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 251–260 of 712 papers

TitleStatusHype
Towards a Unified Transformer-based Framework for Scene Graph Generation and Human-object Interaction Detection—0
Semantic Scene Graph Generation Based on an Edge Dual Scene Graph and Message Passing Neural Network—0
Form follows Function: Text-to-Text Conditional Graph Generation based on Functional Requirements—0
Diversified Node Sampling based Hierarchical Transformer Pooling for Graph Representation Learning—0
SQLformer: Deep Auto-Regressive Query Graph Generation for Text-to-SQL TranslationCode0
FloCoDe: Unbiased Dynamic Scene Graph Generation with Temporal Consistency and Correlation Debiasing—0
EDGE++: Improved Training and Sampling of EDGE—0
GraphMaker: Can Diffusion Models Generate Large Attributed Graphs?Code1
VidCoM: Fast Video Comprehension through Large Language Models with Multimodal Tools—0
TextPSG: Panoptic Scene Graph Generation from Textual Descriptions—0
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Benchmark Results

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