Graph Classification
381 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
Learning Attributed Graphlets: Predictive Graph Mining by Graphlets with Trainable Attribute
LAGRA learns importance weights for small attributed subgraphs, called attributed graphlets (AGs), while simultaneously optimizing their attribute vectors.
Flexible infinite-width graph convolutional networks and the importance of representation learning
A common theoretical approach to understanding neural networks is to take an infinite-width limit, at which point the outputs become Gaussian process (GP) distributed.
A Graph is Worth $K$ Words: Euclideanizing Graph using Pure Transformer
Despite recent GNN and Graphformer efforts encoding graphs as Euclidean vectors, recovering original graph from the vectors remains a challenge.
Neural Scaling Laws on Graphs
In this work, we delve into neural scaling laws on graphs from both model and data perspectives.
Topology-Informed Graph Transformer
Transformers have revolutionized performance in Natural Language Processing and Vision, paving the way for their integration with Graph Neural Networks (GNNs).
Graph Transformers without Positional Encodings
Recently, Transformers for graph representation learning have become increasingly popular, achieving state-of-the-art performance on a wide-variety of datasets, either alone or in combination with message-passing graph neural networks (MP-GNNs).
GPS: Graph Contrastive Learning via Multi-scale Augmented Views from Adversarial Pooling
Self-supervised graph representation learning has recently shown considerable promise in a range of fields, including bioinformatics and social networks.
Towards Causal Classification: A Comprehensive Study on Graph Neural Networks
The exploration of Graph Neural Networks (GNNs) for processing graph-structured data has expanded, particularly their potential for causal analysis due to their universal approximation capabilities.
EMP: Effective Multidimensional Persistence for Graph Representation Learning
This framework empowers the exploration of data by simultaneously varying multiple scale parameters.
Through the Dual-Prism: A Spectral Perspective on Graph Data Augmentation for Graph Classification
Graph Neural Networks (GNNs) have become the preferred tool to process graph data, with their efficacy being boosted through graph data augmentation techniques.
