Search Results for "node link"
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Showing 3 open source projects for "node link"
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Curtain LogTrace File Activity MonitoringCurtain LogTrace File Activity Monitoring is an enterprise file activity monitoring solution. It tracks user actions: create, copy, move, delete, rename, print, open, close, save. Includes source/destination paths and disk type. Perfect for monitoring user file activities.
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Build experiences that drive engagement and increase transactionsSendbird's chat, voice, and video APIs power conversations and communities in hundreds of the most innovative apps and products. Sendbird’s feature-rich platform, and pre-fab UI components make developers more productive. We take care of a ton of operational complexity under the hood, so you can power a rich chat service, and life-like voice, and video experiences, and not worry about features, edge cases, reliability, or scale.
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Barfi
A Python visual Flow Based Programming library
...Then the schema is executed with barfi.ComputeEngine. Each barfi.Block has some properties that enable the FBP and schema building. Firstly, each Block has Input and Output interfaces that link to other Blocks. Each Block can carry an executable function, that is specified by the user. This function can access/get data from the Input interface, perform computations or calculations, and set the Output interface. In general, Barfi is an abstraction of Graphical Programming, Flow-Based Programming, or Node programming. Where the Block is synonymous to a Node, and a Link (connection) is synonymous with an Edge. ... -
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Pyrlang
Erlang node implemented in Python 3.5+ (Asyncio-based)
This is a drop-in Erlang node implementation in Python 3, implementing a network Erlang node protocol. It was designed to allow interoperation between existing Python projects and BEAM languages: Erlang, Elixir, Gleam, Luaerl, LFE, Clojerl, and such. With just a few lines of startup code your Python program becomes an Erlang network node, participating in the Erlang cluster. -
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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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