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 711–720 of 982 papers

TitleStatusHype
X-GOAL: Multiplex Heterogeneous Graph Prototypical Contrastive Learning—0
Graph Neural Networks for Binary Programming—0
Graph Neural Networks Provably Benefit from Structural Information: A Feature Learning Perspective—0
Graph Ordering: Towards the Optimal by Learning—0
Graph Partial Label Learning with Potential Cause Discovering—0
Graph Persistence goes Spectral—0
GraphPMU: Event Clustering via Graph Representation Learning Using Locationally-Scarce Distribution-Level Fundamental and Harmonic PMU Measurements—0
3D Hand Pose Estimation via Regularized Graph Representation Learning—0
Learning Graph Representation by Aggregating Subgraphs via Mutual Information Maximization—0
Graph Representation learning for Audio & Music genre Classification—0
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Benchmark Results

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