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 551–575 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
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Benchmark Results

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