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 531–540 of 982 papers

TitleStatusHype
Mixed-Curvature Transformers for Graph Representation Learning papersreview—0
Accelerating Dynamic Network Embedding with Billions of Parameter Updates to MillisecondsCode0
Self-supervised Learning and Graph Classification under Heterophily—0
CARL-G: Clustering-Accelerated Representation Learning on Graphs—0
Virtual Node Tuning for Few-shot Node Classification—0
CoCo: A Coupled Contrastive Framework for Unsupervised Domain Adaptive Graph Classification—0
Point-Voxel Absorbing Graph Representation Learning for Event Stream based RecognitionCode0
PANE-GNN: Unifying Positive and Negative Edges in Graph Neural Networks for Recommendation—0
Graph-Level Embedding for Time-Evolving Graphs—0
GIMM: InfoMin-Max for Automated Graph Contrastive Learning—0
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Benchmark Results

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