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

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Showing 101–150 of 927 papers

TitleStatusHype
Relaxing Graph Transformers for Adversarial Attacks—0
Molecular Topological Profile (MOLTOP) -- Simple and Strong Baseline for Molecular Graph ClassificationCode0
Graph Structure Prompt Learning: A Novel Methodology to Improve Performance of Graph Neural Networks—0
HyperAggregation: Aggregating over Graph Edges with HypernetworksCode0
Tackling Oversmoothing in GNN via Graph Sparsification: A Truss-based Approach—0
Rethinking the Effectiveness of Graph Classification Datasets in Benchmarks for Assessing GNNsCode0
Unveiling Global Interactive Patterns across Graphs: Towards Interpretable Graph Neural NetworksCode0
Core Knowledge Learning Framework for Graph Adaptation and Scalability Learning—0
MuGSI: Distilling GNNs with Multi-Granularity Structural Information for Graph ClassificationCode0
KAGNNs: Kolmogorov-Arnold Networks meet Graph LearningCode2
Kolmogorov-Arnold Graph Neural Networks—0
TopoGCL: Topological Graph Contrastive LearningCode0
Next Level Message-Passing with Hierarchical Support GraphsCode0
Fast Tree-Field Integrators: From Low Displacement Rank to Topological TransformersCode0
Edge Classification on Graphs: New Directions in Topological ImbalanceCode0
On GNN explanability with activation rules—0
Robustness Inspired Graph Backdoor Defense—0
Motif-driven Subgraph Structure Learning for Graph ClassificationCode0
A Comprehensive Graph Pooling Benchmark: Effectiveness, Robustness and GeneralizabilityCode1
How Interpretable Are Interpretable Graph Neural Networks?Code1
Rethinking the impact of noisy labels in graph classification: A utility and privacy perspective—0
GENIE: Watermarking Graph Neural Networks for Link Prediction—0
GNNAnatomy: Rethinking Model-Level Explanations for Graph Neural Networks—0
PANDA: Expanded Width-Aware Message Passing Beyond RewiringCode1
Learning Long Range Dependencies on Graphs via Random WalksCode1
GEFL: Extended Filtration Learning for Graph ClassificationCode0
Equivariant Machine Learning on Graphs with Nonlinear Spectral Filters—0
Towards a General Recipe for Combinatorial Optimization with Multi-Filter GNNsCode0
CiliaGraph: Enabling Expression-enhanced Hyper-Dimensional Computation in Ultra-Lightweight and One-Shot Graph Classification on Edge—0
Spatio-Spectral Graph Neural NetworksCode1
A Canonicalization Perspective on Invariant and Equivariant LearningCode0
Cross-Context Backdoor Attacks against Graph Prompt LearningCode0
Node Identifiers: Compact, Discrete Representations for Efficient Graph LearningCode1
HC-GAE: The Hierarchical Cluster-based Graph Auto-Encoder for Graph Representation Learning—0
Harnessing Collective Structure Knowledge in Data Augmentation for Graph Neural NetworksCode0
ENADPool: The Edge-Node Attention-based Differentiable Pooling for Graph Neural Networks—0
Perception-Inspired Graph Convolution for Music Understanding TasksCode1
Towards Subgraph Isomorphism Counting with Graph Kernels—0
Dynamic GNNs for Precise Seizure Detection and Classification from EEG DataCode2
Imbalanced Graph Classification with Multi-scale Oversampling Graph Neural NetworksCode0
Conditional Local Feature Encoding for Graph Neural Networks—0
Hypergraph-enhanced Dual Semi-supervised Graph Classification—0
Are Graph Embeddings the Panacea? An Empirical Survey from the Data Fitness PerspectiveCode0
Transductive Spiking Graph Neural Networks for Loihi—0
One Subgraph for All: Efficient Reasoning on Opening Subgraphs for Inductive Knowledge Graph Completion—0
Gradformer: Graph Transformer with Exponential DecayCode2
CKGConv: General Graph Convolution with Continuous KernelsCode1
SPGNN: Recognizing Salient Subgraph Patterns via Enhanced Graph Convolution and Pooling—0
A Clean-graph Backdoor Attack against Graph Convolutional Networks with Poisoned Label Only—0
An Efficient Loop and Clique Coarsening Algorithm for Graph ClassificationCode0
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All datasetsPROTEINSMUTAGNCI1D&DENZYMESIMDb-BCOLLABPTCIMDb-MNCI109CIFAR10 100kREDDIT-B

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1GIN-0Accuracy762—Unverified
2HGP-SLAccuracy84.91—Unverified
3rLap (unsupervised)Accuracy84.3—Unverified
4TFGW ADJ (L=2)Accuracy82.9—Unverified
5FIT-GNNAccuracy82.1—Unverified
6DUGNNAccuracy81.7—Unverified
7MEWISPoolAccuracy80.71—Unverified
8CIN++Accuracy80.5—Unverified
9MAGPoolAccuracy80.36—Unverified
10SAEPoolAccuracy80.36—Unverified
#ModelMetricClaimedVerifiedStatus
1Evolution of Graph ClassifiersAccuracy100—Unverified
2MEWISPoolAccuracy96.66—Unverified
3TFGW ADJ (L=2)Accuracy96.4—Unverified
4GIUNetAccuracy95.7—Unverified
5G_InceptionAccuracy95—Unverified
6GICAccuracy94.44—Unverified
7CIN++Accuracy94.4—Unverified
8sGINAccuracy94.14—Unverified
9CANAccuracy94.1—Unverified
10Deep WL SGN(0,1,2)Accuracy93.68—Unverified
#ModelMetricClaimedVerifiedStatus
1TFGW ADJ (L=2)Accuracy88.1—Unverified
2WKPI-kmeansAccuracy87.2—Unverified
3FGW wl h=4 spAccuracy86.42—Unverified
4WL-OA KernelAccuracy86.1—Unverified
5WL-OAAccuracy86.1—Unverified
6FGW wl h=2 spAccuracy85.82—Unverified
7WWLAccuracy85.75—Unverified
8DUGNNAccuracy85.5—Unverified
9δ-2-LWLAccuracy85.5—Unverified
10CIN++Accuracy85.3—Unverified