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 531–540 of 712 papers

TitleStatusHype
Tackling the Challenges in Scene Graph Generation with Local-to-Global InteractionsCode1
Order Matters: Probabilistic Modeling of Node Sequence for Graph GenerationCode1
Realistic molecule optimization on a learned graph manifold—0
Multiresolution Equivariant Graph Variational AutoencoderCode1
Linguistic Structures as Weak Supervision for Visual Scene Graph GenerationCode1
iTelos -- Purpose Driven Knowledge Graph Generation—0
Survey of Visual-Semantic Embedding Methods for Zero-Shot Image Retrieval—0
Brain Multigraph Prediction using Topology-Aware Adversarial Graph Neural NetworkCode0
VersaGNN: a Versatile accelerator for Graph neural networks—0
Recovering Barabási-Albert Parameters of Graphs through DisentanglementCode0
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Benchmark Results

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