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
Learning Deep Graph Representations via Convolutional Neural NetworksCode0
A Collective Learning Framework to Boost GNN Expressiveness0
Overcoming Catastrophic Forgetting in Graph Neural Networks with Experience Replay0
Adaptive-Step Graph Meta-Learner for Few-Shot Graph Classification0
Unsupervised Hierarchical Graph Representation Learning by Mutual Information MaximizationCode0
Wasserstein-based Graph Alignment0
Ego-based Entropy Measures for Structural Representations0
An End-to-End Graph Convolutional Kernel Support Vector Machine0
Distance Metric Learning for Graph Structured DataCode0
GL2vec: Graph Embedding Enriched by Line Graphs with Edge Features0
Graph Attentional Autoencoder for Anticancer Hyperfood Prediction0
Understanding the Power of Persistence Pairing via Permutation Test0
Unsupervised Learning of Graph Hierarchical Abstractions with Differentiable Coarsening and Optimal TransportCode0
Multi-Channel Graph Convolutional Networks0
Zoom in to where it matters: a hierarchical graph based model for mammogram analysis0
Coloring graph neural networks for node disambiguation0
Bayesian Hyperparameter Optimization with BoTorch, GPyTorch and Ax0
Sparse Graph Attention NetworksCode0
Rethinking the Item Order in Session-based Recommendation with Graph Neural NetworksCode0
Independence Promoted Graph Disentangled Networks0
Scalable Global Alignment Graph Kernel Using Random Features: From Node Embedding to Graph Embedding0
ASAP: Adaptive Structure Aware Pooling for Learning Hierarchical Graph RepresentationsCode0
A Hierarchy of Graph Neural Networks Based on Learnable Local Features0
Fundamental Limits of Deep Graph Convolutional Networks0
Understanding Isomorphism Bias in Graph Data SetsCode0
Bayesian Graph Convolutional Neural Networks Using Non-Parametric Graph Learning0
Deep Learning for Molecular Graphs with Tiered Graph Autoencoders and Graph Prediction0
Hierarchical Representation Learning in Graph Neural Networks with Node Decimation PoolingCode0
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
Universal Graph Transformer Self-Attention NetworksCode0
Graph-Preserving Grid Layout: A Simple Graph Drawing Method for Graph Classification using CNNs0
Ordinary differential equations on graph networks0
How can we generalise learning distributed representations of graphs?0
HaarPooling: Graph Pooling with Compressive Haar Basis0
Haar Graph PoolingCode0
Diagonal Graph Convolutional Networks with Adaptive Neighborhood Aggregation0
Unsupervised Universal Self-Attention Network for Graph Classification0
Subgraph Attention for Node Classification and Hierarchical Graph Pooling0
Empowering Graph Representation Learning with Paired Training and Graph Co-Attention0
Unsupervised Hierarchical Graph Representation Learning with Variational Bayes0
Topology-Aware Pooling via Graph Attention0
Mincut Pooling in Graph Neural Networks0
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
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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