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 501–550 of 982 papers

TitleStatusHype
Knowledge-Induced Medicine Prescribing Network for Medication Recommendation—0
Knowledge-enhanced Session-based Recommendation with Temporal Transformer—0
Knowledge Graph Representation Learning using Ordinary Differential Equations—0
Knowledge Probing for Graph Representation Learning—0
KQGC: Knowledge Graph Embedding with Smoothing Effects of Graph Convolutions for Recommendation—0
Language Embedding Meets Dynamic Graph: A New Exploration for Neural Architecture Representation Learning—0
Large-scale graph representation learning with very deep GNNs and self-supervision—0
Large-scale Graph Representation Learning of Dynamic Brain Connectome with Transformers—0
LaundroGraph: Self-Supervised Graph Representation Learning for Anti-Money Laundering—0
Learning Dynamic Graph Embeddings with Neural Controlled Differential Equations—0
Learning From Graph-Structured Data: Addressing Design Issues and Exploring Practical Applications in Graph Representation Learning—0
Learning by Sampling and Compressing: Efficient Graph Representation Learning with Extremely Limited Annotations—0
Learning Graph Search Heuristics—0
Revisiting the role of heterophily in graph representation learning: An edge classification perspective—0
Learning Hierarchical Graph Representation for Image Manipulation Detection—0
Learning Latent Topology for Graph Matching—0
Learning node embeddings via summary graphs: a brief theoretical analysis—0
Learning over Families of Sets -- Hypergraph Representation Learning for Higher Order Tasks—0
Learning Robust Representations with Graph Denoising Policy Network—0
Learning Robust Representation through Graph Adversarial Contrastive Learning—0
Learning to Hash with Graph Neural Networks for Recommender Systems—0
Learning to Represent the Evolution of Dynamic Graphs with Recurrent Models—0
Learning to schedule job-shop problems: Representation and policy learning using graph neural network and reinforcement learning—0
Learning with Capsules: A Survey—0
Leveraging Auto-Distillation and Generative Self-Supervised Learning in Residual Graph Transformers for Enhanced Recommender Systems—0
Leveraging Multi-facet Paths for Heterogeneous Graph Representation Learning—0
Leveraging Orbital Information and Atomic Feature in Deep Learning Model—0
LiftPool: Lifting-based Graph Pooling for Hierarchical Graph Representation Learning—0
LINGUINE: LearnIng to pruNe on subGraph convolUtIon NEtworks—0
LinkNBed: Multi-Graph Representation Learning with Entity Linkage—0
Large Language Model Enhancers for Graph Neural Networks: An Analysis from the Perspective of Causal Mechanism Identification—0
LMSOC: An approach for socially sensitive pretraining—0
LocalGCL: Local-aware Contrastive Learning for Graphs—0
Localized Graph Collaborative Filtering—0
Local Structure-aware Graph Contrastive Representation Learning—0
Machine Learning Partners in Criminal Networks—0
Mamba-Based Graph Convolutional Networks: Tackling Over-smoothing with Selective State Space—0
Marginalized graph autoencoder for graph clustering—0
Message passing all the way up—0
Mini-Game Lifetime Value Prediction in WeChat—0
Mitigating Relational Bias on Knowledge Graphs—0
Mixed-Curvature Transformers for Graph Representation Learning papersreview—0
Model-Agnostic and Diverse Explanations for Streaming Rumour Graphs—0
Modeling Event Propagation via Graph Biased Temporal Point Process—0
MTLSO: A Multi-Task Learning Approach for Logic Synthesis Optimization—0
MugRep: A Multi-Task Hierarchical Graph Representation Learning Framework for Real Estate Appraisal—0
Multi-Channel Graph Convolutional Networks—0
Multi-fidelity Stability for Graph Representation Learning—0
Multi-Granular Attention based Heterogeneous Hypergraph Neural Network—0
Multi-Level Graph Contrastive Learning—0
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Benchmark Results

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