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Link prediction is a critical problem in graph learning with broad applications such as recommender systems and knowledge graph completion.
Emergence of scaling in random networks
Albert-László Barabási and Réka Albert · 1999
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Friends and neighbors on the web
Lada A Adamic and Eytan Adar · 2003
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The link prediction problem for social networks
David Liben-Nowell and Jon Kleinberg · 2003
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Fast random walk with restart and its applications
Hanghang Tong, Christos Faloutsos, and Jia-Yu Pan · 2006
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Graph evolution: Densification and shrinking diameters
Jure Leskovec, Jon Kleinberg, and Christos Faloutsos · 2007
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Stochastic linear optimization under bandit feedback, 2008
Varsha Dani, Thomas P Hayes, and Sham M Kakade · 2008
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Predicting missing links via local information
Tao Zhou, Linyuan Lü, and Yi-Cheng Zhang · 2009
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A contextual-bandit approach to personalized news article recommendation
Lihong Li, Wei Chu, John Langford, and Robert E Schapire · 2010
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Improved algorithms for linear stochastic bandits
Yasin Abbasi-Yadkori, Dávid Pál, and Csaba Szepesvári · 2011
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Node classification in social networks
Smriti Bhagat, Graham Cormode, and S Muthukrishnan · 2011
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Link prediction in complex networks: A survey
Linyuan Lü and Tao Zhou · 2011
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Learning to discover social circles in ego networks
Jure Leskovec and Julian Mcauley · 2012
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Thompson sampling for contextual bandits with linear payoffs
Shipra Agrawal and Navin Goyal · 2013
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Finite-time analysis of kernelised contextual bandits
Michal Valko, Nathaniel Korda, Rémi Munos, Ilias Flaounas, and Nelo Cristianini · 2013
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Deepwalk: Online learning of social representations
Bryan Perozzi, Rami Al-Rfou, and Steven Skiena · 2014
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The movielens datasets: History and context
F Maxwell Harper and Joseph A Konstan · 2015
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A review of relational machine learning for knowledge graphs
Maximilian Nickel, Kevin Murphy, Volker Tresp, and Evgeniy Gabrilovich · 2015
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Line: Large-scale information network embedding
Jian Tang, Meng Qu, Mingzhe Wang, Ming Zhang, Jun Yan, and Qiaozhu Mei · 2015
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node2vec: Scalable feature learning for networks
Aditya Grover and Jure Leskovec · 2016
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Semi-supervised classification with graph convolutional networks
Thomas N Kipf and Max Welling · 2016
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Collaborative filtering bandits
Shuai Li, Alexandros Karatzoglou, and Claudio Gentile · 2016
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Revisiting semi-supervised learning with graph embeddings
Zhilin Yang, William Cohen, and Ruslan Salakhudinov · 2016
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Linear thompson sampling revisited
Marc Abeille and Alessandro Lazaric · 2017
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Neural message passing for quantum chemistry
Justin Gilmer, Samuel S Schoenholz, Patrick F Riley, Oriol Vinyals, and George E Dahl · 2017
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Inductive representation learning on large graphs
Will Hamilton, Zhitao Ying, and Jure Leskovec · 2017
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Xiuyuan Lu and Benjamin Van Roy · 2017
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Neural tangent kernel: Convergence and generalization in neural networks
Arthur Jacot, Franck Gabriel, and Clément Hongler · 2018
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Carlos Riquelme, George Tucker, and Jasper Snoek · 2018
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Link prediction based on graph neural networks
Muhan Zhang and Yixin Chen · 2018
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A convergence theory for deep learning via over-parameterization
Zeyuan Allen-Zhu, Yuanzhi Li, and Zhao Song · 2019
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On exact computation with an infinitely wide neural net
Sanjeev Arora, Simon S Du, Wei Hu, Zhiyuan Li, Russ R Salakhutdinov, and Ruosong Wang · 2019
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Generalization bounds of stochastic gradient descent for wide and deep neural networks
Yuan Cao and Quanquan Gu · 2019
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Gradient descent finds global minima of deep neural networks
Zhongxiang Dai, Yao Shu, Arun Verma, Flint Xiaofeng Fan, Bryan Kian Hsiang Low, and Patrick Jaillet · 2022
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Data augmentation for deep graph learning: A survey
Kaize Ding, Zhe Xu, Hanghang Tong, and Huan Liu · 2022
