SOTAVerified

Graph Generation

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

Source: Graph Deconvolutional Generation

Papers

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Showing 241–250 of 712 papers

TitleStatusHype
Multi-Label Meta Weighting for Long-Tailed Dynamic Scene Graph GenerationCode0
NetGAN: Generating Graphs via Random WalksCode0
Factorizable Net: An Efficient Subgraph-based Framework for Scene Graph GenerationCode0
MIDGARD: Self-Consistency Using Minimum Description Length for Structured Commonsense ReasoningCode0
Extend, don’t rebuild: Phrasing conditional graph modification as autoregressive sequence labellingCode0
Let There Be Order: Rethinking Ordering in Autoregressive Graph GenerationCode0
LinkNet: Relational Embedding for Scene GraphCode0
Exploiting Long-Term Dependencies for Generating Dynamic Scene GraphsCode0
LLM Meets Scene Graph: Can Large Language Models Understand and Generate Scene Graphs? A Benchmark and Empirical StudyCode0
Explanation Graph Generation via Generative Pre-training over Synthetic GraphsCode0
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Benchmark Results

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