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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 171–180 of 982 papers

TitleStatusHype
GEFL: Extended Filtration Learning for Graph ClassificationCode0
Enhancing Fairness in Unsupervised Graph Anomaly Detection through DisentanglementCode0
Know Your Neighborhood: General and Zero-Shot Capable Binary Function Search Powered by Call GraphletsCode0
Augmentation-based Unsupervised Cross-Domain Functional MRI Adaptation for Major Depressive Disorder Identification—0
Graph External Attention Enhanced TransformerCode1
Learning-Based Link Anomaly Detection in Continuous-Time Dynamic GraphsCode1
Port-Hamiltonian Architectural Bias for Long-Range Propagation in Deep Graph Networks—0
A Structure-Aware Framework for Learning Device Placements on Computation GraphsCode1
Graphlets correct for the topological information missed by random walks—0
HC-GAE: The Hierarchical Cluster-based Graph Auto-Encoder for Graph Representation Learning—0
Show:102550
← PrevPage 18 of 99Next →

Benchmark Results

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