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 851–875 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
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Benchmark Results

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