Graph Classification
380 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
HDReason: Algorithm-Hardware Codesign for Hyperdimensional Knowledge Graph Reasoning
When conducting cross-models and cross-platforms comparison, HDReason yields an average 4. 2x higher performance and 3. 4x better energy efficiency with similar accuracy versus the state-of-the-art FPGA-based GCN training platform.
Multi-Scale Subgraph Contrastive Learning
By an experimental analysis, we discover the semantic information of an augmented graph structure may be not consistent as original graph structure, and whether two augmented graphs are positive or negative pairs is highly related with the multi-scale structures.
Verifying message-passing neural networks via topology-based bounds tightening
Since graph neural networks (GNNs) are often vulnerable to attack, we need to know when we can trust them.
Inductive Graph Alignment Prompt: Bridging the Gap between Graph Pre-training and Inductive Fine-tuning From Spectral Perspective
Based on the insight of graph pre-training, we propose to bridge the graph signal gap and the graph structure gap with learnable prompts in the spectral space.
Class-Balanced and Reinforced Active Learning on Graphs
It learns an optimal policy to acquire class-balanced and informative nodes for annotation, maximizing the performance of GNNs trained with selected labeled nodes.
Message Detouring: A Simple Yet Effective Cycle Representation for Expressive Graph Learning
Graph learning is crucial in the fields of bioinformatics, social networks, and chemicals.
Generalization Error of Graph Neural Networks in the Mean-field Regime
This work provides a theoretical framework for assessing the generalization error of graph classification tasks via graph neural networks in the over-parameterized regime, where the number of parameters surpasses the quantity of data points.
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.
Gaussian process regression with Sliced Wasserstein Weisfeiler-Lehman graph kernels
Supervised learning has recently garnered significant attention in the field of computational physics due to its ability to effectively extract complex patterns for tasks like solving partial differential equations, or predicting material properties.
