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 591–600 of 712 papers

TitleStatusHype
Visual Semantic Information Pursuit: A Survey—0
Visual Traffic Knowledge Graph Generation from Scene Images—0
What can Off-the-Shelves Large Multi-Modal Models do for Dynamic Scene Graph Generation?—0
Will More Expressive Graph Neural Networks do Better on Generative Tasks?—0
Out-of-Distribution Graph Models Merging—0
Overcoming Order in Autoregressive Graph Generation—0
Overview of the Third Workshop on Scholarly Document Processing—0
What Do LLMs Need to Understand Graphs: A Survey of Parametric Representation of Graphs—0
Optimized Crystallographic Graph Generation for Material ScienceCode0
GCAL: Adapting Graph Models to Evolving Domain ShiftsCode0
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Benchmark Results

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