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 871880 of 982 papers

TitleStatusHype
ConCur: Self-supervised graph representation based on contrastive learning with curriculum negative samplingCode0
Graph-based Incident Aggregation for Large-Scale Online Service SystemsCode0
A Hierarchical Block Distance Model for Ultra Low-Dimensional Graph RepresentationsCode0
LOBSTUR: A Local Bootstrap Framework for Tuning Unsupervised Representations in Graph Neural NetworksCode0
Local2Global: A distributed approach for scaling representation learning on graphsCode0
Local2Global: Scaling global representation learning on graphs via local trainingCode0
Local Distance-Preserving Node Embeddings and Their Performance on Random GraphsCode0
WikiGraphs: A Wikipedia Text - Knowledge Graph Paired DatasetCode0
Comprehensive Analysis of Negative Sampling in Knowledge Graph Representation LearningCode0
Companion Animal Disease Diagnostics based on Literal-aware Medical Knowledge Graph Representation LearningCode0
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Benchmark Results

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