Search Results for "graphs" - Page 2
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Showing 1004 open source projects for "graphs"
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KG-LLM-Papers
Papers integrating knowledge graphs (KGs) and large language models
KG-LLM-Papers is a curated academic resource that collects and organizes research papers exploring the intersection between knowledge graphs and large language models. The repository functions as a continuously updated index of scholarly work that investigates how structured knowledge representations can enhance the reasoning, factual accuracy, and interpretability of language models. It includes surveys, benchmark studies, and cutting-edge research that examine topics such as knowledge graph-guided prompting, retrieval-augmented generation, reasoning over structured data, and hybrid architectures combining symbolic and neural systems. ... -
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Linguist
Detect blob languages, suppress generated files and generate graphs
This library is used on GitHub.com to detect blob languages, ignore binary or vendored files, suppress generated files in diffs, and generate language breakdown graphs. Linguist is a Ruby library so you will need a recent version of Ruby installed. There are known problems with the macOS/XCode supplied version of Ruby that causes problems installing some of the dependencies. Accordingly, we highly recommend you install a version of Ruby using Homebrew, rbenv, rvm, ruby-build, asdf or other packaging system, before attempting to install Linguist and the dependencies. ... -
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plotly.js
JavaScript charting library behind Plotly and Dash
...Built on top of d3.js and stack.gl, Plotly.js is a high-level, declarative charting library. plotly.js ships with over 40 chart types, including 3D charts, statistical graphs, and SVG maps. plotly.js is free and open source and you can view the source, report issues or contribute on GitHub. For plotly.js to build with Webpack you will need to install ify-loader@v1.1.0+ and add it to your webpack.config.json. This adds Browserify transform compatibility to Webpack which is necessary for some plotly.js dependencies. ... -
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PyG
Graph Neural Network Library for PyTorch
PyG (PyTorch Geometric) is a library built upon PyTorch to easily write and train Graph Neural Networks (GNNs) for a wide range of applications related to structured data. It consists of various methods for deep learning on graphs and other irregular structures, also known as geometric deep learning, from a variety of published papers. In addition, it consists of easy-to-use mini-batch loaders for operating on many small and single giant graphs, multi GPU-support, DataPipe support, distributed graph learning via Quiver, a large number of common benchmark datasets (based on simple interfaces to create your own), the GraphGym experiment manager, and helpful transforms, both for learning on arbitrary graphs as well as on 3D meshes or point clouds. ... -
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Eino
LLM application development framework for Go with agents and flows
...Eino includes an Agent Development Kit that enables developers to create intelligent agents capable of using tools, coordinating with other agents, and managing conversational context. Eino also offers orchestration capabilities that allow components to be connected into chains, graphs, or workflows for complex AI pipelines. These orchestration features handle concerns such as concurrency, streaming responses, and type safety so developers can focus on application logic. -
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LangGraph.js
Framework to build resilient language agents as graphs
LangGraphJS is a JavaScript framework designed to build stateful AI applications and autonomous agents using graph-based execution models. Developed as part of the LangChain ecosystem, the framework allows developers to represent complex AI workflows as graphs where nodes represent tasks and edges define the flow of execution. This structure makes it easier to implement long-running agents, multi-step reasoning pipelines, and workflows that require persistent state. LangGraphJS supports advanced capabilities such as branching logic, loops, and conditional execution, enabling developers to build sophisticated AI systems that can adapt to dynamic conditions. ... -
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DB Browser for SQLite
The DB Browser for SQLite
...Import and export records as text, import and export tables from/to CSV files, import and export databases from/to SQL dump files, issue SQL queries and inspect the results, examine a log of all SQL commands issued by the application, plot simple graphs based on table or query data. -
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Cyber
Semantic non-deterministic Superintelligence consensus computer