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Disco: Comprehensive and explainable disinformation detection
Dongqi Fu, Yikun Ban, Hanghang Tong, Ross Maciejewski, and Jingrui He · 2022
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Meta-learned metrics over multi-evolution temporal graphs
Dongqi Fu, Liri Fang, Ross Maciejewski, Vetle I. Torvik, and Jingrui He · 2022
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Learning neural contextual bandits through perturbed rewards
Yiling Jia, Weitong Zhang, Dongruo Zhou, Quanquan Gu, and Hongning Wang · 2022
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Neural bandit with arm group graph
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Simon Du, Jason Lee, Haochuan Li, Liwei Wang, and Xiyu Zhai · 2019
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Justifying recommendations using distantly-labeled reviews and fine-grained aspects
Jianmo Ni, Jiacheng Li, and Julian McAuley · 2019
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Inductive matrix completion based on graph neural networks
Muhan Zhang and Yixin Chen · 2019
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Generic outlier detection in multi-armed bandit
Yikun Ban and Jingrui He · 2020
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Open graph benchmark: Datasets for machine learning on graphs
Weihua Hu, Matthias Fey, Marinka Zitnik, Yuxiao Dong, Hongyu Ren, Bowen Liu, Michele Catasta, and Jure Leskovec · 2020
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Temporal graph networks for deep learning on dynamic graphs
Emanuele Rossi, Ben Chamberlain, Fabrizio Frasca, Davide Eynard, Federico Monti, and Michael Bronstein · 2020
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Inductive representation learning on temporal graphs
Da Xu, Chuanwei Ruan, Evren Korpeoglu, Sushant Kumar, and Kannan Achan · 2020
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Yunzhe Qi, Yikun Ban, and Jingrui He · 2022
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Graph sanitation with application to node classification
Zhe Xu, Boxin Du, and Hanghang Tong · 2022
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Neural exploitation and exploration of contextual bandits
Yikun Ban, Yuchen Yan, Arindam Banerjee, and Jingrui He · 2023
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Do we really need complicated model architectures for temporal networks?
Weilin Cong, Si Zhang, Jian Kang, Baichuan Yuan, Hao Wu, Xin Zhou, Hanghang Tong, and Mehrdad Mahdavi · 2023
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Contextual bandits with online neural regression
Rohan Deb, Yikun Ban, Shiliang Zuo, Jingrui He, and Arindam Banerjee · 2023
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Alleviating matthew effect of offline reinforcement learning in interactive recommendation
Chongming Gao, Kexin Huang, Jiawei Chen, Yuan Zhang, Biao Li, Peng Jiang, Shiqi Wang, Zhong Zhang, and Xiangnan He · 2023
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Everything evolves in personalized pagerank
Zihao Li, Dongqi Fu, and Jingrui He · 2023
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Graph neural bandits
Yunzhe Qi, Yikun Ban, and Jingrui He · 2023
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Yuan Sui, Jiaru Zou, Mengyu Zhou, Xinyi He, Lun Du, Shi Han, and Dongmei Zhang · 2023
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Freedyg: Frequency enhanced continuous-time dynamic graph model for link prediction
Yuxing Tian, Yiyan Qi, and Fan Guo · 2023
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Neural common neighbor with completion for link prediction
Xiyuan Wang, Haotong Yang, and Muhan Zhang · 2023
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Node classification beyond homophily: Towards a general solution
Zhe Xu, Yuzhong Chen, Qinghai Zhou, Yuhang Wu, Menghai Pan, Hao Yang, and Hanghang Tong · 2023
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Towards better dynamic graph learning: New architecture and unified library
Le Yu, Leilei Sun, Bowen Du, and Weifeng Lv · 2023
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Neural active learning beyond bandits
Yikun Ban, Ishika Agarwal, Ziwei Wu, Yada Zhu, Kommy Weldemariam, Hanghang Tong, and Jingrui He · 2024
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Meta clustering of neural bandits
Yikun Ban, Yunzhe Qi, Tianxin Wei, Lihui Liu, and Jingrui He · 2024
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Sphere: Expressive and interpretable knowledge graph embedding for set retrieval
Zihao Li, Yuyi Ao, and Jingrui He · 2024
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Bemap: Balanced message passing for fair graph neural network
Xiao Lin, Jian Kang, Weilin Cong, and Hanghang Tong · 2024
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Backtime: Backdoor attacks on multivariate time series forecasting
Xiao Lin, Zhining Liu, Dongqi Fu, Ruizhong Qiu, and Hanghang Tong · 2024
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Meta-learning with neural bandit scheduler
Yunzhe Qi, Yikun Ban, Tianxin Wei, Jiaru Zou, Huaxiu Yao, and Jingrui He · 2024
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Heterogeneous contrastive learning for foundation models and beyond
Lecheng Zheng, Baoyu Jing, Zihao Li, Hanghang Tong, and Jingrui He · 2024
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Jiaru Zou, Mengyu Zhou, Tao Li, Shi Han, and Dongmei Zhang · 2024
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