title | paper link | code link |
---|---|---|
DeepWalk: Online Learning of Social Representations | [paper] | [code(python)] |
LINE: Large-scale Information Network Embedding | [paper] | [code(c++)] |
Watch Your Step: Learning Node Embeddings via Graph Attention | [paper] | [code(python)] |
node2vec: Scalable Feature Learning for Networks | [paper] | [code(python)] |
Deep Graph Infomax | [paper] | [code(python)] |
graph2vec: Learning Distributed Representations of Graphs | [paper] | [code(python)] |
struc2vec: Learning Node Representations from Structural Identity | [paper] | [code(python)] |
Learning Structural Node Embeddings via Diffusion Wavelets | [paper] | [code(python)] |
Label Informed Attributed Network Embedding | [paper] | [code(matlab)] |
Accelerated Attributed Network Embedding | [paper] | [code(matlab)][code(python)] |
Deep Gaussian Embedding of Graphs: Unsupervised Inductive Learning via Ranking | [paper] | [code(python)] |
Network Representation Learning with Rich Text Information | [paper] | [code(matlab)] |
Structural Deep Network Embedding | [paper] | [code(python)] |
title | paper link | code link |
---|---|---|
Diffusion-Convolutional Neural Networks | [paper] | [code(theano)] |
Learning Convolutional Neural Networks for Graphs | [paper] | [code(keras)] |
Geometric Deep Learning: Going beyond Euclidean data | [paper] | [code] |
Deriving Neural Architectures from Sequence and Graph Kernels | [paper] | [code(tensorflow)] |
Semi-Supervised Classification with Graph Convolutional Networks | [paper] | [code(pytorch)][code(tensorflow)] |
Neural Message Passing for Quantum Chemistry | [paper] | [code(pytorch)] |
GRAPH ATTENTION NETWORKS | [paper] | [code(tensorflow)] |
Stochastic Training of Graph Convolutional Networks with Variance Reduction | [paper] | [code(tensorflow)] |
Link Prediction Based on Graph Neural Networks | [paper] | [code(pytorch)] |
HOW POWERFUL ARE GRAPH NEURAL NETWORKS? | [paper] | [code(pytorch)] |
Deeper Insights into Graph Convolutional Networks for Semi-Supervised Learning | [paper] | [code(tensorflow)] |
Supervised Community Detection with Line Graph Neural Networks | [paper] | [code(pytorch)] |
SPARC: Self-Paced Network Representation for Few-Shot Rare Category Characterization | [paper] | [code] |
An End-to-End Deep Learning Architecture for Graph Classification | [paper] | [code(pytorch)] |
Neural Message Passing for Quantum Chemistr | [paper] | [code(pytorch)] |
Capsule Graph Neural Network | [paper] | [code(tensorflow)] |
FastGCN: Fast Learning with Graph Convolutional Networks via Importance Sampling | [paper] | [code(tensorflow)] |
Relational Inductive Biases, Deep Learning, and Graph Networks | [paper] | [code(tensorflow)] |
CANE: Context-Aware Network Embedding for Relation Modeling | [paper] | [code(tensorflow)] |
DIRECT MULTI-HOP ATTENTION BASED GRAPH NEURAL NETWORKS | [paper] | [code] |
DAG-Net: Double Attentive Graph Neural Network for Trajectory Forecasting | [paper] | [code(pytorch)] |
PYTORCH-BIGGRAPH: A LARGE-SCALE GRAPH EMBEDDING SYSTEM | [paper] | [code(pytorch)] |
Graph Neural Networks with Convolutional ARMA Filters | [paper] | [code] |
Weisfeiler and Leman Go Neural: Higher-order Graph Neural Networks | [paper] | [code(pytorch)] |
Simplifying Graph Convolutional Networks | [paper] | [code(pytorch)] |
Graph U-Net | [paper] | [code(pytorch)] |
Combining Neural Networks with Personalized PageRank for Classification on Graphs | [paper] | [code(pytorch)] |
Modeling Relational Data with Graph Convolutional Networks | [paper] | [code(keras)] |
Attention-based Graph Neural Networks for Semi-Supervised Learning | [paper] | [code] |
title | paper link | code link |
---|---|---|
Graph-Bert: Only Attention is Needed for Learning Graph Representations | [paper] | [code(pytorch)] |
Universal Graph Transformer Self-Attention Networks | [paper] | [code(pytorch)] |
G5: A Universal GRAPH-BERT for Graph-to-Graph Transfer and Apocalypse Learning | [paper] | [code] |
A Generalization of Transformer Networks to Graphs | [paper] | [code(pytorch)] |
SLAPS: Self-Supervision Improves Structure Learning for Graph Neural Networks | [paper] | [code(pytorch)] |