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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/sonalisrijan/Deep-Learning-based-Defect-Prediction-Model-for-Source-Code

Attention Is All You Need

https://github.com/sonrisa07/transformer

Attention Is All You Need

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

Attention Is All You Need

https://github.com/sooftware/attentions

Attention Is All You Need

https://github.com/sooftware/nlp-attentions

Attention Is All You Need

https://github.com/soumik12345/transformer.pytorch

Attention Is All You Need

https://github.com/souvikshanku/translit-former

Attention Is All You Need

https://github.com/sreycodes/YLR

Link Prediction Based on Graph Neural Networks

https://github.com/sseung0703/GALA_TF2.0

Symmetric Graph Convolutional Autoencoder for Unsupervised Graph Representation Learning

https://github.com/stellargraph/stellargraph

Complex Embeddings for Simple Link Prediction

https://github.com/stevinc/Transformer_Timeseries

Attention Is All You Need

https://github.com/studio-ousia/luke

Attention Is All You Need

https://github.com/subercui/pyGConvAT

Graph Attention Networks

https://github.com/sunlab-osu/BioNEV

Graph Embedding on Biomedical Networks: Methods, Applications, and Evaluations

https://github.com/sunnan191/evisec

EvolveGCN: Evolving Graph Convolutional Networks for Dynamic Graphs

https://github.com/susurrant/flow-imputation

Modeling Relational Data with Graph Convolutional Networks

https://github.com/susurrant/spatial-interaction-modeling

Modeling Relational Data with Graph Convolutional Networks

https://github.com/svakulenk0/response_eval

Attention Is All You Need

https://github.com/svjan5/gnns-for-nlp

Neural Graph Embedding Methods for Natural Language Processing

https://github.com/swimmingCreative/DeepSpeechRecognition-master

Attention Is All You Need

https://github.com/swtheing/Multiview-Link-Representation-Learning

Graph Convolutional Matrix Completion

https://github.com/taishan1994/pytorch_gat

Graph Attention Networks

https://github.com/talkingwallace/NGCF-pytorch

Neural Graph Collaborative Filtering

https://github.com/tanimutomo/gcmc

Graph Convolutional Matrix Completion

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

Attention Is All You Need

https://github.com/taohu88/recommendations

Higher-Order Factorization Machines

https://github.com/tatp22/linformer-pytorch

Attention Is All You Need

https://github.com/tatp22/multidim-positional-encoding

Attention Is All You Need

https://github.com/tatp22/pytorch-fast-GAT

Graph Attention Networks

https://github.com/tbmoon/LANL_Earthquake_Prediction

Attention Is All You Need

https://github.com/tcnguyen/bert

Attention Is All You Need

https://github.com/tcxdgit/MachineTranslation_t2t

Attention Is All You Need

https://github.com/temibabs/I-RGCN

Few-shot link prediction via graph neural networks for Covid-19 drug-repurposing

https://github.com/tensorflow/gnn/blob/main/tensorflow_gnn/docs/api_docs/python/gnn/keras/layers/GATv2.md

How Attentive are Graph Attention Networks?

https://github.com/tensorflow/neural-structured-learning/tree/master/research/kg_hyp_emb

Low-Dimensional Hyperbolic Knowledge Graph Embeddings

https://github.com/tensorops/TransformerX

Attention Is All You Need

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