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 681–690 of 982 papers

TitleStatusHype
GRAPE for Fast and Scalable Graph Processing and random walk-based EmbeddingCode1
GCN-SE: Attention as Explainability for Node Classification in Dynamic Graphs—0
Pre-training Molecular Graph Representation with 3D GeometryCode1
Relation Prediction as an Auxiliary Training Objective for Improving Multi-Relational Graph RepresentationsCode1
Cycle Representation Learning for Inductive Relation PredictionCode0
Revisiting SVD to generate powerful Node Embeddings for Recommendation Systems—0
Wireless Link Scheduling via Graph Representation Learning: A Comparative Study of Different Supervision LevelsCode0
Graph Representation Learning for Spatial Image Steganalysis—0
Reconstruction for Powerful Graph Representations—0
Learning Compact Representations of Neural Networks using DiscriminAtive Masking (DAM)Code1
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Benchmark Results

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