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
Large Language Model Enhancers for Graph Neural Networks: An Analysis from the Perspective of Causal Mechanism Identification—0
LMSOC: An approach for socially sensitive pretraining—0
LocalGCL: Local-aware Contrastive Learning for Graphs—0
Localized Graph Collaborative Filtering—0
Local Structure-aware Graph Contrastive Representation Learning—0
Machine Learning Partners in Criminal Networks—0
Mamba-Based Graph Convolutional Networks: Tackling Over-smoothing with Selective State Space—0
Marginalized graph autoencoder for graph clustering—0
Message passing all the way up—0
Mini-Game Lifetime Value Prediction in WeChat—0
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Benchmark Results

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