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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/frankcgq105/BERTCHEN

Attention Is All You Need

https://github.com/franknb/Self-attention-DCGAN

Attention Is All You Need

https://github.com/franknb/Text-Summarization

Attention Is All You Need

https://github.com/fs302/EasyLink

Link Prediction Based on Graph Neural Networks

https://github.com/fs302/EasyLink/blob/main/example/ogbl_ppa_ra.py

Link Prediction Based on Graph Neural Networks

https://github.com/fstahlberg/tensor2tensor

Attention Is All You Need

https://github.com/fumiyo0607/KGAN

Learning Heterogeneous Knowledge Base Embeddings for Explainable Recommendation

https://github.com/galkampel/HyperNetworks

Graph Attention Networks

https://github.com/gavincaoyuji/NLP

Attention Is All You Need

https://github.com/gayanku/FDGATII

Graph Attention Networks

https://github.com/gcucurull/jax-gat

Graph Attention Networks

https://github.com/geffy/tffm

Higher-Order Factorization Machines

https://github.com/gengchenmai/se-kge

SE-KGE: A Location-Aware Knowledge Graph Embedding Model for Geographic Question Answering and Spatial Semantic Lifting

https://github.com/gewu-lab/ogm-ge_cvpr2022

IMKGA-SM: Interpretable Multimodal Knowledge Graph Answer Prediction via Sequence Modeling

https://github.com/gggggeun/DeepLearning_Architecture

Attention Is All You Need

https://github.com/giagia96/DeepComedy

Attention Is All You Need

https://github.com/giantonia/KGATv2

Learning Heterogeneous Knowledge Base Embeddings for Explainable Recommendation

https://github.com/giuseppefutia/link-prediction-code

Graph Attention Networks

https://github.com/giuseppefutia/semi

Embedding Entities and Relations for Learning and Inference in Knowledge Bases

https://github.com/glorotxa/SME

Combining Two And Three-Way Embeddings Models for Link Prediction in Knowledge Bases

https://github.com/glouppe/info8010-deep-learning

Attention Is All You Need

https://github.com/gmum/dl-mo-2021

Attention Is All You Need

https://github.com/gmum/umwpl2021

Graph Attention Networks

https://github.com/going-doer/paper2code

Attention Is All You Need

https://github.com/goldenbili/Bert_Test2

Attention Is All You Need

https://github.com/goldenbili/Bert_Test3

Attention Is All You Need

https://github.com/goldenbili/bert_lamb_pretrain

Attention Is All You Need

https://github.com/goodluck4s/bert-

Attention Is All You Need

https://github.com/google/asymproj_edge_dnn

Learning Edge Representations via Low-Rank Asymmetric Projections

https://github.com/google/flax/tree/master/examples/wmt

Attention Is All You Need

https://github.com/gooofy/zbrain

Attention Is All You Need

https://github.com/gooppe/transformer-summarization

Attention Is All You Need

https://github.com/gordicaleksa/pytorch-GAT

Graph Attention Networks

https://github.com/gordicaleksa/pytorch-original-transformer

Attention Is All You Need

https://github.com/gospodima/extended-simgnn

Hierarchical Graph Representation Learning with Differentiable Pooling

https://github.com/graph-star-team/graph_star

Graph Star Net for Generalized Multi-Task Learning

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