SOTAVerified

Graph Classification

Graph Classification is a task that involves classifying a graph-structured data into different classes or categories. Graphs are a powerful way to represent relationships and interactions between different entities, and graph classification can be applied to a wide range of applications, such as social network analysis, bioinformatics, and recommendation systems. In graph classification, the input is a graph, and the goal is to learn a classifier that can accurately predict the class of the graph.

( Image credit: Hierarchical Graph Pooling with Structure Learning )

Papers

Showing 801850 of 927 papers

TitleStatusHype
Exploiting Edge Features for Graph Neural Networks0
Discriminative structural graph classification0
Graph Neural Tangent Kernel: Fusing Graph Neural Networks with Graph KernelsCode0
Solving graph compression via optimal transport0
Strategies for Pre-training Graph Neural NetworksCode1
Provably Powerful Graph NetworksCode0
Edge Contraction Pooling for Graph Neural Networks0
Graph Filtration LearningCode0
Mutual Information Maximization in Graph Neural NetworksCode0
IPC: A Benchmark Data Set for Learning with Graph-Structured DataCode0
Function Space Pooling For Graph Convolutional Networks0
On Graph Classification Networks, Datasets and Baselines0
Graph U-NetsCode0
Are Powerful Graph Neural Nets Necessary? A Dissection on Graph ClassificationCode0
PiNet: A Permutation Invariant Graph Neural Network for Graph ClassificationCode0
Understanding Attention and Generalization in Graph Neural NetworksCode0
Learning Graph Neural Networks with Noisy Labels0
Graph Classification with Geometric Scattering0
DEEP GEOMETRICAL GRAPH CLASSIFICATION0
Capsule Graph Neural NetworkCode0
Graph Transformer0
Graph Convolutional Networks with EigenPoolingCode0
Graph Kernels: A Survey0
DDGK: Learning Graph Representations for Deep Divergence Graph KernelsCode0
PersLay: A Neural Network Layer for Persistence Diagrams and New Graph Topological SignaturesCode0
edGNN: a Simple and Powerful GNN for Directed Labeled GraphsCode0
Self-Attention Graph PoolingCode0
Semi-Supervised Graph Classification: A Hierarchical Graph PerspectiveCode0
SPI-GCN: A Simple Permutation-Invariant Graph Convolutional Network0
DeepGCNs: Can GCNs Go as Deep as CNNs?Code0
Learning Backtrackless Aligned-Spatial Graph Convolutional Networks for Graph Classification0
DAGCN: Dual Attention Graph Convolutional NetworksCode0
Rep the Set: Neural Networks for Learning Set RepresentationsCode0
Weisfeiler and Leman go sparse: Towards scalable higher-order graph embeddingsCode0
Quantum-based subgraph convolutional neural networks0
Unsupervised Inductive Graph-Level Representation Learning via Graph-Graph ProximityCode0
Clique pooling for graph classificationCode0
A Survey on Graph Kernels0
Subgraph Networks with Application to Structural Feature Space Expansion0
GNNExplainer: Generating Explanations for Graph Neural NetworksCode1
Relational Pooling for Graph RepresentationsCode0
Fast Graph Representation Learning with PyTorch GeometricCode1
Graph Kernels Based on Linear Patterns: Theoretical and Experimental ComparisonsCode0
Learning Vertex Convolutional Networks for Graph Classification0
Capsule Neural Networks for Graph Classification using Explicit Tensorial Graph Representations0
Fake News Detection on Social Media using Geometric Deep LearningCode1
Variational Recurrent Neural Networks for Graph ClassificationCode1
Propagation kernels: efficient graph kernels from propagated informationCode0
Graph Neural Networks with convolutional ARMA filtersCode0
Attentional Heterogeneous Graph Neural Network: Application to Program Reidentification0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1GIN-0Accuracy762Unverified
2HGP-SLAccuracy84.91Unverified
3rLap (unsupervised)Accuracy84.3Unverified
4TFGW ADJ (L=2)Accuracy82.9Unverified
5FIT-GNNAccuracy82.1Unverified
6DUGNNAccuracy81.7Unverified
7MEWISPoolAccuracy80.71Unverified
8CIN++Accuracy80.5Unverified
9MAGPoolAccuracy80.36Unverified
10SAEPoolAccuracy80.36Unverified
#ModelMetricClaimedVerifiedStatus
1Evolution of Graph ClassifiersAccuracy100Unverified
2MEWISPoolAccuracy96.66Unverified
3TFGW ADJ (L=2)Accuracy96.4Unverified
4GIUNetAccuracy95.7Unverified
5G_InceptionAccuracy95Unverified
6GICAccuracy94.44Unverified
7CIN++Accuracy94.4Unverified
8sGINAccuracy94.14Unverified
9CANAccuracy94.1Unverified
10Deep WL SGN(0,1,2)Accuracy93.68Unverified
#ModelMetricClaimedVerifiedStatus
1TFGW ADJ (L=2)Accuracy88.1Unverified
2WKPI-kmeansAccuracy87.2Unverified
3FGW wl h=4 spAccuracy86.42Unverified
4WL-OA KernelAccuracy86.1Unverified
5WL-OAAccuracy86.1Unverified
6FGW wl h=2 spAccuracy85.82Unverified
7WWLAccuracy85.75Unverified
8DUGNNAccuracy85.5Unverified
9δ-2-LWLAccuracy85.5Unverified
10CIN++Accuracy85.3Unverified