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 701–710 of 982 papers

TitleStatusHype
Uniting Heterogeneity, Inductiveness, and Efficiency for Graph Representation Learning—0
Unsupervised Adversarially-Robust Representation Learning on Graphs—0
Unsupervised Hierarchical Graph Representation Learning with Variational Bayes—0
Unveiling the Potential of Spiking Dynamics in Graph Representation Learning through Spatial-Temporal Normalization and Coding Strategies—0
Uplink Scheduling in Federated Learning: an Importance-Aware Approach via Graph Representation Learning—0
Urban Region Profiling via A Multi-Graph Representation Learning Framework—0
Using Graph Representation Learning with Schema Encoders to Measure the Severity of Depressive Symptoms—0
Using Large-scale Heterogeneous Graph Representation Learning for Code Review Recommendations at Microsoft—0
Variational Graph Auto-Encoder Based Inductive Learning Method for Semi-Supervised Classification—0
Verbalized Graph Representation Learning: A Fully Interpretable Graph Model Based on Large Language Models Throughout the Entire Process—0
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Benchmark Results

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