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 421430 of 982 papers

TitleStatusHype
Graph Representation Learning: A SurveyCode0
Graph Representation Learning Beyond Node and HomophilyCode0
Hierarchical and Unsupervised Graph Representation Learning with Loukas's CoarseningCode0
Centrality Graph Shift Operators for Graph Neural NetworksCode0
ARIEL: Adversarial Graph Contrastive LearningCode0
Cell Attention NetworksCode0
Het-node2vec: second order random walk sampling for heterogeneous multigraphs embeddingCode0
Hierarchical Multi-Relational Graph Representation Learning for Large-Scale Prediction of Drug-Drug InteractionsCode0
Embedding Graphs on Grassmann ManifoldCode0
EGAD: Evolving Graph Representation Learning with Self-Attention and Knowledge Distillation for Live Video Streaming EventsCode0
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Benchmark Results

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