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Graph Representation Learning
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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.
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
SMART: Relation-Aware Learning of Geometric Representations for Knowledge Graphs
Permutation Equivariant Neural Controlled Differential Equations for Dynamic Graph Representation Learning
Heterogeneous Temporal Hypergraph Neural Network
Wasserstein Hypergraph Neural Network
Devil's Hand: Data Poisoning Attacks to Locally Private Graph Learning Protocols
Language Embedding Meets Dynamic Graph: A New Exploration for Neural Architecture Representation Learning
ETT-CKGE: Efficient Task-driven Tokens for Continual Knowledge Graph Embedding
Positional Encoding meets Persistent Homology on Graphs
Graph Persistence goes Spectral
Studying and Improving Graph Neural Network-based Motif Estimation
Subgraph Gaussian Embedding Contrast for Self-Supervised Graph Representation Learning
Simple yet Effective Graph Distillation via Clustering
Supervised Graph Contrastive Learning for Gene Regulatory Network
Convexified Message-Passing Graph Neural Networks
Predicate-Conditional Conformalized Answer Sets for Knowledge Graph Embeddings
Mini-Game Lifetime Value Prediction in WeChat
LOBSTUR: A Local Bootstrap Framework for Tuning Unsupervised Representations in Graph Neural Networks
Radiogenomic Bipartite Graph Representation Learning for Alzheimer's Disease Detection
Robust Knowledge Graph Embedding via Denoising
Large Language Model Enhancers for Graph Neural Networks: An Analysis from the Perspective of Causal Mechanism Identification
DyGSSM: Multi-view Dynamic Graph Embeddings with State Space Model Gradient Update
Injecting Knowledge Graphs into Large Language Models
The Correspondence Between Bounded Graph Neural Networks and Fragments of First-Order Logic
Graffe: Graph Representation Learning via Diffusion Probabilistic Models
A Conjoint Graph Representation Learning Framework for Hypertension Comorbidity Risk Prediction
PyTDC: A multimodal machine learning training, evaluation, and inference platform for biomedical foundation models
Multi-Granular Attention based Heterogeneous Hypergraph Neural Network
A Survey on Temporal Interaction Graph Representation Learning: Progress, Challenges, and Opportunities
GRAPE: Heterogeneous Graph Representation Learning for Genetic Perturbation with Coding and Non-Coding Biotype
Multimodal Graph Representation Learning for Robust Surgical Workflow Recognition with Adversarial Feature Disentanglement
ABG-NAS: Adaptive Bayesian Genetic Neural Architecture Search for Graph Representation Learning
OmniSage: Large Scale, Multi-Entity Heterogeneous Graph Representation Learning
Mitigating Degree Bias in Graph Representation Learning with Learnable Structural Augmentation and Structural Self-Attention
QuatE-D: A Distance-Based Quaternion Model for Knowledge Graph Embedding
Multimodal Spatio-temporal Graph Learning for Alignment-free RGBT Video Object Detection
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.