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Graph representation learning models aim to represent the graph structure and its features into low-dimensional vectors in a latent space, which can benefit various downstream tasks, such as node classification and link prediction.
Translating embeddings for modeling multi-relational data
Antoine Bordes, Nicolas Usunier, Alberto García-Durán, Jason Weston, and Oksana Yakhnenko · 2013
Earlier work this paper cites.
Deepwalk: online learning of social representations
Bryan Perozzi, Rami Al-Rfou, and Steven Skiena · 2014
Earlier work this paper cites.
Knowledge graph embedding by translating on hyperplanes
Zhen Wang, Jianwen Zhang, Jianlin Feng, and Zheng Chen · 2014
Earlier work this paper cites.
Learning entity and relation embeddings for knowledge graph completion
Yankai Lin, Zhiyuan Liu, Maosong Sun, Yang Liu, and Xuan Zhu · 2015
Earlier work this paper cites.
node2vec: Scalable feature learning for networks
Aditya Grover and Jure Leskovec · 2016
Earlier work this paper cites.
Asymmetric transitivity preserving graph embedding
Mingdong Ou, Peng Cui, Jian Pei, Ziwei Zhang, and Wenwu Zhu · 2016
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Semi-supervised classification with graph convolutional networks
Thomas N. Kipf and Max Welling · 2017
Cited alongside, same era.
struc2vec : Learning node representations from structural identity
Leonardo Filipe Rodrigues Ribeiro, Pedro H. P. Saverese, and Daniel R. Figueiredo · 2017
Cited alongside, same era.
Signed network embedding in social media
Suhang Wang, Jiliang Tang, Charu C. Aggarwal, Yi Chang, and Huan Liu · 2017
Cited alongside, same era.
metapath2vec: Scalable representation learning for heterogeneous networks
Yuxiao Dong, Nitesh V. Chawla, and Ananthram Swami · 2017
Cited alongside, same era.
Inductive representation learning on large graphs
William L. Hamilton, Zhitao Ying, and Jure Leskovec · 2017
Cited alongside, same era.
Graph attention networks, 2017
Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Liò, and Yoshua Bengio · 2017
Fast graph representation learning with pytorch geometric
Matthias Fey and Jan Eric Lenssen · 2019
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How powerful are graph neural networks?
Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka · 2019
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Deep graph library: A graph-centric, highly-performant package for graph neural networks
Minjie Wang, Da Zheng, Zihao Ye, Quan Gan, Mufei Li, Xiang Song, Jinjing Zhou, Chao Ma, Lingfan Yu, Yu Gai, Tianjun Xiao, Tong He, George Karypis, Jinyang Li, and Zheng Zhang · 2019
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Graph representation learning: a survey
Fenxiao Chen, Yun-Cheng Wang, Bin Wang, and C.-C Kuo · 2020
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Cogdl: An extensive toolkit for deep learning on graphs
Yukuo Cen, Zhenyu Hou, Yan Wang, Qibin Chen, Yizhen Luo, Xingcheng Yao, Aohan Zeng, Shiguang Guo, Peng Zhang, Guohao Dai, Yu Wang, Chang Zhou, Hongxia Yang, and Jie Tang · 2021
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Cited alongside, same era.
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Graph representation learning and its applications: A survey
Van Thuy Hoang , Hyeon-Ju Jeon , Eun-Soon You , Yoewon Yoon , Sungyeop Jung , and O-Joun Lee · 2023
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