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 451–460 of 982 papers

TitleStatusHype
Self-supervised Graph Representation Learning for Black Market Account Detection—0
Graph Convolutional Neural Networks with Diverse Negative Samples via Decomposed Determinant Point ProcessesCode0
Coordinating Cross-modal Distillation for Molecular Property Prediction—0
Mitigating Relational Bias on Knowledge Graphs—0
Beyond Smoothing: Unsupervised Graph Representation Learning with Edge Heterophily DiscriminatingCode1
End-to-end Wind Turbine Wake Modelling with Deep Graph Representation Learning—0
Enhancing Intra-class Information Extraction for Heterophilous Graphs: One Neural Architecture Search Approach—0
RHCO: A Relation-aware Heterogeneous Graph Neural Network with Contrastive Learning for Large-scale GraphsCode1
Towards Generalizable Graph Contrastive Learning: An Information Theory Perspective—0
GRATIS: Deep Learning Graph Representation with Task-specific Topology and Multi-dimensional Edge FeaturesCode1
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Benchmark Results

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