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 231–240 of 982 papers

TitleStatusHype
Advancing Biomedicine with Graph Representation Learning: Recent Progress, Challenges, and Future Directions—0
Bridging Large Language Models and Graph Structure Learning Models for Robust Representation Learning—0
A Comparative Study on Dynamic Graph Embedding based on Mamba and Transformers—0
Exploring Task Unification in Graph Representation Learning via Generative Approach—0
Uplifting Message Passing Neural Network with Graph Original Information—0
DPGNN: Dual-Perception Graph Neural Network for Representation Learning—0
Bootstrapping Heterogeneous Graph Representation Learning via Large Language Models: A Generalized Approach—0
An Edge-Aware Graph Autoencoder Trained on Scale-Imbalanced Data for Traveling Salesman Problems—0
Expressivity of Representation Learning on Continuous-Time Dynamic Graphs: An Information-Flow Centric Review—0
Domain Adaptive Graph Classification—0
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Benchmark Results

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