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Link Prediction
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Link Prediction is a task in graph and network analysis where the goal is to predict missing or future connections between nodes in a network. Given a partially observed network, the goal of link prediction is to infer which links are most likely to be added or missing based on the observed connections and the structure of the network.
<span style="color:grey; opacity: 0.6">( Image credit: Inductive Representation Learning on Large Graphs )</span>
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
Representation learning of dynamic networks
Edge Contrastive Learning: An Augmentation-Free Graph Contrastive Learning Model
A Comparative Study on Dynamic Graph Embedding based on Mamba and Transformers
Multi-Class and Multi-Task Strategies for Neural Directed Link Prediction
A Survey on Knowledge Graph Structure and Knowledge Graph Embeddings
From communities to interpretable network and word embedding: an unified approach
A novel robust integrating method by high-order proximity for self-supervised attribute network embedding
Hyperedge Anomaly Detection with Hypergraph Neural Network
eXpath: Explaining Knowledge Graph Link Prediction with Ontological Closed Path Rules
Training MLPs on Graphs without Supervision
Distance-Adaptive Quaternion Knowledge Graph Embedding with Bidirectional Rotation
Multi-Scale Node Embeddings for Graph Modeling and Generation
Enhancing Supply Chain Visibility with Generative AI: An Exploratory Case Study on Relationship Prediction in Knowledge Graphs
SA-GNAS: Seed Architecture Expansion for Efficient Large-scale Graph Neural Architecture Search
How the use of feature selection methods influences the efficiency and accuracy of complex network simulations
Learn to Unlearn: Meta-Learning-Based Knowledge Graph Embedding Unlearning
TGTOD: A Global Temporal Graph Transformer for Outlier Detection at Scale
One Model for One Graph: A New Perspective for Pretraining with Cross-domain Graphs
ContextGNN: Beyond Two-Tower Recommendation Systems
Integrating Social Determinants of Health into Knowledge Graphs: Evaluating Prediction Bias and Fairness in Healthcare
Attribute-Enhanced Similarity Ranking for Sparse Link Prediction
Perturbation Ontology based Graph Attention Networks
Bridging AI and Science: Implications from a Large-Scale Literature Analysis of AI4Science
Deep Sparse Latent Feature Models for Knowledge Graph Completion
TANGNN: a Concise, Scalable and Effective Graph Neural Networks with Top-m Attention Mechanism for Graph Representation Learning
Can GNNs Learn Link Heuristics? A Concise Review and Evaluation of Link Prediction Methods
VisGraphVar: A Benchmark Generator for Assessing Variability in Graph Analysis Using Large Vision-Language Models
Heterophilic Graph Neural Networks Optimization with Causal Message-passing
Scalable Deep Metric Learning on Attributed Graphs
Scalable and Effective Negative Sample Generation for Hyperedge Prediction
Hierarchical-Graph-Structured Edge Partition Models for Learning Evolving Community Structure
Enhancing Link Prediction with Fuzzy Graph Attention Networks and Dynamic Negative Sampling
Shedding Light on Problems with Hyperbolic Graph Learning
UniHR: Hierarchical Representation Learning for Unified Knowledge Graph Link Prediction
HeteroSample: Meta-path Guided Sampling for Heterogeneous Graph Representation Learning
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