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 521–530 of 982 papers

TitleStatusHype
From random-walks to graph-sprints: a low-latency node embedding framework on continuous-time dynamic graphs—0
DistTGL: Distributed Memory-Based Temporal Graph Neural Network Training—0
Frameless Graph Knowledge DistillationCode0
Neural Causal Graph Collaborative FilteringCode0
Improving Heterogeneous Graph Learning with Weighted Mixed-Curvature Product ManifoldCode0
ENGAGE: Explanation Guided Data Augmentation for Graph Representation LearningCode0
Graph Neural Networks Provably Benefit from Structural Information: A Feature Learning Perspective—0
Directional diffusion models for graph representation learning—0
Transforming Graphs for Enhanced Attribute Clustering: An Innovative Graph Transformer-Based Method—0
Advancing Biomedicine with Graph Representation Learning: Recent Progress, Challenges, and Future Directions—0
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Benchmark Results

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