What does dsgiitr/graph_nets do?

Graph Nets is a graph neural network library and educational toolkit implemented in PyTorch, providing implementations of popular graph representation learning algorithms and research papers. The project covers core graph machine learning tasks including semi-supervised node classification, inductive and unsupervised node embedding generation, and neighborhood feature aggregation.

What are the main features of dsgiitr/graph_nets?

The main features of dsgiitr/graph_nets are: Graph Neural Network Implementations, Graph Representation Learning, Dynamic Attention, PyTorch-Based Frameworks, Graph Neighborhood Sampling, Graph Convolutions, Skip-Gram Model Architectures, Chebyshev Polynomial Approximations.

Which projects share features with dsgiitr/graph_nets?

Projects with overlapping indexed features include: shenweichen/graphembedding — GraphEmbedding is a graph network representation library and node embedding framework. It provides a toolkit for… williamleif/graphsage — GraphSAGE is a graph neural network framework designed for inductive representation learning on large-scale graphs. It… tkipf/gcn — This is a graph convolutional network library designed for performing node and graph classification on… packtpublishing/hands-on-graph-neural-networks-using-python — This repository serves as an educational resource for implementing graph neural networks using Python. It provides a… dmlc/dgl — DGL is a Python library for building and training graph neural networks. It functions as a graph message passing… tkipf/pygcn — pygcn is a PyTorch library and framework for implementing graph convolutional networks. It provides tools for…

Graph Nets - train graph neural networks

Name: dsgiitr/graph_nets
Author: dsgiitr

Features

Graph Neural Network Implementations - Implements popular graph neural network algorithms and research papers using PyTorch for representation learning.
Graph Representation Learning - Generate continuous vector representations for nodes in a network using unsupervised learning approaches inspired by natural language modeling techniques.
Dynamic Attention - Assigns adaptive attention weights to neighboring nodes during feature aggregation without requiring full graph visibility.
PyTorch-Based Frameworks - Provides a collection of deep learning models and representation algorithms built on top of PyTorch.
Graph Neighborhood Sampling - Computes inductive node embeddings by sampling fixed-size local neighborhoods and aggregating neighbor features for unseen graph structures.
Graph Convolutions - Applies uniform filter parameters across non-euclidean graph neighborhoods to perform semi-supervised node classification using localized message passing.
Skip-Gram Model Architectures - Learns continuous vector representations of nodes by generating random walk sequences and optimising a language-modeling objective function.
Chebyshev Polynomial Approximations - Formulates graph convolutions using truncated Chebyshev polynomial expansions to enable fast localized spectral filtering without explicit eigen-decomposition.
Node Classification - Apply shared filter parameters across graph locations to perform semi-supervised classification on non-euclidean graph data structures.
Inductive Learning - Leverage node feature information through sampling and aggregation strategies to efficiently compute embeddings for previously unseen graph nodes.
Graph Node Filtering - Apply convolutional operations across non-euclidean graph structures using shared filter parameters to perform semi-supervised classification on nodes.
Unsupervised Node Embeddings - Generates continuous vector representations of nodes in an unsupervised manner using random walks and language modeling objectives.
Attention-Weighted Feature Pairs - Assign learned attention weights dynamically to prioritize relevant connections during feature aggregation without requiring prior knowledge of the full graph structure.
Spectral Graph Convolution Tools - Formulate convolutional neural networks on graphs using fast localized spectral filtering based on Chebyshev polynomials.
Node Embeddings - Use inductive frameworks with neighborhood feature sampling to efficiently compute vector representations for newly added graph nodes.
PyTorch Model Components - Organises deep learning operations into reusable neural network layers using tensor manipulation primitives provided by a tensor computing framework.
Graph Traversal Embeddings - Generates continuous vector representations by simulating random paths across network structures and optimising co-occurrence objectives using language modeling.
Educational Toolkits - Serves as an educational toolkit for exploring graph convolutions, attention mechanisms, and random walks.

Projects sharing features with Graph Nets

These projects share indexed features with Graph Nets. Shared tags can include platform or build tooling; verify the primary use case before treating a result as a replacement.

