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
Graph AI in Medicine—0
Graph-Based Re-ranking: Emerging Techniques, Limitations, and Opportunities—0
Graph Anomaly Detection in Time Series: A Survey—0
On Understanding and Mitigating the Dimensional Collapse of Graph Contrastive Learning: a Non-Maximum Removal Approach—0
Graph Context Encoder: Graph Feature Inpainting for Graph Generation and Self-supervised Pretraining—0
Graph Contrastive Learning with Generative Adversarial Network—0
Graph Learning for Combinatorial Optimization: A Survey of State-of-the-Art—0
Graph Learning with Localized Neighborhood Fairness—0
Graphlets correct for the topological information missed by random walks—0
Graph-Level Embedding for Time-Evolving Graphs—0
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Benchmark Results

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