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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.
MSGCN: Multiplex Spatial Graph Convolution Network for Interlayer Link Weight Prediction
A Novel Graph Transformer Framework for Gene Regulatory Network Inference
Hierarchical Vector Quantized Graph Autoencoder with Annealing-Based Code Selection
Balancing Graph Embedding Smoothness in Self-Supervised Learning via Information-Theoretic Decomposition
Integrating Structural and Semantic Signals in Text-Attributed Graphs with BiGTex
Transfer Learning for Temporal Link Prediction
Trajectory Encoding Temporal Graph Networks
GNN-ACLP: Graph Neural Networks based Analog Circuit Link Prediction
A Systematic Evaluation of Knowledge Graph Embeddings for Gene-Disease Association Prediction
On Large-scale Evaluation of Embedding Models for Knowledge Graph Completion
FLASH: Flexible Learning of Adaptive Sampling from History in Temporal Graph Neural Networks
Knowledge Graph Completion with Relation-Aware Anchor Enhancement
Exploiting Meta-Learning-based Poisoning Attacks for Graph Link Prediction
Optimal Embedding Guided Negative Sample Generation for Knowledge Graph Link Prediction
Knowledge Graph Completion with Mixed Geometry Tensor Factorization
Graph Attention for Heterogeneous Graphs with Positional Encoding
Inductive Link Prediction on N-ary Relational Facts via Semantic Hypergraph Reasoning
Comparison of Metadata Representation Models for Knowledge Graph Embeddings
Finsler Multi-Dimensional Scaling: Manifold Learning for Asymmetric Dimensionality Reduction and Embedding
ContextGNN goes to Elliot: Towards Benchmarking Relational Deep Learning for Static Link Prediction (aka Personalized Item Recommendation)
Unifying Text Semantics and Graph Structures for Temporal Text-attributed Graphs with Large Language Models
Efficient and Privacy-Preserved Link Prediction via Condensed Graphs
Matrix Factorization for Inferring Associations and Missing Links
BicliqueEncoder: An Efficient Method for Link Prediction in Bipartite Networks using Formal Concept Analysis and Transformer Encoder
Leap: Inductive Link Prediction via Learnable TopologyAugmentation
The Signed Two-Space Proximity Model for Learning Representations in Protein-Protein Interaction Networks
Unsupervised Attributed Dynamic Network Embedding with Stability Guarantees
How Low Can You Go? Searching for the Intrinsic Dimensionality of Complex Networks using Metric Node Embeddings
A Survey of Link Prediction in Temporal Networks
Exploring Graph Tasks with Pure LLMs: A Comprehensive Benchmark and Investigation
Make Literature-Based Discovery Great Again through Reproducible Pipelines
FHGE: A Fast Heterogeneous Graph Embedding with Ad-hoc Meta-paths
HetFS: A Method for Fast Similarity Search with Ad-hoc Meta-paths on Heterogeneous Information Networks
Evaluating link prediction: New perspectives and recommendations
LLM-driven Knowledge Distillation for Dynamic Text-Attributed Graphs
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