shenweichen/graphembedding
shenweichen/GraphEmbedding
3,844View on GitHub
GraphEmbedding is a graph network representation library and node embedding framework. It provides a toolkit for transforming complex network nodes into low-dimensional vector spaces, enabling the integration of relational graph data into machine learning workflows. The library functions as a dimensionality reduction toolkit and network topology analysis tool. It uses matrix-factorization techniques to preserve global connectivity and employs random-walk sampling with skip-gram based vector optimization to learn numerical representations of nodes. The framework covers several domain-specific
Pythondeepwalkgraphgraphembedding
View on GitHub3,844
williamleif/graphsage
williamleif/GraphSAGE
3,657View on GitHub
GraphSAGE is a graph neural network framework designed for inductive representation learning on large-scale graphs. It functions as an inductive graph embedding tool and neighborhood aggregation engine, enabling the generation of numerical node representations that generalize to previously unseen data. The system distinguishes itself by computing node embeddings through the aggregation of features from local neighborhoods rather than relying on a global lookup table. This approach allows the framework to operate as both a supervised graph classifier for predicting categorical node classes and
Python
View on GitHub3,657
tkipf/gcn
tkipf/gcn
7,361View on GitHub
This is a graph convolutional network library designed for performing node and graph classification on graph-structured data. It functions as a framework for generating graph embeddings and implementing spectral convolutional neural networks to predict labels for nodes and entire graph structures. The library provides specialized tools for spectral graph convolutions, utilizing Chebyshev polynomial approximations to perform feature aggregation. It includes a multi-graph processing framework that manages batches of different graph instances through block-diagonal adjacency matrices and pooling
Python
View on GitHub7,361
packtpublishing/hands-on-graph-neural-networks-using-python
PacktPublishing/Hands-On-Graph-Neural-Networks-Using-Python
1,044View on GitHub
This repository serves as an educational resource for implementing graph neural networks using Python. It provides a collection of structured code examples and tutorials designed to guide developers through the process of building and training machine learning models that operate on complex, interconnected datasets. The project covers the core mechanics of graph-based deep learning, including message-passing architectures, feature aggregation, and the stacking of convolutional layers. It demonstrates how to represent non-Euclidean data as static graphs and how to manage memory during training
Jupyter Notebook
View on GitHub1,044

Compare all 30 related projects

Projects sharing features with Graph Nets

These projects share indexed features with Graph Nets. Shared tags can include platform or build tooling; verify the primary use case before treating a result as a replacement.

shenweichen/graphembedding
shenweichen/GraphEmbedding
3,844View on GitHub
GraphEmbedding is a graph network representation library and node embedding framework. It provides a toolkit for transforming complex network nodes into low-dimensional vector spaces, enabling the integration of relational graph data into machine learning workflows. The library functions as a dimensionality reduction toolkit and network topology analysis tool. It uses matrix-factorization techniques to preserve global connectivity and employs random-walk sampling with skip-gram based vector optimization to learn numerical representations of nodes. The framework covers several domain-specific
Pythondeepwalkgraphgraphembedding
View on GitHub3,844
williamleif/graphsage
williamleif/GraphSAGE
3,657View on GitHub
GraphSAGE is a graph neural network framework designed for inductive representation learning on large-scale graphs. It functions as an inductive graph embedding tool and neighborhood aggregation engine, enabling the generation of numerical node representations that generalize to previously unseen data. The system distinguishes itself by computing node embeddings through the aggregation of features from local neighborhoods rather than relying on a global lookup table. This approach allows the framework to operate as both a supervised graph classifier for predicting categorical node classes and
Python
View on GitHub3,657
tkipf/gcn
tkipf/gcn
7,361View on GitHub
This is a graph convolutional network library designed for performing node and graph classification on graph-structured data. It functions as a framework for generating graph embeddings and implementing spectral convolutional neural networks to predict labels for nodes and entire graph structures. The library provides specialized tools for spectral graph convolutions, utilizing Chebyshev polynomial approximations to perform feature aggregation. It includes a multi-graph processing framework that manages batches of different graph instances through block-diagonal adjacency matrices and pooling
Python
View on GitHub7,361
packtpublishing/hands-on-graph-neural-networks-using-python
PacktPublishing/Hands-On-Graph-Neural-Networks-Using-Python
1,044View on GitHub
This repository serves as an educational resource for implementing graph neural networks using Python. It provides a collection of structured code examples and tutorials designed to guide developers through the process of building and training machine learning models that operate on complex, interconnected datasets. The project covers the core mechanics of graph-based deep learning, including message-passing architectures, feature aggregation, and the stacking of convolutional layers. It demonstrates how to represent non-Euclidean data as static graphs and how to manage memory during training
Jupyter Notebook
View on GitHub1,044

Compare all 30 related projects

dsgiitrgraph_nets

Graph Nets

Features

Projects sharing features with Graph Nets

shenweichen/GraphEmbedding

williamleif/GraphSAGE

tkipf/gcn

PacktPublishing/Hands-On-Graph-Neural-Networks-Using-Python

Curated searches featuring Graph Nets

Frequently asked questions

Star history

Frequently asked questions

Projects sharing features with Graph Nets

shenweichen/GraphEmbedding

williamleif/GraphSAGE

tkipf/gcn

PacktPublishing/Hands-On-Graph-Neural-Networks-Using-Python

Curated searches featuring Graph Nets