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 451460 of 982 papers

TitleStatusHype
Het-node2vec: second order random walk sampling for heterogeneous multigraphs embeddingCode0
Heterogeneous Deep Graph InfomaxCode0
Are Graph Embeddings the Panacea? An Empirical Survey from the Data Fitness PerspectiveCode0
HeGAE-AC: heterogeneous graph auto-encoder for attribute completionCode0
GTNet: A Tree-Based Deep Graph Learning ArchitectureCode0
Graph-wise Common Latent Factor Extraction for Unsupervised Graph Representation LearningCode0
DyTSCL: Dynamic graph representation via tempo-structural contrastive learningCode0
GT-SVQ: A Linear-Time Graph Transformer for Node Classification Using Spiking Vector QuantizationCode0
dyngraph2vec: Capturing Network Dynamics using Dynamic Graph Representation LearningCode0
DynGL-SDP: Dynamic Graph Learning for Semantic Dependency ParsingCode0
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Benchmark Results

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