Graph Models

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Graphs • 66 methods

The Graph Methods include neural network architectures for learning on graphs with prior structure information, popularly called as Graph Neural Networks (GNNs).

Recently, deep learning approaches are being extended to work on graph-structured data, giving rise to a series of graph neural networks addressing different challenges. Graph neural networks are particularly useful in applications where data are generated from non-Euclidean domains and represented as graphs with complex relationships.

Some tasks where GNNs are widely used include node classification, graph classification, link prediction, and much more.

In the taxonomy presented by Wu et al. (2019), graph neural networks can be divided into four categories: recurrent graph neural networks, convolutional graph neural networks, graph autoencoders, and spatial-temporal graph neural networks.

Image source: A Comprehensive Survey on Graph NeuralNetworks

Methods

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Method	Year	Papers
GCN Semi-Supervised Classification with Graph Convolutional Networks	2016	957
Graph Transformer A Generalization of Transformer Networks to Graphs	2020	288
DCNN Diffusion-Convolutional Neural Networks	2015	274
GAT Graph Attention Networks	2017	193
GraphSAGE Inductive Representation Learning on Large Graphs	2017	128
MPNN Neural Message Passing for Quantum Chemistry	2017	65
DGCNN An End-to-End Deep Learning Architecture for Graph Classification	2018	48
LightGCN LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation	2020	46
GIN How Powerful are Graph Neural Networks?	2018	41
SSE Learning Steady-States of Iterative Algorithms over Graphs	2018	40
ARMA Graph Neural Networks with convolutional ARMA filters	2019	37
MoNet Geometric deep learning on graphs and manifolds using mixture model CNNs	2016	25
RGCN Modeling Relational Data with Graph Convolutional Networks	2017	22
GNS Learning to Simulate Complex Physics with Graph Networks	2020	21
SchNet SchNet: A continuous-filter convolutional neural network for modeling quantum interactions	2017	20
TGN Temporal Graph Networks for Deep Learning on Dynamic Graphs	2020	15
CGNN Crystal Graph Neural Networks for Data Mining in Materials Science	2019	11
DGI Deep Graph Infomax	2018	10
GGS-NNs Gated Graph Sequence Neural Networks	2015	9
GATv2 How Attentive are Graph Attention Networks?	2021	9
AGCN Adaptive Graph Convolutional Neural Networks	2018	8
GCNII Simple and Deep Graph Convolutional Networks	2020	7
DiffPool Hierarchical Graph Representation Learning with Differentiable Pooling	2018	7
ChebNet Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering	2016	6
PNA Principal Neighbourhood Aggregation for Graph Nets	2020	5
MeshGraphNet Learning Mesh-Based Simulation with Graph Networks	2020	5
FastGCN FastGCN: Fast Learning with Graph Convolutional Networks via Importance Sampling	2018	5
BiGCN Rumor Detection on Social Media with Bi-Directional Graph Convolutional Networks	2020	4
Graph Contrastive Coding GCC: Graph Contrastive Coding for Graph Neural Network Pre-Training	2020	4
LGCL Large-Scale Learnable Graph Convolutional Networks	2018	4
S-GCN Spherical convolutions on molecular graphs for protein model quality assessment	2020	3
BiGG Scalable Deep Generative Modeling for Sparse Graphs	2020	3
CGMM Contextual Graph Markov Model: A Deep and Generative Approach to Graph Processing	2018	3
Cluster-GCN Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks	2019	3
RE-NET Recurrent Event Network: Autoregressive Structure Inference over Temporal Knowledge Graphs	2019	2
GNNCL Graph Neural Networks with Continual Learning for Fake News Detection from Social Media	2020	2
GCNFN Fake News Detection on Social Media using Geometric Deep Learning	2019	2
GaAN GaAN: Gated Attention Networks for Learning on Large and Spatiotemporal Graphs	2018	2
MinCutPool Spectral Clustering with Graph Neural Networks for Graph Pooling	2019	2
Symbolic Deep Learning Discovering Symbolic Models from Deep Learning with Inductive Biases	2020	1
HMGNN Heterogeneous Molecular Graph Neural Networks for Predicting Molecule Properties	2020	1
MXMNet Molecular Mechanics-Driven Graph Neural Network with Multiplex Graph for Molecular Structures	2020	1
PinvGCN Pseudoinverse Graph Convolutional Networks: Fast Filters Tailored for Large Eigengaps of Dense Graphs and Hypergraphs	2020	1
TaxoExpan TaxoExpan: Self-supervised Taxonomy Expansion with Position-Enhanced Graph Neural Network	2020	1
Point-GNN Point-GNN: Graph Neural Network for 3D Object Detection in a Point Cloud	2020	1
IPA-GNN Learning to Execute Programs with Instruction Pointer Attention Graph Neural Networks	2020	1
HEGCN A Hierarchical Entity Graph Convolutional Network for Relation Extraction across Documents	2021	1
AdaGPR Layer-wise Adaptive Graph Convolution Networks Using Generalized Pagerank	2021	1
NeuroTactic Graph Contrastive Pre-training for Effective Theorem Reasoning	2021	1
GAGNN Group-Aware Graph Neural Network for Nationwide City Air Quality Forecasting	2021	1
Hi-LANDER Learning Hierarchical Graph Neural Networks for Image Clustering	2021	1
GraphESN	2010	1
CayleyNet CayleyNets: Graph Convolutional Neural Networks with Complex Rational Spectral Filters	2017	1
DualGCN	2018	1
PGC-DGCNN On Filter Size in Graph Convolutional Networks	2018	1
StoGCN Stochastic Training of Graph Convolutional Networks with Variance Reduction	2017	1
GeniePath GeniePath: Graph Neural Networks with Adaptive Receptive Paths	2018	1
CT-Layer DiffWire: Inductive Graph Rewiring via the Lovász Bound	2022	1
GAP-Layer DiffWire: Inductive Graph Rewiring via the Lovász Bound	2022	1
AutoGL An adaptive graph learning method for automated molecular interactions and properties predictions	2022	1
NN4G	2009	0

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