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 331–340 of 982 papers

TitleStatusHype
Graph-Level Embedding for Time-Evolving Graphs—0
Asymmetric Graph Representation Learning—0
Curve Your Attention: Mixed-Curvature Transformers for Graph Representation Learning—0
Adaptive Multi-Neighborhood Attention based Transformer for Graph Representation Learning—0
Graph Learning with Localized Neighborhood Fairness—0
A Survey on Temporal Knowledge Graph: Representation Learning and Applications—0
Creating generalizable downstream graph models with random projections—0
Graph Neural Network-based Spectral Filtering Mechanism for Imbalance Classification in Network Digital Twin—0
CORE: Data Augmentation for Link Prediction via Information Bottleneck—0
A Survey on Temporal Interaction Graph Representation Learning: Progress, Challenges, and Opportunities—0
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Benchmark Results

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