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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.
Unified Graph Networks (UGN): A Deep Neural Framework for Solving Graph Problems
Graph Neural Networks at a Fraction
HyGEN: Regularizing Negative Hyperedge Generation for Accurate Hyperedge Prediction
Rethinking Link Prediction for Directed Graphs
TGB-Seq Benchmark: Challenging Temporal GNNs with Complex Sequential Dynamics
Graph Neural Networks for O-RAN Mobility Management: A Link Prediction Approach
Leveraging Joint Predictive Embedding and Bayesian Inference in Graph Self Supervised Learning
Spectro-Riemannian Graph Neural Networks
PathE: Leveraging Entity-Agnostic Paths for Parameter-Efficient Knowledge Graph Embeddings
Brain-inspired sparse training enables Transformers and LLMs to perform as fully connected
Self-supervised Quantized Representation for Seamlessly Integrating Knowledge Graphs with Large Language Models
Beyond Message Passing: Neural Graph Pattern Machine
Heuristic-Informed Mixture of Experts for Link Prediction in Multilayer Networks
Random Walk Guided Hyperbolic Graph Distillation
Utilizing Graph Neural Networks for Effective Link Prediction in Microservice Architectures
HierPromptLM: A Pure PLM-based Framework for Representation Learning on Heterogeneous Text-rich Networks
Community-Aware Temporal Walks: Parameter-Free Representation Learning on Continuous-Time Dynamic Graphs
Adaptive Spatiotemporal Augmentation for Improving Dynamic Graph Learning
Enhancing Graph Representation Learning with Localized Topological Features
Predict Confidently, Predict Right: Abstention in Dynamic Graph Learning
Graph Contrastive Learning on Multi-label Classification for Recommendations
HyperQuery: Beyond Binary Link Prediction
Structure-Preference Enabled Graph Embedding Generation under Differential Privacy
Coupled Hierarchical Structure Learning using Tree-Wasserstein Distance
CHAT: Beyond Contrastive Graph Transformer for Link Prediction in Heterogeneous Networks
DiffGraph: Heterogeneous Graph Diffusion Model
Multimodal Contrastive Representation Learning in Augmented Biomedical Knowledge Graphs
Efficient Relational Context Perception for Knowledge Graph Completion
Large Language Models Meet Graph Neural Networks: A Perspective of Graph Mining
LASE: Learned Adjacency Spectral Embeddings
A Deep Probabilistic Framework for Continuous Time Dynamic Graph Generation
Extending TWIG: Zero-Shot Predictive Hyperparameter Selection for KGEs based on Graph Structure
Two Layer Walk: A Community-Aware Graph Embedding
Practicable Black-box Evasion Attacks on Link Prediction in Dynamic Graphs -- A Graph Sequential Embedding Method
GNN Applied to Ego-nets for Friend Suggestions
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