...There are however at least 3 problems associated with the above-mentioned implementations. (1) the subjective nature of relevance, (2) difficulty in scaling consensus computers for over-sized knowledge graphs, (3) the lack of quality amongst such knowledge graphs. They are prone to various surface attacks, such as sybil attacks, and the selfish behavior of the interacting agents. In this document, we define a protocol framework for provable consensus computing of relevance, between content-addresable objects, which can be computed on GPUs. -
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Entity relation diagrams generator
Entity Relation Diagrams generation tool
ERAlchemy generates Entity Relation (ER) diagram from databases or from SQLAlchemy models. The simplest way to install eralchemy on OSX is by using Homebrew. ERAlchemy requires GraphViz to generate the graphs and Python. Both are available for Windows, Mac and Linux. Thanks to it's modular architecture, it can be connected to other ORMs/ODMs/OGMs/O*Ms. Some tests require a local postgres database with a schema named test in a database named test all owned by a user named postgres with a password of postgres. ERAlchemy was inspired by erd, though it is able to render the ER diagram directly from the database and not just only from the ER markup language. -
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DGL
Python package built to ease deep learning on graph
Build your models with PyTorch, TensorFlow or Apache MXNet. Fast and memory-efficient message passing primitives for training Graph Neural Networks. Scale to giant graphs via multi-GPU acceleration and distributed training infrastructure. DGL empowers a variety of domain-specific projects including DGL-KE for learning large-scale knowledge graph embeddings, DGL-LifeSci for bioinformatics and cheminformatics, and many others. We are keen to bringing graphs closer to deep learning researchers. We want to make it easy to implement graph neural networks model family. ... -
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Mangle
Go library for Datalog-style logical reasoning and domain modeling
...Unlike SQL, Mangle’s declarative syntax naturally supports recursion and modular program structure, making it suitable for representing hierarchical or interconnected datasets like dependency graphs or knowledge graphs. The project’s implementation is provided as a Go library, enabling developers to embed Mangle directly into their applications or services, such as database-like gRPC systems. -
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Nezha Monitoring
Self-hosted, lightweight server and website monitoring and O&M tool
Nezha is a self-hosted monitoring and alerting tool for servers, providing real-time tracking and notifications on server health and performance. -
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Dask
Parallel computing with task scheduling
Dask is a Python library for parallel and distributed computing, designed to scale analytics workloads from single machines to large clusters. It integrates with familiar tools like NumPy, Pandas, and scikit-learn while enabling execution across cores or nodes with minimal code changes. Dask excels at handling large datasets that don’t fit into memory and is widely used in data science, machine learning, and big data pipelines. -
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Writer Framework
No-code in the front, Python in the back. An open-source framework
...It follows a hybrid approach where user interfaces are created using a drag-and-drop editor while business logic is implemented in Python, allowing teams to balance speed and flexibility without sacrificing control. The framework is particularly focused on AI use cases, enabling developers to integrate large language models, knowledge graphs, and custom machine learning workflows into user-facing applications. Its architecture enforces a clear separation of concerns between frontend and backend, which improves maintainability and scalability as applications grow in complexity. The system is designed to support rapid prototyping, enabling developers to iterate on UI and backend logic independently and deploy changes quickly. -
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F1 Race Replay
An interactive Formula 1 race visualisation and data analysis tool
...It ingests official timing and positional data, then renders vehicle movements through track maps and 3D visualizations so fans, analysts, and engineers can review strategy, overtakes, tire degradation effects, and pit stop impacts in detail. Users can scrub through time, jump between cars, and overlay performance graphs such as speed, sector times, and gap differentials to evaluate performance trends across laps. This deep dive capability turns passive viewing into active exploration, empowering enthusiasts and professionals to discover insights usually hidden in raw data. The viewer also supports annotations and bookmark capabilities so users can mark moments of interest for future review or comparison. -
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ngraph.path
Path finding in a graph
