Graph Classification
379 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
| Trend | Dataset | Best Model | Paper | Code | Compare |
|---|---|---|---|---|---|
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
Libraries
Use these libraries to find Graph Classification models and implementations
CIFAR-10
MNIST
Pubmed
OGB
Reddit
Cora
Latest papers
Harnessing the Power of Large Language Model for Uncertainty Aware Graph Processing
code4paper-2024/code4paper
•
•
To equip the graph processing with both high accuracy and explainability, we introduce a novel approach that harnesses the power of a large language model (LLM), enhanced by an uncertainty-aware module to provide a confidence score on the generated answer.
Learning the mechanisms of network growth
We propose a novel model-selection method for dynamic real-life networks.
Cooperative Classification and Rationalization for Graph Generalization
To address these challenges, in this paper, we propose a Cooperative Classification and Rationalization (C2R) method, consisting of the classification and the rationalization module.
Graph Parsing Networks
lumia-group/graphparsingnetworks
•
•
GPN benefits from the discrete assignments generated by the graph parsing algorithm, allowing good memory efficiency while preserving node information intact.
G-Retriever: Retrieval-Augmented Generation for Textual Graph Understanding and Question Answering
Given a graph with textual attributes, we enable users to `chat with their graph': that is, to ask questions about the graph using a conversational interface.
Graph Contrastive Learning with Cohesive Subgraph Awareness
However, such stochastic augmentations may severely damage the intrinsic properties of a graph and deteriorate the following representation learning process.
Tensor-view Topological Graph Neural Network
Graph classification is an important learning task for graph-structured data.
GNN-LoFI: a Novel Graph Neural Network through Localized Feature-based Histogram Intersection
gdl-unive/histogram-intersection-kernel
•
•
Graph neural networks are increasingly becoming the framework of choice for graph-based machine learning.
On the Power of Graph Neural Networks and Feature Augmentation Strategies to Classify Social Networks
walidgeuttala/Synthetic-Benchmark-for-Graph-Classification
•
•
The generalisation ability of these models is also analysed using a second synthetic network dataset (containing networks of different sizes). Our results point towards the balanced importance of the computational power of the GNN architecture and the the information level provided by the artificial features.
View-based Explanations for Graph Neural Networks
zju-daily/gvex
•
•
Existing approaches aim to understand the overall results of GNNs rather than providing explanations for specific class labels of interest, and may return explanation structures that are hard to access, nor directly queryable. We propose GVEX, a novel paradigm that generates Graph Views for EXplanation.
