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 261–270 of 712 papers

TitleStatusHype
Domain-wise Invariant Learning for Panoptic Scene Graph Generation—0
Adaptive Visual Scene Understanding: Incremental Scene Graph GenerationCode0
Less is More: Toward Zero-Shot Local Scene Graph Generation via Foundation ModelsCode1
Logical Bias Learning for Object Relation Prediction—0
Streamlining Attack Tree Generation: A Fragment-Based Approach—0
SANGEA: Scalable and Attributed Network Generation—0
Node-Aligned Graph-to-Graph (NAG2G): Elevating Template-Free Deep Learning Approaches in Single-Step RetrosynthesisCode1
Spatial-Temporal Knowledge-Embedded Transformer for Video Scene Graph GenerationCode1
Predicate Classification Using Optimal Transport Loss in Scene Graph Generation—0
Towards Debiasing Frame Length Bias in Text-Video Retrieval via Causal Intervention—0
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Benchmark Results

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