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 101–110 of 712 papers

TitleStatusHype
Situational Scene Graph for Structured Human-centric Situation UnderstandingCode0
Semantic Enrichment of the Quantum Cascade Laser Properties in Text- A Knowledge Graph Generation ApproachCode0
Mitigating Graph Covariate Shift via Score-based Out-of-distribution Augmentation—0
SeaDAG: Semi-autoregressive Diffusion for Conditional Directed Acyclic Graph Generation—0
Scene Graph Generation with Role-Playing Large Language ModelsCode1
GeSubNet: Gene Interaction Inference for Disease Subtype Network Generation—0
What Do LLMs Need to Understand Graphs: A Survey of Parametric Representation of Graphs—0
Heterogeneous Graph Generation: A Hierarchical Approach using Node Feature Pooling—0
GUISE: Graph GaUssIan Shading watErmark—0
LLM-Based Multi-Agent Systems are Scalable Graph Generative ModelsCode2
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Benchmark Results

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