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Repository for benchmarking graph neural networks
The repository implements the Transformer over Directed Acyclic Graph (DAG transformer) in Pytorch Geometric.
A graph neural network tailored to directed acyclic graphs that outperforms conventional GNNs by leveraging the partial order as strong inductive bias besides other suitable architectural features.
Unofficial PyTorch implementation of the CVPR'19 paper "Skeleton-Based Action Recognition with Directed Graph Neural Networks".
The PyTorch implementation of Directed Graph Contrastive Learning (DiGCL), NeurIPS-2021
Implement of DiGCN, NeurIPS-2020
Dir-GNN is a machine learning model that enables learning on directed graphs.
A bestiary of evolutionary, swarm and other metaphor-based algorithms
Implementation of Graph Convolutional Networks in TensorFlow
Geom_GCN re-implementation using only pytorch. ( Not using DGL)
Official Repository of "A Fair Comparison of Graph Neural Networks for Graph Classification", ICLR 2020
Official code for the ICML2022 paper -- GNNRank: Learning Global Rankings from Pairwise Comparisons via Directed Graph Neural Networks
Graph-Mamba: Towards Long-Range Graph Sequence Modelling with Selective State Spaces
Recipe for a General, Powerful, Scalable Graph Transformer
This directory contains code necessary to run the GraphNAS algorithm.
Representation learning on large graphs using stochastic graph convolutions.
[ICLR 2020; IPDPS 2019] Fast and accurate minibatch training for deep GNNs and large graphs (GraphSAINT: Graph Sampling Based Inductive Learning Method).
"GraphSHA: Synthesizing Harder Samples for Class-Imbalanced Node Classification" in KDD'23
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Graph Transformer Architecture. Source code for "A Generalization of Transformer Networks to Graphs", DLG-AAAI'21.
Boost learning for GNNs from the graph structure under challenging heterophily settings. (NeurIPS'20)
[WSDM'2023] "HGCL: Heterogeneous Graph Contrastive Learning for Recommendation"
Source code for our AAAI paper "Weisfeiler and Leman Go Neural: Higher-order Graph Neural Networks".
Codes for NIPS 2019 Paper: Rethinking Kernel Methods for Node Representation Learning on Graphs
Network dataset extraction library – part of the KONECT project by Jérôme Kunegis, University of Namur
A declarative, efficient, and flexible JavaScript library for building user interfaces.
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
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JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
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We are working to build community through open source technology. NB: members must have two-factor auth.
Open source projects and samples from Microsoft.
Google ❤️ Open Source for everyone.
Alibaba Open Source for everyone
Data-Driven Documents codes.
China tencent open source team.