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

Showing 91100 of 982 papers

TitleStatusHype
RHCO: A Relation-aware Heterogeneous Graph Neural Network with Contrastive Learning for Large-scale GraphsCode1
GRATIS: Deep Learning Graph Representation with Task-specific Topology and Multi-dimensional Edge FeaturesCode1
Implicit Graphon Neural RepresentationCode1
Unleashing the Power of Graph Data Augmentation on Covariate Distribution ShiftCode1
Geometry-Complete Perceptron Networks for 3D Molecular GraphsCode1
PAGE: Prototype-Based Model-Level Explanations for Graph Neural NetworksCode1
Multi-dimensional Edge-based Audio Event Relational Graph Representation Learning for Acoustic Scene ClassificationCode1
Transformers over Directed Acyclic GraphsCode1
DyTed: Disentangled Representation Learning for Discrete-time Dynamic GraphCode1
Unifying Graph Contrastive Learning with Flexible Contextual ScopesCode1
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Benchmark Results

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