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
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
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
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
Wasserstein Weisfeiler-Lehman Graph KernelsCode0
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
Provably Powerful Graph NetworksCode0
Graph Filtration LearningCode0
Edge Contraction Pooling for Graph Neural Networks0
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
Are Powerful Graph Neural Nets Necessary? A Dissection on Graph ClassificationCode0
Graph U-NetsCode0
Understanding Attention and Generalization in Graph Neural NetworksCode0
PiNet: A Permutation Invariant Graph Neural Network for Graph ClassificationCode0
Learning Graph Neural Networks with Noisy Labels0
Capsule Graph Neural NetworkCode0
Graph Classification with Geometric Scattering0
Graph Transformer0
DEEP GEOMETRICAL GRAPH CLASSIFICATION0
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
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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
8δ-2-LWLAccuracy85.5Unverified
9DUGNNAccuracy85.5Unverified
10CIN++Accuracy85.3Unverified