SOTAVerified

Graph Representation Learning

The goal of Graph Representation Learning is to construct a set of features (‘embeddings’) representing the structure of the graph and the data thereon. We can distinguish among Node-wise embeddings, representing each node of the graph, Edge-wise embeddings, representing each edge in the graph, and Graph-wise embeddings representing the graph as a whole.

Source: SIGN: Scalable Inception Graph Neural Networks

Papers

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Showing 731–740 of 982 papers

TitleStatusHype
Graph Representation Learning via Contrasting Cluster Assignments—0
Graph Representation Learning via Multi-task Knowledge Distillation—0
Graph Representation Learning with Individualization and Refinement—0
Graph Representation Learning with Diffusion Generative Models—0
Graph sampling for node embedding—0
Graph Self-Contrast Representation Learning—0
Graph Transformer GANs with Graph Masked Modeling for Architectural Layout Generation—0
Graph Transformers without Positional Encodings—0
GraphVAMPNet, using graph neural networks and variational approach to markov processes for dynamical modeling of biomolecules—0
GRE^2-MDCL: Graph Representation Embedding Enhanced via Multidimensional Contrastive Learning—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Pi-net-linearError (mm)0.47—Unverified