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aoam

AOAM: Automatic Optimization of Adjacency Matrix for Graph Convolutional Network

awesome-network-embedding

A curated list of network embedding techniques.

center-loss.pytorch

center loss for face recognition

confgcn

AISTATS 2019: Confidence-based Graph Convolutional Networks for Semi-Supervised Learning

cvpr2020-shoestring

cvpr2020 source code

deeper_insights_into_gcns

Deeper insights into graph convolutional networks for semi-supervised learning

deepergnn

Official PyTorch implementation of "Towards Deeper Graph Neural Networks" [KDD2020]

deepgwc

code of DeepGWC: A Deep Graph Wavelet Convolutional Neural Network for Semi-supervised Node Classification

gat-for-ppi

The code of Graph Attention Networks for Cora, Citeseer, Pubmed and PPI

gaug

AAAI'21: Data Augmentation for Graph Neural Networks

gcc

GCC: Graph Contrastive Coding for Graph Neural Network Pre-Training

gcnii

PyTorch implementation of "Simple and Deep Graph Convolutional Networks"

gcnmf

PyTorch implementation of "Graph Convolutional Networks for Graphs Containing Missing Features"

gnnfpa

A pytorch implementation of "Propagation is All You Need: A New Framework for Representation Learning and Classifier Training on Graphs".

gnnpapers

Must-read papers on graph neural networks (GNN)

graph-based-deep-learning-literature

links to conference publications in graph-based deep learning

graphgallery

GraphGallery is a gallery for benchmarking Graph Neural Networks (GNNs) with TensorFlow 2.x and PyTorch backend.

graphhop

graphormer

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

gresnet

Source code of GResNet

hellogithub

:octocat: 分享 GitHub 上有趣、入门级的开源项目

hypergcn

NeurIPS 2019: HyperGCN: A New Method of Training Graph Convolutional Networks on Hypergraphs

lcn

AISTATS 2019: Lovász Convolutional Networks

m-gwnn

max-mahalanobis-centerloss_pytorch

neural_tree

non-homophily-benchmarks

[WWW 2021 GLB] New Benchmarks for Learning on Non-Homophilous Graphs

non-homophily-large-scale

[NeurIPS 2021] Large Scale Learning on Non-Homophilous Graphs: New Benchmarks and Strong Simple Methods

nrlpapers

Must-read papers on network representation learning (NRL) / network embedding (NE)

os-k-means

现有聚类算法面向高维稀疏数据多未考虑类簇可重叠和离群点的存在，导致聚类效果不理想。针对此，提出一种可重叠子空间K-Means聚类算法(An Overlapping Subspace K-Means Clustering Algorithm, OS-K-Means)。给出类簇子空间计算策略，在聚类过程中动态更新每个类簇的属性子空间，并定义合理的约束函数指导聚类过程，从而实现类簇的可重叠性与寻找离群点的效果。具体地，定义合理的目标函数对传统的K-Means算法进行修正，利用熵权约束分别计算每个类簇中每个维度的权重，使用权重值来标识对不同类簇中维度的相对重要性，并加入对重叠程度和离群值数量控制的参数。