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 671–680 of 982 papers

TitleStatusHype
Learning with Capsules: A Survey—0
A knowledge graph representation learning approach to predict novel kinase-substrate interactionsCode0
An Empirical Study of Retrieval-enhanced Graph Neural NetworksCode0
Omni-Granular Ego-Semantic Propagation for Self-Supervised Graph Representation Learning—0
Embedding Graphs on Grassmann ManifoldCode0
GraphPMU: Event Clustering via Graph Representation Learning Using Locationally-Scarce Distribution-Level Fundamental and Harmonic PMU Measurements—0
KQGC: Knowledge Graph Embedding with Smoothing Effects of Graph Convolutions for Recommendation—0
Revisiting the role of heterophily in graph representation learning: An edge classification perspective—0
Are Graph Representation Learning Methods Robust to Graph Sparsity and Asymmetric Node Information?—0
Poincaré Heterogeneous Graph Neural Networks for Sequential Recommendation—0
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Benchmark Results

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