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graph_cnn icon graph_cnn

Supplementary code to "Convolutional Neural Networks Generalization Utilizing the Data Graph Structure"

graphgym icon graphgym

Platform for designing and evaluating Graph Neural Networks (GNN)

graphical-gan icon graphical-gan

source code of the paper Graphical Generative Adversarial Networks

graphics icon graphics

TensorFlow Graphics: Differentiable Graphics Layers for TensorFlow

graphiql icon graphiql

An in-browser IDE for exploring GraphQL.

graphite icon graphite

A decentralized and encrypted alternative to Google's G-Suite

graphormer icon graphormer

This is the official implementation for "Do Transformers Really Perform Bad for Graph Representation?".

graphpipe icon graphpipe

Machine Learning Model Deployment Made Simple

graphql icon graphql

GraphQL is a query language and execution engine tied to any backend service.

graphql-compose icon graphql-compose

graphql-compose – toolkit for construction complex GraphQL schemas (compose, modify and relate types). Provides bunch of plugins for type generation. Allows to build your own plugins/helper-functions for type construction/modification.

graphql-faker icon graphql-faker

🎲 Mock or extend your GraphQL API with faked data. No coding required.

graphql-js icon graphql-js

A reference implementation of GraphQL for JavaScript

graphql-relay-js icon graphql-relay-js

A library to help construct a graphql-js server supporting react-relay.

graphql-tools icon graphql-tools

:wrench: A collection of handy tools for manipulating GraphQL schemas

graphsage icon graphsage

Inductive representation learning on large graphs.

graphscape icon graphscape

A directed graph model of the visualization design space, using Vega-Lite.

graphtransformer icon graphtransformer

Graph Transformer Architecture. Source code for "A Generalization of Transformer Networks to Graphs", DLG-AAAI'21.

graphvite icon graphvite

GraphVite: A General and High-performance Graph Embedding System

graphwavemachine icon graphwavemachine

A scalable implementation of "Learning Structural Node Embeddings Via Diffusion Wavelets (KDD 2018)".

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