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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/emanuele-progr/PSSP

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https://github.com/enhuiz/torchnmt

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https://github.com/enhuiz/transformer-pytorch

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https://github.com/ericperfect/Bert_Position_BiLSTM_Attention_CRF_LSTMDecoder

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https://github.com/ermongroup/graphite

Graphite: Iterative Generative Modeling of Graphs

https://github.com/espectre/GCNs_on_imbalanced_datasets

Linkage Based Face Clustering via Graph Convolution Network

https://github.com/ethancaballero/neural-engineers-first-attempt

Attention Is All You Need

https://github.com/eunseo1092/Graduation_project

Attention Is All You Need

https://github.com/ewdowiak/Sicilian_Translator

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https://github.com/exa-labs/exa-mcp-server

Attention Is All You Need

https://github.com/extensive-nlp/TSAI-DeepNLP-END2.0

Attention Is All You Need

https://github.com/facebookresearch/PyTorch-BigGraph

Embedding Entities and Relations for Learning and Inference in Knowledge Bases

https://github.com/facebookresearch/clutrr

Learning Reasoning Strategies in End-to-End Differentiable Proving

https://github.com/facebookresearch/fairseq

Attention Is All You Need

https://github.com/facebookresearch/poincare-embeddings

Poincaré Embeddings for Learning Hierarchical Representations

https://github.com/facebookresearch/taser-tgnn

TGL: A General Framework for Temporal GNN Training on Billion-Scale Graphs

https://github.com/facebookresearch/xformers

Attention Is All You Need

https://github.com/fahrbach/icml-2020-faster-graph-embeddings

node2vec: Scalable Feature Learning for Networks

https://github.com/faizansuhail89/bert

Attention Is All You Need

https://github.com/fanchenyou/transformer-study

Attention Is All You Need

https://github.com/fancyerii/bert

Attention Is All You Need

https://github.com/fanzhenliu/dagad

Graph Attention Networks

https://github.com/fciannel/bert_fciannel

Attention Is All You Need

https://github.com/fdb78/NCF_recommender_systems

Neural Collaborative Filtering vs. Matrix Factorization Revisited

https://github.com/feizc/Machine-Translation-Pytorch

Attention Is All You Need

https://github.com/felixSchober/ABSA-Transformer

Attention Is All You Need

https://github.com/fjiangai/mmapis

Attention Is All You Need

https://github.com/flawless1202/vgae_pyg

Variational Graph Auto-Encoders

https://github.com/fleverest/gravity_graph_autoencoders

Variational Graph Auto-Encoders

https://github.com/florianeBhz/NGCF_Pytorch

Neural Graph Collaborative Filtering

https://github.com/flxst/nerblackbox

Attention Is All You Need

https://github.com/flyingdoog/PTDNet

Learning to Drop: Robust Graph Neural Network via Topological Denoising

https://github.com/flyliu2017/bert

Attention Is All You Need

https://github.com/flyliu2017/bert_modularized

Attention Is All You Need

https://github.com/fongyk/graph-attention

Graph Attention Networks

https://github.com/fra-mari/The-Karl-Marx-Press-Review

Attention Is All You Need

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