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 631–640 of 712 papers

TitleStatusHype
LTLBench: Towards Benchmarks for Evaluating Temporal Logic Reasoning in Large Language ModelsCode0
Extend, don’t rebuild: Phrasing conditional graph modification as autoregressive sequence labellingCode0
LLM Meets Scene Graph: Can Large Language Models Understand and Generate Scene Graphs? A Benchmark and Empirical StudyCode0
RBA-GCN: Relational Bilevel Aggregation Graph Convolutional Network for Emotion RecognitionCode0
SQLformer: Deep Auto-Regressive Query Graph Generation for Text-to-SQL TranslationCode0
Brain Multigraph Prediction using Topology-Aware Adversarial Graph Neural NetworkCode0
LinkNet: Relational Embedding for Scene GraphCode0
Exploiting Long-Term Dependencies for Generating Dynamic Scene GraphsCode0
Recovering Barabási-Albert Parameters of Graphs through DisentanglementCode0
ReFormer: The Relational Transformer for Image CaptioningCode0
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Benchmark Results

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