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 851–900 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
About Graph Degeneracy, Representation Learning and ScalabilityCode0
CAGNN: Cluster-Aware Graph Neural Networks for Unsupervised Graph Representation Learning—0
VeRNAl: Mining RNA Structures for Fuzzy Base Pairing Network MotifsCode0
OFFER: A Motif Dimensional Framework for Network Representation Learning—0
Graph Learning for Combinatorial Optimization: A Survey of State-of-the-Art—0
Tree Structure-Aware Graph Representation Learning via Integrated Hierarchical Aggregation and Relational Metric Learning—0
Efficient Knowledge Graph Validation via Cross-Graph Representation Learning—0
A Matrix Chernoff Bound for Markov Chains and Its Application to Co-occurrence Matrices—0
node2coords: Graph Representation Learning with Wasserstein Barycenters—0
Recent Advances in Network-based Methods for Disease Gene PredictionCode0
Deep Representation Learning For Multimodal Brain Networks—0
Are Hyperbolic Representations in Graphs Created Equal?—0
Dynamic Graph Representation Learning for Video Dialog via Multi-Modal Shuffled Transformers—0
Hierarchical and Unsupervised Graph Representation Learning with Loukas's CoarseningCode0
Maximizing Cohesion and Separation in Graph Representation Learning: A Distance-aware Negative Sampling ApproachCode0
GCN-BMP: Investigating Graph Representation Learning for DDI Prediction Task—0
Navigating the Dynamics of Financial Embeddings over Time—0
Structural Landmarking and Interaction Modelling: on Resolution Dilemmas in Graph Classification—0
Hop Sampling: A Simple Regularized Graph Learning for Non-Stationary Environments—0
Time-varying Graph Representation Learning via Higher-Order Skip-Gram with Negative SamplingCode0
Quantifying Challenges in the Application of Graph Representation Learning—0
G5: A Universal GRAPH-BERT for Graph-to-Graph Transfer and Apocalypse Learning—0
Graph Representation Learning Network via Adaptive SamplingCode0
Adversarial Attack on Hierarchical Graph Pooling Neural Networks—0
A Graph Feature Auto-Encoder for the Prediction of Unobserved Node Features on Biological Networks—0
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Benchmark Results

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