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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/yiyc-kor/bert-study

Attention Is All You Need

https://github.com/ykrmm/ICLR_2020

Attention Is All You Need

https://github.com/ykrmm/TREMBA

Attention Is All You Need

https://github.com/yl-1993/learn-to-cluster

Linkage Based Face Clustering via Graph Convolution Network

https://github.com/yoongtr/Grammar-Error-Correction-with-OpenNMT-tf

Attention Is All You Need

https://github.com/youngandbin/pfotgnrec

Inductive Representation Learning on Temporal Graphs

https://github.com/youngch12/Cluster_KGCN

Knowledge Graph Convolutional Networks for Recommender Systems

https://github.com/ypeleg/MinimalIsAllYouNeed

Attention Is All You Need

https://github.com/ytchx1999/GCN_res-FLAG

Robust Optimization as Data Augmentation for Large-scale Graphs

https://github.com/yuanmu97/secure-transformer-inference

Attention Is All You Need

https://github.com/yueyongjiao/Transformer-Contextual

Attention Is All You Need

https://github.com/yueyongjiao/attention_is_all_you_need_pytorch

Attention Is All You Need

https://github.com/yuhangT/tf_bert

Attention Is All You Need

https://github.com/yumoh/speech-keras

Attention Is All You Need

https://github.com/yururyuru00/GAT_pyG

Graph Attention Networks

https://github.com/yydai/bert_test

Attention Is All You Need

https://github.com/yzhangee/NSCaching

NSCaching: Simple and Efficient Negative Sampling for Knowledge Graph Embedding

https://github.com/zankner/Transformer

Attention Is All You Need

https://github.com/zapplea/bert

Attention Is All You Need

https://github.com/zehong-wang/g2pm

Beyond Message Passing: Neural Graph Pattern Machine

https://github.com/zer0sh0t/artificial_intelligence/tree/master/transformer

Attention Is All You Need

https://github.com/zfjsail/gae-pytorch

Variational Graph Auto-Encoders

https://github.com/zhang-huihui/git-repository

Attention Is All You Need

https://github.com/zhangbo2008/GAT_network

Graph Attention Networks

https://github.com/zhangbo2008/best_pytorch_transformer

Attention Is All You Need

https://github.com/zhangfw123/TLT-KGE

https://github.com/zhanglab-aim/cancer-net

Graph Attention Networks

https://github.com/zhao-tong/GNNs-easy-to-use

Graph Attention Networks

https://github.com/zhengjingwei/cluster_GCN

Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks

https://github.com/zhenv5/atp

ATP: Directed Graph Embedding with Asymmetric Transitivity Preservation

https://github.com/zhitao-wang/PLNLP

Pairwise Learning for Neural Link Prediction

https://github.com/zhiweihu1103/qe-temp

Logic Embeddings for Complex Query Answering

https://github.com/zhliping/Deep-Learning

Attention Models in Graphs: A Survey

https://github.com/zipeiyang/DeepSpeechRecognition

Attention Is All You Need

https://github.com/zjs123/streame

Temporal Knowledge Graph Embedding Model based on Additive Time Series Decomposition

https://github.com/zju-dbl/metakg

KGAT: Knowledge Graph Attention Network for Recommendation

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