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 551–600 of 982 papers

TitleStatusHype
Multi-modal Graph Learning for Disease Prediction—0
Multimodal Graph Representation Learning for Robust Surgical Workflow Recognition with Adversarial Feature Disentanglement—0
Multimodal Spatio-temporal Graph Learning for Alignment-free RGBT Video Object Detection—0
Multi-object event graph representation learning for Video Question Answering—0
MultiSAGE: a multiplex embedding algorithm for inter-layer link prediction—0
Multivariate Time Series Classification with Hierarchical Variational Graph Pooling—0
Multi-View MOOC Quality Evaluation via Information-Aware Graph Representation Learning—0
Multi-View Node Pruning for Accurate Graph Representation—0
MxPool: Multiplex Pooling for Hierarchical Graph Representation Learning—0
Navigating the Dynamics of Financial Embeddings over Time—0
Negative Sampling in Knowledge Graph Representation Learning: A Review—0
Neighborhood Convolutional Network: A New Paradigm of Graph Neural Networks for Node Classification—0
Neighbour-level Message Interaction Encoding for Improved Representation Learning on Graphs—0
Neural Oscillators are Universal—0
Neural Spacetimes for DAG Representation Learning—0
node2coords: Graph Representation Learning with Wasserstein Barycenters—0
Node Classification Meets Link Prediction on Knowledge Graphs—0
Node Embeddings via Neighbor Embeddings—0
Node Level Graph Autoencoder: Unified Pretraining for Textual Graph Learning—0
OCTAL: Graph Representation Learning for LTL Model Checking—0
OCTAL: Graph Representation Learning for LTL Model Checking—0
OFFER: A Motif Dimensional Framework for Network Representation Learning—0
Omni-Granular Ego-Semantic Propagation for Self-Supervised Graph Representation Learning—0
OmniSage: Large Scale, Multi-Entity Heterogeneous Graph Representation Learning—0
Hyperdimensional Representation Learning for Node Classification and Link Prediction—0
Online Disease Self-diagnosis with Inductive Heterogeneous Graph Convolutional Networks—0
On Node Features for Graph Neural Networks—0
On provable privacy vulnerabilities of graph representations—0
On the combination of graph data for assessing thin-file borrowers' creditworthiness—0
On the Interpretability and Evaluation of Graph Representation Learning—0
On the Use of Unrealistic Predictions in Hundreds of Papers Evaluating Graph Representations—0
Optimizing Blockchain Analysis: Tackling Temporality and Scalability with an Incremental Approach with Metropolis-Hastings Random Walks—0
Optimizing Supply Chain Networks with the Power of Graph Neural Networks—0
Pair-view Unsupervised Graph Representation Learning—0
Pairwise Half-graph Discrimination: A Simple Graph-level Self-supervised Strategy for Pre-training Graph Neural Networks—0
PANE-GNN: Unifying Positive and Negative Edges in Graph Neural Networks for Recommendation—0
Parameter-Efficient Tuning Large Language Models for Graph Representation Learning—0
Path-LLM: A Shortest-Path-based LLM Learning for Unified Graph Representation—0
Permutation Equivariant Neural Controlled Differential Equations for Dynamic Graph Representation Learning—0
PersGNN: Applying Topological Data Analysis and Geometric Deep Learning to Structure-Based Protein Function Prediction—0
Perturbation Ontology based Graph Attention Networks—0
Piecewise-Velocity Model for Learning Continuous-time Dynamic Node Representations—0
Poincaré Heterogeneous Graph Neural Networks for Sequential Recommendation—0
Pointspectrum: Equivariance Meets Laplacian Filtering for Graph Representation Learning—0
Polyp-artifact relationship analysis using graph inductive learned representations—0
Position: Topological Deep Learning is the New Frontier for Relational Learning—0
Post-Hoc Robustness Enhancement in Graph Neural Networks with Conditional Random Fields—0
Tackling the Local Bias in Federated Graph Learning—0
A Graph Feature Auto-Encoder for the Prediction of Unobserved Node Features on Biological Networks—0
A Knowledge Graph-Enhanced Tensor Factorisation Model for Discovering Drug Targets—0
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Benchmark Results

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