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 371–380 of 982 papers

TitleStatusHype
Graph Representation Learning for Interactive Biomolecule Systems—0
Geo-BERT Pre-training Model for Query Rewriting in POI Search—0
Dealing with Missing Modalities in Multimodal Recommendation: a Feature Propagation-based Approach—0
Control-based Graph Embeddings with Data Augmentation for Contrastive Learning—0
Generating the Graph Gestalt: Kernel-Regularized Graph Representation Learning—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
Distributed Representations of Entities in Open-World Knowledge Graphs—0
Generating Counterfactual Hard Negative Samples for Graph Contrastive Learning—0
Contrastive Representation Learning Based on Multiple Node-centered Subgraphs—0
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Benchmark Results

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