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 391–400 of 982 papers

TitleStatusHype
Graph Self-Contrast Representation Learning—0
GCN-SE: Attention as Explainability for Node Classification in Dynamic Graphs—0
Contrastive Brain Network Learning via Hierarchical Signed Graph Pooling Model—0
GCN-BMP: Investigating Graph Representation Learning for DDI Prediction Task—0
Deep Graph Generators: A Survey—0
A Survey on Heterogeneous Graph Embedding: Methods, Techniques, Applications and Sources—0
G5: A Universal GRAPH-BERT for Graph-to-Graph Transfer and Apocalypse Learning—0
Consensus Graph Representation Learning for Better Grounded Image Captioning—0
From random-walks to graph-sprints: a low-latency node embedding framework on continuous-time dynamic graphs—0
A Survey on Graph Representation Learning Methods—0
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Benchmark Results

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