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

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Showing 851–875 of 982 papers

TitleStatusHype
Biomedical Knowledge Graph Refinement and Completion using Graph Representation Learning and Top-K Similarity Measure—0
Decimated Framelet System on Graphs and Fast G-Framelet TransformsCode0
Pair-view Unsupervised Graph Representation Learning—0
CommPOOL: An Interpretable Graph Pooling Framework for Hierarchical Graph Representation Learning—0
Unsupervised Adversarially-Robust Representation Learning on Graphs—0
A Survey on Heterogeneous Graph Embedding: Methods, Techniques, Applications and Sources—0
A Data-Driven Study of Commonsense Knowledge using the ConceptNet Knowledge Base—0
GL-Coarsener: A Graph representation learning framework to construct coarse grid hierarchy for AMG solversCode0
EGAD: Evolving Graph Representation Learning with Self-Attention and Knowledge Distillation for Live Video Streaming EventsCode0
VStreamDRLS: Dynamic Graph Representation Learning with Self-Attention for Enterprise Distributed Video Streaming SolutionsCode0
Distill2Vec: Dynamic Graph Representation Learning with Knowledge DistillationCode0
Relation-weighted Link Prediction for Disease Gene Identification—0
Self-supervised Graph Representation Learning via Bootstrapping—0
Massively Parallel Graph Drawing and Representation LearningCode0
When Contrastive Learning Meets Active Learning: A Novel Graph Active Learning Paradigm with Self-Supervision—0
PersGNN: Applying Topological Data Analysis and Geometric Deep Learning to Structure-Based Protein Function Prediction—0
Geometric Scattering Attention NetworksCode0
XLVIN: eXecuted Latent Value Iteration Nets—0
Distributed Representations of Entities in Open-World Knowledge Graphs—0
Multivariate Time Series Classification with Hierarchical Variational Graph Pooling—0
Towards Expressive Graph RepresentationCode0
NodeSig: Binary Node Embeddings via Random Walk Diffusion—0
div2vec: Diversity-Emphasized Node Embedding—0
Polyp-artifact relationship analysis using graph inductive learned representations—0
Online Disease Self-diagnosis with Inductive Heterogeneous Graph Convolutional Networks—0
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Benchmark Results

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