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 751800 of 927 papers

TitleStatusHype
A Hierarchy of Graph Neural Networks Based on Learnable Local Features0
Composition-based Multi-Relational Graph Convolutional NetworksCode1
Fundamental Limits of Deep Graph Convolutional Networks0
Bayesian Graph Convolutional Neural Networks Using Non-Parametric Graph Learning0
Understanding Isomorphism Bias in Graph Data SetsCode0
Hierarchical Representation Learning in Graph Neural Networks with Node Decimation PoolingCode0
Deep Learning for Molecular Graphs with Tiered Graph Autoencoders and Graph Prediction0
Temporal Graph Kernels for Classifying Dissemination Processes0
Rethinking Kernel Methods for Node Representation Learning on GraphsCode0
Evolution of Graph ClassifiersCode0
Graph-Hist: Graph Classification from Latent Feature Histograms With Application to Bot Detection0
AttPool: Towards Hierarchical Feature Representation in Graph Convolutional Networks via Attention MechanismCode0
Graph-Preserving Grid Layout: A Simple Graph Drawing Method for Graph Classification using CNNs0
Universal Graph Transformer Self-Attention NetworksCode0
Subgraph Attention for Node Classification and Hierarchical Graph Pooling0
How can we generalise learning distributed representations of graphs?0
HaarPooling: Graph Pooling with Compressive Haar Basis0
Diagonal Graph Convolutional Networks with Adaptive Neighborhood Aggregation0
Topology-Aware Pooling via Graph Attention0
Unsupervised Hierarchical Graph Representation Learning with Variational Bayes0
Ordinary differential equations on graph networks0
Empowering Graph Representation Learning with Paired Training and Graph Co-Attention0
Unsupervised Universal Self-Attention Network for Graph Classification0
Mincut Pooling in Graph Neural Networks0
Haar Graph PoolingCode0
Learning Universal Graph Neural Network Embeddings With Aid Of Transfer LearningCode0
A Non-Negative Factorization approach to node pooling in Graph Convolutional Neural Networks0
NEAR: Neighborhood Edge AggregatoR for Graph Classification0
Deep Weisfeiler-Lehman Assignment Kernels via Multiple Kernel Learning0
Invariant embedding for graph classification0
CensNet: Convolution with Edge-Node Switching in Graph Neural Networks0
HATS: A Hierarchical Graph Attention Network for Stock Movement PredictionCode0
Graph Neural Networks for Small Graph and Giant Network Representation Learning: An Overview0
InfoGraph: Unsupervised and Semi-supervised Graph-Level Representation Learning via Mutual Information MaximizationCode1
IsoNN: Isomorphic Neural Network for Graph Representation Learning and ClassificationCode0
Topology Based Scalable Graph Kernels0
k-hop Graph Neural NetworksCode0
Semi-Supervised Graph Embedding for Multi-Label Graph Node Classification0
Label-Aware Graph Convolutional Networks0
Graph Representation Learning via Hard and Channel-Wise Attention NetworksCode0
Improving Attention Mechanism in Graph Neural Networks via Cardinality PreservationCode0
iPool -- Information-based Pooling in Hierarchical Graph Neural Networks0
Spectral Clustering with Graph Neural Networks for Graph PoolingCode1
Graph Star Net for Generalized Multi-Task LearningCode0
Attacking Graph Convolutional Networks via Rewiring0
A Persistent Weisfeiler–Lehman Procedure for Graph ClassificationCode0
Labeled Graph Generative Adversarial Networks0
DEMO-Net: Degree-specific Graph Neural Networks for Node and Graph ClassificationCode0
GOT: An Optimal Transport framework for Graph comparisonCode1
Wasserstein Weisfeiler-Lehman Graph KernelsCode0
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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