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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 671–680 of 982 papers

TitleStatusHype
Sub-GMN: The Neural Subgraph Matching Network Model—0
Supervised Graph Contrastive Learning for Gene Regulatory Network—0
Symmetry Breaking and Equivariant Neural Networks—0
Synergizing LLM Agents and Knowledge Graph for Socioeconomic Prediction in LBSN—0
Temporal Graph Representation Learning with Adaptive Augmentation Contrastive—0
Tensor Graph Convolutional Network for Dynamic Graph Representation Learning—0
The Correspondence Between Bounded Graph Neural Networks and Fragments of First-Order Logic—0
The Heterophilic Graph Learning Handbook: Benchmarks, Models, Theoretical Analysis, Applications and Challenges—0
Fundamental Limits of Deep Graph Convolutional Networks—0
The Power of Graph Convolutional Networks to Distinguish Random Graph Models: Short Version—0
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Benchmark Results

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