ngraph.path is a JavaScript library that implements efficient pathfinding algorithms for graphs, primarily designed to compute shortest paths in weighted or unweighted networks. It provides a clean API for constructing graph models, assigning weights to edges, and querying for optimal routes between nodes, making it useful for routing, games, maps, and network optimization. The library includes several algorithm implementations such as A*, Dijkstra’s, and breadth-first search, each suited to different types of graph structure and performance needs. ... -
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Vince
Self Hosted Alternative To Google Analytics
vince is a versatile tool that assists in data analysis and visualization, providing users with insights through interactive charts and graphs. -
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Benchee
Easy and extensible benchmarking in Elixir
...Benchee allows you to compare the performance of different pieces of code at a glance. It is also versatile and extensible, relying only on functions. There are also a bunch of plugins to draw pretty graphs and more! Benchee runs each of your functions for a given amount of time after an initial warmup, it then measures their run time and optionally memory consumption. It then shows different statistical values like average, standard deviation etc. The aforementioned plugins like benchee_html make it possible to generate nice-looking HTML reports, where individual graphs can also be exported as PNG images. first runs the functions for a given warmup time without recording the results, to simulate a "warm"/running system. ... -
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Plotly Graphing Library for MATLAB
Plotly Graphing Library for MATLAB
Create interactive charts in your web browser with MATLAB® and Plotly. -
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Youtu-GraphRAG
Vertically Unified Agents for Graph Retrieval-Augmented Reasoning
Youtu-GraphRAG is a research framework developed by Tencent for performing complex reasoning using graph-based retrieval-augmented generation. The system combines knowledge graphs, retrieval mechanisms, and agent-based reasoning into a unified architecture designed to handle knowledge-intensive tasks. Instead of relying solely on text retrieval, the framework organizes information into structured graph schemas that represent entities, relationships, and attributes. These structures allow the system to perform multi-hop reasoning by decomposing complex questions into smaller queries that can be executed across different parts of the graph. ... -
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Huatuo-Llama-Med-Chinese
Instruction-tuning LLM with Chinese Medical Knowledge
...The project builds specialized models by fine-tuning architectures such as LLaMA, Alpaca-Chinese, and Bloom with curated medical datasets. These datasets are constructed from medical knowledge graphs, academic literature, and question-answer pairs designed to teach models how to respond accurately to healthcare-related queries. The goal of the project is to improve the reliability and domain expertise of language models when answering medical questions or assisting with healthcare-related tasks. By combining domain-specific training data with instruction-tuning techniques, the project produces models capable of generating more accurate medical responses than general-purpose models. -
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Visual Blocks
Visual Blocks for ML is a Google visual programming framework
...Under the hood it leans on web-friendly runtimes (e.g., WebGPU/WebGL/WebNN or TensorFlow.js backends) to execute pipelines locally, which is great for demos, teaching, and privacy-sensitive prototypes. The block abstraction encourages modularity: you can package a preprocessor, a model, and a postprocessor as a reusable composite for others to slot into their graphs. Because everything lives in the browser, sharing is as simple as exporting a project or link, and collaborators can experiment without installing toolchains. For educators and product teams alike, Visual Blocks reduces the distance from idea to interactive proof-of-concept by turning ML diagrams. -
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Deptrac
Keep your architecture clean.
Deptrac is a static analysis tool for PHP that helps maintain architectural boundaries within codebases. It analyzes dependencies between classes and ensures that code follows predefined architectural rules. Deptrac is useful for preventing unwanted couplings, enforcing clean code architecture, and detecting violations early during development. -
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TrustGraph
Deploy reasoning AI agents powered by agentic graph RAG in minutes
TrustGraph is an AI-driven framework designed to assess and visualize trust relationships within networks, aiding in the analysis of trustworthiness and influence among entities. -
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LCOV
LCOV
LCOV is an extension of GCOV, a GNU tool that provides information about what parts of a program are actually executed (i.e. "covered") while running a particular test case. The extension consists of a set of Perl scripts that builds on the textual GCOV output to implement the following enhanced functionality,
