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 611–620 of 712 papers

TitleStatusHype
Towards Fast Graph Generation via Autoregressive Noisy Filtration ModelingCode0
Narrative-of-Thought: Improving Temporal Reasoning of Large Language Models via Recounted NarrativesCode0
Pixels to Graphs by Associative EmbeddingCode0
Pixel-wise Graph Attention Networks for Person Re-identificationCode0
AgraSSt: Approximate Graph Stein Statistics for Interpretable Assessment of Implicit Graph GeneratorsCode0
A Scalable AutoML Approach Based on Graph Neural NetworksCode0
Situational Scene Graph for Structured Human-centric Situation UnderstandingCode0
Heuristic Semi-Supervised Learning for Graph Generation Inspired by Electoral CollegeCode0
Skew Class-balanced Re-weighting for Unbiased Scene Graph GenerationCode0
Fine-Grained Scene Graph Generation via Sample-Level Bias PredictionCode0
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Benchmark Results

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