Search Results for "node graph"
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Showing 7 open source projects for "node graph"
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1
Testcontainers node
Supports tests, providing lightweight, throwaway instances of database
Testcontainers is an open source library for providing throwaway, lightweight instances of databases, message brokers, web browsers, or just about anything that can run in a Docker container. No more need for mocks or complicated environment configurations. Define your test dependencies as code, then simply run your tests and containers will be created and then deleted. With support for many languages and testing frameworks, all you need is Docker. Use a containerized instance of your... -
2
miniaudio
Audio playback and capture library written in C,
...With the low-level API, just initialize a connection to the device and send or receive raw audio data. The modular design of miniaudio allows you to use the low-level API without compromising your ability to make use of other features like the node graph and resource manager. miniaudio's node graph system gives you an easy way to set up advanced mixing and effect graphs. Each node sends it's output to another node, and so on and so forth to produce all kinds of effects. You can even implement your own custom nodes and plug them in however you like. Plug multiple nodes into the same input node for mixing and use the splitter node for complex routing. -
3
TensorFlow
TensorFlow is an open source library for machine learning
...The platform can be easily deployed on multiple CPUs, GPUs and Google's proprietary chip, the tensor processing unit (TPU). TensorFlow expresses its computations as dataflow graphs, with each node in the graph representing an operation. Nodes take tensors—multidimensional arrays—as input and produce tensors as output. The framework allows for these algorithms to be run in C++ for better performance, while the multiple levels of APIs let the user determine how high or low they wish the level of abstraction to be in the models produced. ... -
4
Svelvet
A library for building dynamic, infinitely customizable interfaces
Build interactive, node-based UIs and diagrams in Svelte. Svelvet is a lightweight component library for building dynamic, customizable node graphs. Svelvet allows you to easily create intuitive user interfaces and diagrams with pre-built components with node-to-node data flow, seamless zooming and panning, customizable edges and nodes and more. Start mapping out your ideas with our NPM package. Simply provide your node data to the Svelvet component and you're ready to show off your... -
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5
Flowy
The minimal javascript library to create flowcharts
flowy is a minimal JavaScript library for building interactive flowchart-style interfaces in web applications, allowing developers to create node-based editors, automation builders, or visual programming tools with relatively little code. It provides draggable, connectable blocks (nodes) that can be placed on a canvas, connected via lines, and rearranged dynamically while preserving the underlying graph structure. The library focuses on simplicity and aesthetics, offering a clean look out of the box that can be customized with your own CSS, making it suitable for dashboards, SaaS products, or internal tools. ... -
6
VivaGraph
Graph drawing library for JavaScript
VivaGraphJS is a powerful, high-performance graph drawing library for JavaScript that enables developers to visualize complex networks directly in the browser or in Node.js environments. It is designed for speed and scalability, handling large graph datasets with smooth rendering and interactive capabilities such as dragging nodes and zooming. The library supports multiple rendering backends including SVG, WebGL, and Canvas, allowing developers to choose the best balance of performance and... -
7
node2vec
Learn continuous vector embeddings for nodes in a graph using biased R
The node2vec project provides an implementation of the node2vec algorithm, a scalable feature learning method for networks. The algorithm is designed to learn continuous vector representations of nodes in a graph by simulating biased random walks and applying skip-gram models from natural language processing. These embeddings capture community structure as well as structural equivalence, enabling machine learning on graphs for tasks such as classification, clustering, and link prediction....
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