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 561–570 of 982 papers

TitleStatusHype
Augmentations in Graph Contrastive Learning: Current Methodological Flaws & Towards Better Practices—0
Spectral-Aware Augmentation for Enhanced Graph Representation Learning—0
A Unified Graph Selective Prompt Learning for Graph Neural Networks—0
A Unified View on Neural Message Passing with Opinion Dynamics for Social Networks—0
Automated Graph Self-supervised Learning via Multi-teacher Knowledge Distillation—0
BCDR: Betweenness Centrality-based Distance Resampling for Graph Shortest Distance Embedding—0
Beyond COVID-19 Diagnosis: Prognosis with Hierarchical Graph Representation Learning—0
Biomedical Knowledge Graph Refinement and Completion using Graph Representation Learning and Top-K Similarity Measure—0
Biphasic Face Photo-Sketch Synthesis via Semantic-Driven Generative Adversarial Network with Graph Representation Learning—0
Black-box Gradient Attack on Graph Neural Networks: Deeper Insights in Graph-based Attack and Defense—0
Show:102550
← PrevPage 57 of 99Next →

Benchmark Results

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