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 541–550 of 982 papers

TitleStatusHype
Mitigating Relational Bias on Knowledge Graphs—0
Mixed-Curvature Transformers for Graph Representation Learning papersreview—0
Model-Agnostic and Diverse Explanations for Streaming Rumour Graphs—0
Modeling Event Propagation via Graph Biased Temporal Point Process—0
MTLSO: A Multi-Task Learning Approach for Logic Synthesis Optimization—0
MugRep: A Multi-Task Hierarchical Graph Representation Learning Framework for Real Estate Appraisal—0
Multi-Channel Graph Convolutional Networks—0
Multi-fidelity Stability for Graph Representation Learning—0
Multi-Granular Attention based Heterogeneous Hypergraph Neural Network—0
Multi-Level Graph Contrastive Learning—0
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Benchmark Results

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