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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/coxy1989/tfmr

Attention Is All You Need

https://github.com/cpm0722/transformer_pytorch

Attention Is All You Need

https://github.com/cuber2460/bert

Attention Is All You Need

https://github.com/cui0523/Code6/tree/main/rotate

RotatE: Knowledge Graph Embedding by Relational Rotation in Complex Space

https://github.com/cvg/scrstudio

node2vec: Scalable Feature Learning for Networks

https://github.com/cyberzhg/keras-pos-embd

Attention Is All You Need

https://github.com/cynricfu/MAGNN

MAGNN: Metapath Aggregated Graph Neural Network for Heterogeneous Graph Embedding

https://github.com/czero69/acomoeye-NN

Attention Is All You Need

https://github.com/dair-iitd/kbc-baseline

Knowledge Base Completion: Baseline strikes back (Again)

https://github.com/danielegrattarola/keras-gat

Graph Attention Networks

https://github.com/danielzuegner/netgan

NetGAN: Generating Graphs via Random Walks

https://github.com/davendw49/gakg

SimplE Embedding for Link Prediction in Knowledge Graphs

https://github.com/davidalbertonogueira/NLP-tutorials

Attention Is All You Need

https://github.com/davidpicard/homm

Graph Attention Networks

https://github.com/davisking/dlib-models

Attention Is All You Need

https://github.com/dccuchile/CC6204

Attention Is All You Need

https://github.com/deepmind/haiku

Attention Is All You Need

https://github.com/deepmipt/bert/tree/feat/multi_gpu

Attention Is All You Need

https://github.com/demelin/nematode

Attention Is All You Need

https://github.com/derylucio/Transformer

Attention Is All You Need

https://github.com/dglai/wsdm2022-challenge

Modeling Relational Data with Graph Convolutional Networks

https://github.com/dhiraa/tener

Attention Is All You Need

https://github.com/dice-group/hybridfc

Convolutional Complex Knowledge Graph Embeddings

https://github.com/dinobby/hypemo

Hyperbolic Entailment Cones for Learning Hierarchical Embeddings

https://github.com/dl-nlp/dl-nlp.github.io

Attention Is All You Need

https://github.com/dmeoli/neuro-sat

Graph Attention Networks

https://github.com/dmlc/dgl

Recurrent Event Network: Autoregressive Structure Inference over Temporal Knowledge Graphs

https://github.com/dmlc/dgl/blob/master/python/dgl/nn/pytorch/conv/gatv2conv.py

How Attentive are Graph Attention Networks?

https://github.com/dmlc/dgl/tree/master/examples/mxnet/_deprecated/gcmc

Graph Convolutional Matrix Completion

https://github.com/dmlc/dgl/tree/master/examples/mxnet/gat

Graph Attention Networks

https://github.com/dmlc/dgl/tree/master/examples/mxnet/gin

Inductive Representation Learning on Large Graphs

https://github.com/dmlc/dgl/tree/master/examples/mxnet/graphsage

Inductive Representation Learning on Large Graphs

https://github.com/dmlc/dgl/tree/master/examples/mxnet/rgcn

Modeling Relational Data with Graph Convolutional Networks

https://github.com/dmlc/dgl/tree/master/examples/pytorch/GATNE-T

Representation Learning for Attributed Multiplex Heterogeneous Network

https://github.com/dmlc/dgl/tree/master/examples/pytorch/NGCF

Neural Graph Collaborative Filtering

https://github.com/dmlc/dgl/tree/master/examples/pytorch/P-GNN

Position-aware Graph Neural Networks

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