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 376–400 of 712 papers

TitleStatusHype
Contextual Associated Triplet Queries for Panoptic Scene Graph Generation—0
CLIP-Driven Open-Vocabulary 3D Scene Graph Generation via Cross-Modality Contrastive Learning—0
ALF: Adaptive Label Finetuning for Scene Graph Generation—0
Replica Tree-based Federated Learning using Limited DataCode0
TSPP: A Unified Benchmarking Tool for Time-series ForecastingCode0
Swap-based Deep Reinforcement Learning for Facility Location Problems in Networks—0
Indoor and Outdoor 3D Scene Graph Generation via Language-Enabled Spatial Ontologies—0
Collect and Connect Data Leaves to Feature Concepts: Interactive Graph Generation Toward Well-being—0
GPT4SGG: Synthesizing Scene Graphs from Holistic and Region-specific NarrativesCode0
HIG: Hierarchical Interlacement Graph Approach to Scene Graph Generation in Video Understanding—0
HAtt-Flow: Hierarchical Attention-Flow Mechanism for Group Activity Scene Graph Generation in Videos—0
RetroDiff: Retrosynthesis as Multi-stage Distribution Interpolation—0
Exploring Graph Classification Techniques Under Low Data Constraints: A Comprehensive Study—0
Two Stream Scene Understanding on Graph Embedding—0
Identification of vortex in unstructured mesh with graph neural networks—0
Generative Explanations for Graph Neural Network: Methods and Evaluations—0
Finding Increasingly Large Extremal Graphs with AlphaZero and Tabu Search—0
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
VidCoM: Fast Video Comprehension through Large Language Models with Multimodal Tools—0
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Benchmark Results

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