Graph Classification
382 papers with code • 65 benchmarks • 46 datasets
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 )
Benchmarks
These leaderboards are used to track progress in Graph Classification
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Libraries
Use these libraries to find Graph Classification models and implementations
CIFAR-10
MNIST
Pubmed
OGB
Reddit
Cora
Latest papers with no code
Hypergraph-enhanced Dual Semi-supervised Graph Classification
In this paper, we study semi-supervised graph classification, which aims at accurately predicting the categories of graphs in scenarios with limited labeled graphs and abundant unlabeled graphs.
Imbalanced Graph Classification with Multi-scale Oversampling Graph Neural Networks
Extensive experiments on 16 imbalanced graph datasets show that MOSGNN i) significantly outperforms five state-of-the-art models, and ii) offers a generic framework, in which different advanced imbalanced learning loss functions can be easily plugged in and obtain significantly improved classification performance.
Conditional Local Feature Encoding for Graph Neural Networks
The key mechanism of current GNNs is message passing, where a node's feature is updated based on the information passing from its local neighbourhood.
Transductive Spiking Graph Neural Networks for Loihi
We showcase the performance benefits of combining neuromorphic Bayesian optimization with our approach for citation graph classification using fixed-precision spiking neurons.
One Subgraph for All: Efficient Reasoning on Opening Subgraphs for Inductive Knowledge Graph Completion
Knowledge Graph Completion (KGC) has garnered massive research interest recently, and most existing methods are designed following a transductive setting where all entities are observed during training.
SPGNN: Recognizing Salient Subgraph Patterns via Enhanced Graph Convolution and Pooling
We propose a novel Subgraph Pattern GNN (SPGNN) architecture that incorporates these enhancements.
CKGConv: General Graph Convolution with Continuous Kernels
The existing definitions of graph convolution, either from spatial or spectral perspectives, are inflexible and not unified.
A Clean-graph Backdoor Attack against Graph Convolutional Networks with Poisoned Label Only
In order to explore the backdoor vulnerability of GCNs and create a more practical and stealthy backdoor attack method, this paper proposes a clean-graph backdoor attack against GCNs (CBAG) in the node classification task, which only poisons the training labels without any modification to the training samples, revealing that GCNs have this security vulnerability.
Multi-view Graph Structural Representation Learning via Graph Coarsening
Specifically, we build three unique views, original, coarsening, and conversion, to learn a thorough structural representation.
Graph data augmentation with Gromow-Wasserstein Barycenters
This is primarily due to the complex and non-Euclidean nature of graph data.
