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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/RingBDStack/dynamic_network_embedding

공식 코드

Dynamic Network Embedding via Incremental Skip-gram with Negative Sampling

https://github.com/SabanciParallelComputing/GOSH

공식 코드

Understanding Coarsening for Embedding Large-Scale Graphs

https://github.com/SeongJinAhn/VGNAE

공식 코드

Variational Graph Normalized Auto-Encoders

https://github.com/SmartDataAnalytics/LiteralE

공식 코드

Incorporating Literals into Knowledge Graph Embeddings

https://github.com/SotirisKot/Content-Aware-N2V

공식 코드

Embedding Biomedical Ontologies by Jointly Encoding Network Structure and Textual Node Descriptors

https://github.com/SoyeonTiffanyMin/TransINT

공식 코드

TransINT: Embedding Implication Rules in Knowledge Graphs with Isomorphic Intersections of Linear Subspaces

https://github.com/StatsDLMathsRecomSys/Inductive-representation-learning-on-temporal-graphs

공식 코드

Inductive Representation Learning on Temporal Graphs

https://github.com/Supermaxman/covid19-vaccine-twitter

공식 코드

Automatic Detection of COVID-19 Vaccine Misinformation with Graph Link Prediction

https://github.com/Synerise/cleora

공식 코드

Cleora: A Simple, Strong and Scalable Graph Embedding Scheme

https://github.com/TGB-Seq/TGB-Seq

공식 코드

TGB-Seq Benchmark: Challenging Temporal GNNs with Complex Sequential Dynamics

https://github.com/THU-KEG/BIMR

공식 코드

Is Multi-Hop Reasoning Really Explainable? Towards Benchmarking Reasoning Interpretability

https://github.com/THU-KEG/KEPLER

공식 코드

KEPLER: A Unified Model for Knowledge Embedding and Pre-trained Language Representation

https://github.com/THU-KEG/MetaKGR

공식 코드

Adapting Meta Knowledge Graph Information for Multi-Hop Reasoning over Few-Shot Relations

https://github.com/THUDM/GCC

공식 코드

GCC: Graph Contrastive Coding for Graph Neural Network Pre-Training

https://github.com/THUDM/OAG-BERT

공식 코드

OAG-BERT: Towards A Unified Backbone Language Model For Academic Knowledge Services

https://github.com/THUDM/cogdl

공식 코드

CogDL: A Comprehensive Library for Graph Deep Learning

https://github.com/Tebmer/SNRI

공식 코드

Subgraph Neighboring Relations Infomax for Inductive Link Prediction on Knowledge Graphs

https://github.com/TemporalKGTeam/xERTE

공식 코드

xERTE: Explainable Reasoning on Temporal Knowledge Graphs for Forecasting Future Links

https://github.com/TimDettmers/ConvE

공식 코드

Convolutional 2D Knowledge Graph Embeddings

https://github.com/Tommoo/FinslerMDS

공식 코드

Finsler Multi-Dimensional Scaling: Manifold Learning for Asymmetric Dimensionality Reduction and Embedding

https://github.com/VGraphRNN/VGRNN

공식 코드

Variational Graph Recurrent Neural Networks

https://github.com/VITA-Group/Unified-LTH-GNN

공식 코드

A Unified Lottery Ticket Hypothesis for Graph Neural Networks

https://github.com/Vachik-Dave/Neural-Brane-Neural-Bayesian-Personalized-Ranking-for-Attributed-Network-Embedding

공식 코드

Neural-Brane: Neural Bayesian Personalized Ranking for Attributed Network Embedding

https://github.com/VeritasYin/DEGNN_node_classification

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Revisiting graph neural networks and distance encoding from a practical view

https://github.com/WEIYanbin1999/GITA

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GITA: Graph to Visual and Textual Integration for Vision-Language Graph Reasoning

https://github.com/Wentao-Xu/SEEK

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SEEK: Segmented Embedding of Knowledge Graphs

https://github.com/XinyiZ001/mSHINE

공식 코드

mSHINE: A Multiple-meta-paths Simultaneous Learning Framework for Heterogeneous Information Network Embedding

https://github.com/Yangyeeee/SGAT

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Sparse Graph Attention Networks

https://github.com/YijunRan/Maximum-Capability-Link-Prediction

공식 코드

The maximum capability of a topological feature in link prediction

https://github.com/Yunbo-max/HyDRO

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Random Walk Guided Hyperbolic Graph Distillation

https://github.com/ZINUX1998/DyCSC

공식 코드

DyCSC: Modeling the Evolutionary Process of Dynamic Networks Based on Cluster Structure

https://github.com/ZM7/DGSR

공식 코드

Dynamic Graph Neural Networks for Sequential Recommendation

https://github.com/ZW-ZHANG/RandNE

공식 코드

Billion-scale Network Embedding with Iterative Random Projection

https://github.com/Zening-Li/PrivGE

공식 코드

Privacy-Preserving Graph Embedding based on Local Differential Privacy

https://github.com/ZhenyueQin/PSGNN

공식 코드

Position-Sensing Graph Neural Networks: Proactively Learning Nodes Relative Positions

https://github.com/Zhongdao/gcn_clustering

공식 코드

Linkage Based Face Clustering via Graph Convolution Network

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