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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/why2000/DuReader-bert

Attention Is All You Need

https://github.com/wrk226/Translating-Embeddings-for-Modeling-Multi-relational-Data

https://github.com/wxt1997/Transformer-Transducer

Attention Is All You Need

https://github.com/wyd1502/DGNN

Streaming Graph Neural Networks

https://github.com/wzlxjtu/PositionalEncoding2D

Attention Is All You Need

https://github.com/xandaschofield/locally_private_bpf_icml19

Locally Private Bayesian Inference for Count Models

https://github.com/xavi-ai/relational-implication-dataset

Probabilistic Models of Relational Implication

https://github.com/xbtlin/All-about-Machine-Learning

Attention Is All You Need

https://github.com/xcmyz/FastSpeech

Attention Is All You Need

https://github.com/xesdiny/test-bert-master

Attention Is All You Need

https://github.com/xiaoleiHou214/BGCF-pytorch-master

Neural Graph Collaborative Filtering

https://github.com/xiaoleiHou214/gc-mc-master

Graph Convolutional Matrix Completion

https://github.com/xiaopingzhong/bert-finetune-for-classfier

Attention Is All You Need

https://github.com/xiaopp123/bert_explain

Attention Is All You Need

https://github.com/xiaoxiong74/face-cluster-by-infomap

Linkage Based Face Clustering via Graph Convolution Network

https://github.com/xingjian-zhang/massw

Attention Is All You Need

https://github.com/xingniu/sockeye

Attention Is All You Need

https://github.com/xitianxiaofeixue/BERT

Attention Is All You Need

https://github.com/xiyou3368/DGVAE

Variational Graph Auto-Encoders

https://github.com/xmu-xiaoma666/External-Attention-pytorch

Attention Is All You Need

https://github.com/xuChenSJTU/SAT-master-link-prediction

Node Attribute Generation on Graphs

https://github.com/xuChenSJTU/SAT-master-node-attribute-completion

Node Attribute Generation on Graphs

https://github.com/xue-pai/FuxiCTR

Higher-Order Factorization Machines

https://github.com/xuehansheng/dualhgcn

Multiplex Bipartite Network Embedding using Dual Hypergraph Convolutional Networks

https://github.com/xuzhezhaozhao/bert_reading

Attention Is All You Need

https://github.com/xydaytoy/BMI-NMT

Attention Is All You Need

https://github.com/yCobanoglu/infinite-width-gnns

Line Graph Neural Networks for Link Prediction

https://github.com/yanghaoxie/GCN-GAN-for-Weighted-Dynamic-Networks

GCN-GAN: A Non-linear Temporal Link Prediction Model for Weighted Dynamic Networks

https://github.com/yangyucheng000/Papers/tree/main/MindSpore-Retrieval-augmented

Modeling Relational Data with Graph Convolutional Networks

https://github.com/yangyucheng000/TransX-2

https://github.com/yangyucheng000/transX

https://github.com/yanqi1811/attention-is-all-you-need

Attention Is All You Need

https://github.com/yashkotadia/GatedGCN-PATTERN

Benchmarking Graph Neural Networks

https://github.com/ydtydr/hyla

Hyperbolic Graph Convolutional Neural Networks

https://github.com/ydzhang-stormstout/HAT

Graph Attention Networks

https://github.com/yeeeqichen/Bert

Attention Is All You Need

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