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 851–900 of 982 papers

TitleStatusHype
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
Pre-training Graph Neural Network for Cross Domain Recommendation—0
Prior Information based Decomposition and Reconstruction Learning for Micro-Expression Recognition—0
Privacy-preserving design of graph neural networks with applications to vertical federated learning—0
Probing Negative Sampling Strategies to Learn GraphRepresentations via Unsupervised Contrastive Learning—0
PSLF: A PID Controller-incorporated Second-order Latent Factor Analysis Model for Recommender System—0
Quantifying Challenges in the Application of Graph Representation Learning—0
RandAlign: A Parameter-Free Method for Regularizing Graph Convolutional Networks—0
Reconstruction for Powerful Graph Representations—0
Dynamic Graph Representation Learning via Edge Temporal States Modeling and Structure-reinforced Transformer—0
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Benchmark Results

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