SOTAVerified

Graph Generation

Graph Generation is an important research area with significant applications in drug and material designs.

Source: Graph Deconvolutional Generation

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 621–630 of 712 papers

TitleStatusHype
Pre-Training on Dynamic Graph Neural NetworksCode0
Addressing the Impact of Localized Training Data in Graph Neural NetworksCode0
WikiGraphs: A Wikipedia Text - Knowledge Graph Paired DatasetCode0
Multi-Label Meta Weighting for Long-Tailed Dynamic Scene Graph GenerationCode0
Multi-Class and Multi-Task Strategies for Neural Directed Link PredictionCode0
MIDGARD: Self-Consistency Using Minimum Description Length for Structured Commonsense ReasoningCode0
MALOnt: An Ontology for Malware Threat IntelligenceCode0
Fine-Grained is Too Coarse: A Novel Data-Centric Approach for Efficient Scene Graph GenerationCode0
Federated Voxel Scene Graph for Intracranial HemorrhageCode0
Factorizable Net: An Efficient Subgraph-based Framework for Scene Graph GenerationCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1RNNStreetMover0.03—Unverified
2GraphRNNStreetMover0.02—Unverified
3GGT without CAStreetMover0.02—Unverified
4GGTStreetMover0.02—Unverified