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
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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
Graph data augmentation with Gromow-Wasserstein Barycenters
This is primarily due to the complex and non-Euclidean nature of graph data.
SSHPool: The Separated Subgraph-based Hierarchical Pooling
To this end, we commence by assigning the nodes of a sample graph into different clusters, resulting in a family of separated subgraphs.
AKBR: Learning Adaptive Kernel-based Representations for Graph Classification
In this paper, we propose a new model to learn Adaptive Kernel-based Representations (AKBR) for graph classification.
GTAGCN: Generalized Topology Adaptive Graph Convolutional Networks
Graph Neural Networks (GNN) have emerged as a popular and standard approach for learning from graph-structured data.
Molecular Classification Using Hyperdimensional Graph Classification
Our work introduces an innovative approach to graph learning by leveraging Hyperdimensional Computing.
Generation is better than Modification: Combating High Class Homophily Variance in Graph Anomaly Detection
We find that in graph anomaly detection, the homophily distribution differences between different classes are significantly greater than those in homophilic and heterophilic graphs.
A Differential Geometric View and Explainability of GNN on Evolving Graphs
Graphs are ubiquitous in social networks and biochemistry, where Graph Neural Networks (GNN) are the state-of-the-art models for prediction.
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.
