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

Showing 361370 of 982 papers

TitleStatusHype
Harvesting Textual and Structured Data from the HAL Publication Repository0
Unveiling the Potential of Spiking Dynamics in Graph Representation Learning through Spatial-Temporal Normalization and Coding Strategies0
Leveraging Multi-facet Paths for Heterogeneous Graph Representation Learning0
Noise-Resilient Unsupervised Graph Representation Learning via Multi-Hop Feature Quality EstimationCode0
Semantic Communication Enhanced by Knowledge Graph Representation Learning0
DTFormer: A Transformer-Based Method for Discrete-Time Dynamic Graph Representation Learning0
Scalable Graph Compressed ConvolutionsCode0
Your Graph Recommender is Provably a Single-view Graph Contrastive Learning0
Exploring the Role of Node Diversity in Directed Graph Representation LearningCode0
PolyFormer: Scalable Node-wise Filters via Polynomial Graph TransformerCode0
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Benchmark Results

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