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 471480 of 982 papers

TitleStatusHype
Hierarchical and Unsupervised Graph Representation Learning with Loukas's CoarseningCode0
dyngraph2vec: Capturing Network Dynamics using Dynamic Graph Representation LearningCode0
DynGL-SDP: Dynamic Graph Learning for Semantic Dependency ParsingCode0
HeGAE-AC: heterogeneous graph auto-encoder for attribute completionCode0
Calibrate and Boost Logical Expressiveness of GNN Over Multi-Relational and Temporal GraphsCode0
Het-node2vec: second order random walk sampling for heterogeneous multigraphs embeddingCode0
Hyper-SAGNN: a self-attention based graph neural network for hypergraphsCode0
Joint Prediction of Audio Event and Annoyance Rating in an Urban Soundscape by Hierarchical Graph Representation LearningCode0
PROXI: Challenging the GNNs for Link PredictionCode0
Dynamic Graph Representation Learning with Neural Networks: A Survey0
Show:102550
← PrevPage 48 of 99Next →

Benchmark Results

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