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Link prediction (LP) has been recognized as an important task in graph learning with its broad practical applications.
Nearest neighbor pattern classification
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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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Matrix factorization techniques for recommender systems
Yehuda Koren, Robert Bell, and Chris Volinsky · 2009
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Predicting missing links via local information
Tao Zhou, Linyuan Lü, and Yi-Cheng Zhang · 2009
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Introduction to modern information retrieval
Gobinda G Chowdhury · 2010
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Literature survey on nearest neighbor search and search in graphs
Philipp M Riegger · 2010
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Link prediction in complex networks: A survey
Linyuan Lü and Tao Zhou · 2011
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Link prediction via matrix factorization
Aditya Krishna Menon and Charles Elkan · 2011
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Fast approximate similarity search based on degree-reduced neighborhood graphs
Kazuo Aoyama, Kazumi Saito, Hiroshi Sawada, and Naonori Ueda · 2011
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Large scale nearest neighbors search based on neighborhood graph
Wenhui Zhou, Chunfeng Yuan, Rong Gu, and Yihua Huang · 2013
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Deepwalk: Online learning of social representations
Bryan Perozzi, Rami Al-Rfou, and Steven Skiena · 2014
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Asymmetric lsh (alsh) for sublinear time maximum inner product search (mips)
Anshumali Shrivastava and Ping Li · 2014
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Learning polynomials with neural networks
Alexandr Andoni, Rina Panigrahy, Gregory Valiant, and Li Zhang · 2014
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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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Víctor Martínez, Fernando Berzal, and Juan-Carlos Cubero · 2016
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Aditya Grover and Jure Leskovec · 2016
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Masajiro Iwasaki · 2016
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Google’s neural machine translation system: Bridging the gap between human and machine translation
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Inductive representation learning on large graphs
Efficient and robust approximate nearest neighbor search using hierarchical navigable small world graphs
Yu A Malkov and Dmitry A Yashunin · 2018
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Latent cross: Making use of context in recurrent recommender systems
Alex Beutel, Paul Covington, Sagar Jain, Can Xu, Jia Li, Vince Gatto, and Ed H Chi · 2018
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A bandit approach to maximum inner product search
Rui Liu, Tianyi Wu, and Barzan Mozafari · 2019
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Möbius transformation for fast inner product search on graph
Zhixin Zhou, Shulong Tan, Zhaozhuo Xu, and Ping Li · 2019
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Accelerating large-scale molecular similarity search through exploiting high performance computing
Chun Jiang Zhu, Tan Zhu, Haining Li, Jinbo Bi, and Minghu Song · 2019
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Will Hamilton, Zhitao Ying, and Jure Leskovec · 2017
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Convergence analysis of two-layer neural networks with relu activation
Yuanzhi Li and Yang Yuan · 2017
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Predictive network representation learning for link prediction
Zhitao Wang, Chengyao Chen, and Wenjie Li · 2017
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Weisfeiler-lehman neural machine for link prediction
Muhan Zhang and Yixin Chen · 2017
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A greedy approach for budgeted maximum inner product search
Hsiang-Fu Yu, Cho-Jui Hsieh, Qi Lei, and Inderjit S Dhillon · 2017
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Fast approximate nearest neighbor search with the navigating spreading-out graph
Cong Fu, Chao Xiang, Changxu Wang, and Deng Cai · 2017
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Query-aware locality-sensitive hashing scheme for l _ p l\_p norm
Qiang Huang, Jianlin Feng, Qiong Fang, Wilfred Ng, and Wei Wang · 2017
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Atsutake Kosuge and Takashi Oshima · 2019
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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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Adaptive graph diffusion networks with hop-wise attention
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Neural collaborative filtering vs. matrix factorization revisited
Steffen Rendle, Walid Krichene, Li Zhang, and John Anderson · 2020
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Norm-explicit quantization: Improving vector quantization for maximum inner product search
Xinyan Dai, Xiao Yan, Kelvin KW Ng, Jiu Liu, and James Cheng · 2020
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Accelerating large-scale inference with anisotropic vector quantization
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Fast item ranking under neural network based measures
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Open graph benchmark: Datasets for machine learning on graphs
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Understanding and improving proximity graph based maximum inner product search
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Pmd: An optimal transportation-based user distance for recommender systems
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Pairwise learning for neural link prediction
Zhitao Wang, Yong Zhou, Litao Hong, Yuanhang Zou, and Hanjing Su · 2021
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Ogb-lsc: A large-scale challenge for machine learning on graphs
Weihua Hu, Matthias Fey, Hongyu Ren, Maho Nakata, Yuxiao Dong, and Jure Leskovec · 2021
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Labeling trick: A theory of using graph neural networks for multi-node representation learning
Muhan Zhang, Pan Li, Yinglong Xia, Kai Wang, and Long Jin · 2021
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Algorithm and system co-design for efficient subgraph-based graph representation learning
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