamic dependency scheduler that parallelizes symbolic and imperative operations, with a graph optimization layer for symbolic execution. The repository was archived on Nov 17, 2023, and is read-only.
AutoML framework & toolkit for machine learning on graphs.
Organizes AI tools and resources so people can browse and compare relevant services.
A curated list of fraud detection papers from conferences including AAAI, CIKM, ICML, IJCAI, KDD, ICDM, and WWW. It also references similar collections about graph classification and other papers with implementations.
A curated list of Monte Carlo tree search papers with implementations, organized by conference and journal sources including NeurIPS, ICML, CVPR, ACL, AAAI, and others. The repository also links to similar collections covering graph classification, gradient boosting, and fraud detection papers.
