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 51–75 of 982 papers

TitleStatusHype
CCGL: Contrastive Cascade Graph LearningCode1
A Meta-Learning Approach for Graph Representation Learning in Multi-Task SettingsCode1
Efficient and Feasible Robotic Assembly Sequence Planning via Graph Representation LearningCode1
E-GraphSAGE: A Graph Neural Network based Intrusion Detection System for IoTCode1
A critical look at the evaluation of GNNs under heterophily: Are we really making progress?Code1
An adaptive graph learning method for automated molecular interactions and properties predictionsCode1
Adversarial Graph DisentanglementCode1
Expert Knowledge-Aware Image Difference Graph Representation Learning for Difference-Aware Medical Visual Question AnsweringCode1
Distance Encoding: Design Provably More Powerful Neural Networks for Graph Representation LearningCode1
An Effective and Efficient Entity Alignment Decoding Algorithm via Third-Order Tensor IsomorphismCode1
Fast Graph Learning with Unique Optimal SolutionsCode1
An Open Challenge for Inductive Link Prediction on Knowledge GraphsCode1
Multi-hop Attention Graph Neural NetworkCode1
Bi-GCN: Binary Graph Convolutional NetworkCode1
Disentangle-based Continual Graph Representation LearningCode1
Distribution-Aware Graph Representation Learning for Transient Stability Assessment of Power SystemCode1
Audio Event-Relational Graph Representation Learning for Acoustic Scene ClassificationCode1
Decoupling Weighing and Selecting for Integrating Multiple Graph Pre-training TasksCode1
Deep Graph Contrastive Representation LearningCode1
A Representation Learning Framework for Property GraphsCode1
Beyond Smoothing: Unsupervised Graph Representation Learning with Edge Heterophily DiscriminatingCode1
AutoGCL: Automated Graph Contrastive Learning via Learnable View GeneratorsCode1
DiffKG: Knowledge Graph Diffusion Model for RecommendationCode1
A step towards neural genome assemblyCode1
Unleashing the Power of Graph Data Augmentation on Covariate Distribution ShiftCode1
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Benchmark Results

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