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Link Prediction

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하위 태스크: 6
평가표: 79
보존된 평가 기록: 497

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>

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/KrainskiL/CGE.jl

공식 코드

Evaluating Node Embeddings of Complex Networks

https://github.com/LARS-research/HL-GNN

공식 코드

Heuristic Learning with Graph Neural Networks: A Unified Framework for Link Prediction

https://github.com/LJeub/Local2Global_embedding

공식 코드

Local2Global: Scaling global representation learning on graphs via local training

https://github.com/LPioL/MultiVERSE

공식 코드

MultiVERSE: a multiplex and multiplex-heterogeneous network embedding approach

https://github.com/LUOyk1999/NodeID

공식 코드

Node Identifiers: Compact, Discrete Representations for Efficient Graph Learning

https://github.com/LeonResearch/NPGNN

공식 코드

How Neural Processes Improve Graph Link Prediction

https://github.com/LeonResearch/WARGA

공식 코드

Wasserstein Adversarially Regularized Graph Autoencoder

https://github.com/Linda-sunshine/JNET

공식 코드

JNET: Learning User Representations via Joint Network Embedding and Topic Embedding

https://github.com/Luoyadan/SIHG

공식 코드

Interpretable Signed Link Prediction with Signed Infomax Hyperbolic Graph

https://github.com/MIRALab-USTC/KG-TACT

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Topology-Aware Correlations Between Relations for Inductive Link Prediction in Knowledge Graphs

https://github.com/MIRALab-USTC/KGE-DURA

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Duality-Induced Regularizer for Tensor Factorization Based Knowledge Graph Completion

https://github.com/MIRALab-USTC/KGE-HAKE

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Learning Hierarchy-Aware Knowledge Graph Embeddings for Link Prediction

https://github.com/MLAI-Yonsei/GPIO

공식 코드

Graph Perceiver IO: A General Architecture for Graph Structured Data

https://github.com/MaKaili/Contrast-Reg

공식 코드

Calibrating and Improving Graph Contrastive Learning

https://github.com/MaastrichtU-IDS/LMKBC-2023

공식 코드

Expanding the Vocabulary of BERT for Knowledge Base Construction

https://github.com/MaoXinn/RREA

공식 코드

Relational Reflection Entity Alignment

https://github.com/Mehran-k/SimplE

공식 코드

SimplE Embedding for Link Prediction in Knowledge Graphs

https://github.com/Mobzhang/PT-DGNN

공식 코드

Pre-Training on Dynamic Graph Neural Networks

https://github.com/MysteryVaibhav/DW-GAE

공식 코드

RWR-GAE: Random Walk Regularization for Graph Auto Encoders

https://github.com/NVIDIA/DeepLearningExamples

공식 코드

A Framework for Large Scale Synthetic Graph Dataset Generation

https://github.com/NYXFLOWER/GripNet

공식 코드

GripNet: Graph Information Propagation on Supergraph for Heterogeneous Graphs

https://github.com/NYXFLOWER/TIP

공식 코드

Tri-graph Information Propagation for Polypharmacy Side Effect Prediction

https://github.com/NilBiescas/GeoContrastNet

공식 코드

GeoContrastNet: Contrastive Key-Value Edge Learning for Language-Agnostic Document Understanding

https://github.com/OSU-NLP-Group/KG-R3

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A Retrieve-and-Read Framework for Knowledge Graph Link Prediction

https://github.com/OpenBioLink/OpenBioLink

공식 코드

OpenBioLink: A benchmarking framework for large-scale biomedical link prediction

https://github.com/OpenBioLink/SAFRAN

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Scalable and interpretable rule-based link prediction for large heterogeneous knowledge graphs

https://github.com/OxCSML-BayesNP/HawkesNetOC

공식 코드

Modelling sparsity, heterogeneity, reciprocity and community structure in temporal interaction data

https://github.com/PaddlePaddle/Research

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CoKE: Contextualized Knowledge Graph Embedding

https://github.com/PaddlePaddle/Research/tree/master/KG/ACL2021_GRAN

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Link Prediction on N-ary Relational Facts: A Graph-based Approach

https://github.com/PaddlePaddle/models

공식 코드

CoKE: Contextualized Knowledge Graph Embedding

https://github.com/PetarV-/GAT

공식 코드

Graph Attention Networks

https://github.com/Peter7Yao/OPTransE

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Representation Learning with Ordered Relation Paths for Knowledge Graph Completion

https://github.com/PriyeshV/NRL_Benchmark

공식 코드

Network Representation Learning: Consolidation and Renewed Bearing

https://github.com/RayyanRiaz/EVGAE

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Epitomic Variational Graph Autoencoder

https://github.com/RexYing/gnn-model-explainer

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GNNExplainer: Generating Explanations for Graph Neural Networks

https://github.com/RingBDStack/AGE

공식 코드

A Robust and Generalized Framework for Adversarial Graph Embedding

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.