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 471–480 of 712 papers

TitleStatusHype
DIG: A Turnkey Library for Diving into Graph Deep Learning Research—0
Discovering Graph Generation Algorithms—0
Disentangled Spatiotemporal Graph Generative Models—0
Disentangling Interpretable Generative Parameters of Random and Real-World Graphs—0
Disentangling Node Attributes from Graph Topology for Improved Generalizability in Link Prediction—0
Distributed Training of Embeddings using Graph Analytics—0
Diversified Node Sampling based Hierarchical Transformer Pooling for Graph Representation Learning—0
Do Graph Diffusion Models Accurately Capture and Generate Substructure Distributions?—0
Domain-wise Invariant Learning for Panoptic Scene Graph Generation—0
Doubly Reparameterized Importance Weighted Structure Learning for Scene Graph Generation—0
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Benchmark Results

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