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 501–525 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
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Benchmark Results

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