SOTAVerified

Graph Classification

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 )

Papers

Showing 110 of 927 papers

TitleStatusHype
Density-aware Walks for Coordinated Campaign DetectionCode0
Positional Encoding meets Persistent Homology on GraphsCode0
Weisfeiler and Leman Follow the Arrow of Time: Expressive Power of Message Passing in Temporal Event Graphs0
Improving the Effective Receptive Field of Message-Passing Neural NetworksCode1
Graph Style Transfer for Counterfactual ExplainabilityCode0
Scalable Graph Generative Modeling via Substructure SequencesCode0
Addressing the Scarcity of Benchmarks for Graph XAICode0
Schreier-Coset Graph Propagation0
Efficient Mixed Precision Quantization in Graph Neural NetworksCode0
Rhomboid Tiling for Geometric Graph Deep Learning0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1WKPI-kcentersAccuracy87.3Unverified
2WL-OAAccuracy86.3Unverified
3δ-2-LWLAccuracy84.7Unverified
4CIN++Accuracy84.5Unverified
5PINAccuracy84Unverified
6Spec-GNAccuracy83.62Unverified
7CANAccuracy83.6Unverified
8Propagation kernels (pk)Accuracy83.5Unverified
9GICAccuracy82.86Unverified
10PPGNAccuracy82.23Unverified