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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/scrayish/ML_NLP

Attention Is All You Need

https://github.com/seeyourmind/tkgelib

HittER: Hierarchical Transformers for Knowledge Graph Embeddings

https://github.com/semal/bert

Attention Is All You Need

https://github.com/semicontinuity/nlp

Attention Is All You Need

https://github.com/seominseok0429/inception-I3D-NON-LOCAL

Attention Is All You Need

https://github.com/seunghwan1228/Transfomer-MachineTranslation

Attention Is All You Need

https://github.com/sh951011/Attention-Implementation

Attention Is All You Need

https://github.com/shahrukhx01/ocr-test

Attention Is All You Need

https://github.com/shaikhzhas/bert

Attention Is All You Need

https://github.com/shao-chi/ImageCaption

Attention Is All You Need

https://github.com/shawon100/Web-Link-Prediction-System

Web Links Prediction And Category-Wise Recommendation Based On Browser History

https://github.com/shenweichen/DeepCTR

Neural Factorization Machines for Sparse Predictive Analytics

https://github.com/shenweichen/DeepCTR-Torch

Neural Factorization Machines for Sparse Predictive Analytics

https://github.com/shenweichen/GraphEmbedding

LINE: Large-scale Information Network Embedding

https://github.com/shenweichen/GraphNeuralNetwork

Graph Attention Networks

https://github.com/sherinaseri/bert

Attention Is All You Need

https://github.com/shi27feng/transformers.satisfy

Graph Attention Networks

https://github.com/shijx12/kqapro_baselines

Modeling Relational Data with Graph Convolutional Networks

https://github.com/shouvikcirca/Transformer

Attention Is All You Need

https://github.com/shouvikcirca/Transformer_MiniProject

Attention Is All You Need

https://github.com/shreyash8/English_to_Hindi_NMT

Attention Is All You Need

https://github.com/shreyashankar/gpt3-sandbox

Attention Is All You Need

https://github.com/shuaishuaij/Machine-Translation

Attention Is All You Need

https://github.com/shuo-git/GraphGAN

GraphGAN: Graph Representation Learning with Generative Adversarial Nets

https://github.com/shyam-skk/Parallel_CNN_Transfromer

Attention Is All You Need

https://github.com/silent-code/deep-trace

Inductive Representation Learning on Large Graphs

https://github.com/simonepiaggesi/dine

DINE: Dimensional Interpretability of Node Embeddings

https://github.com/simonjisu/annotated-transformer-kr

Attention Is All You Need

https://github.com/sjyttkl/Transformer_learning

Attention Is All You Need

https://github.com/skepsun/SAGN_with_SLE

Adaptive Graph Diffusion Networks

https://github.com/skojaku/stacklp

Stacking Models for Nearly Optimal Link Prediction in Complex Networks

https://github.com/snap-stanford/graphgym

Identity-aware Graph Neural Networks

https://github.com/snowkylin/gnn

Graph Attention Networks

https://github.com/snownus/COOP

Graph Attention Networks

https://github.com/sntcristian/and-kge

Embedding Entities and Relations for Learning and Inference in Knowledge Bases

https://github.com/socc-io/piqaboo

Attention Is All You Need

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