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Link prediction, a fundamental task on graphs, has proven indispensable in various applications, e.g., friend recommendation, protein analysis, and drug interaction prediction.
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Jure Leskovec, Daniel Huttenlocher, and Jon Kleinberg · 2010
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Theoretical justification of popular link prediction heuristics
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Tore Opsahl · 2013
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Applications of link prediction in social networks: A review
Nur Nasuha Daud, Siti Hafizah Ab Hamid, Muntadher Saadoon, Firdaus Sahran, and Nor Badrul Anuar · 2020
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Lightgcn: Simplifying and powering graph convolution network for recommendation
Xiangnan He, Kuan Deng, Xiang Wang, Yan Li, Yongdong Zhang, and Meng Wang · 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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The homophily principle in social network analysis
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Maximilian Nickel, Xueyan Jiang, and Volker Tresp · 2014
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Triadic closure pattern analysis and prediction in social networks
Hong Huang, Jie Tang, Lu Liu, JarDer Luo, and Xiaoming Fu · 2015
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Image-based recommendations on styles and substitutes
Julian McAuley, Christopher Targett, Qinfeng Shi, and Anton Van Den Hengel · 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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Network representation learning with rich text information
Cheng Yang, Zhiyuan Liu, Deli Zhao, Maosong Sun, and Edward Y Chang · 2015
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node2vec: Scalable feature learning for networks
Aditya Grover and Jure Leskovec · 2016
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Variational graph auto-encoders
Thomas N Kipf and Max Welling · 2016
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Link prediction techniques, applications, and performance: A survey
Ajay Kumar, Shashank Sheshar Singh, Kuldeep Singh, and Bhaskar Biswas · 2020
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From closing triangles to closing higher-order motifs
Ryan A Rossi, Anup Rao, Sungchul Kim, Eunyee Koh, and Nesreen Ahmed · 2020
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Seven-layer model in complex networks link prediction: A survey
Hui Wang and Zichun Le · 2020
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The paradox of second-order homophily in networks
Anna Evtushenko and Jon Kleinberg · 2021
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Inequality is rising where social network segregation interacts with urban topology
Gergő Tóth, Johannes Wachs, Riccardo Di Clemente, Ákos Jakobi, Bence Ságvári, János Kertész, and Balázs Lengyel · 2021
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Hashing-accelerated graph neural networks for link prediction
Wei Wu, Bin Li, Chuan Luo, and Wolfgang Nejdl · 2021
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Neo-gnns: Neighborhood overlap-aware graph neural networks for link prediction
Seongjun Yun, Seoyoon Kim, Junhyun Lee, Jaewoo Kang, and Hyunwoo J Kim · 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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On the effect of triadic closure on network segregation
Rediet Abebe, Nicole Immorlica, Jon Kleinberg, Brendan Lucier, and Ali Shirali · 2022
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Avoiding biases due to similarity assumptions in node embeddings
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Revisiting neighborhood-based link prediction for collaborative filtering
Hao-Ming Fu, Patrick Poirson, Kwot Sin Lee, and Chen Wang · 2022
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Geodesic graph neural network for efficient graph representation learning
Lecheng Kong, Yixin Chen, and Muhan Zhang · 2022
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Can gnns learn heuristic information for link prediction?
Shuming Liang, Yu Ding, Zhidong Li, Bin Liang, Yang Wang, Fang Chen, et al · 2022
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Equivariant and stable positional encoding for more powerful graph neural networks
Haorui Wang, Haoteng Yin, Muhan Zhang, and Pan Li · 2022
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Algorithm and system co-design for efficient subgraph-based graph representation learning
Haoteng Yin, Muhan Zhang, Yanbang Wang, Jianguo Wang, and Pan Li · 2022
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Ood link prediction generalization capabilities of message-passing gnns in larger test graphs
Yangze Zhou, Gitta Kutyniok, and Bruno Ribeiro · 2022
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Learning to efficiently propagate for reasoning on knowledge graphs
Zhaocheng Zhu, Xinyu Yuan, Louis-Pascal Xhonneux, Ming Zhang, Maxime Gazeau, and Jian Tang · 2022
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Graph neural networks for link prediction with subgraph sketching
Benjamin Paul Chamberlain, Sergey Shirobokov, Emanuele Rossi, Fabrizio Frasca, Thomas Markovich, Nils Hammerla, Michael M Bronstein, and Max Hansmire · 2023
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Evaluating graph neural networks for link prediction: Current pitfalls and new benchmarking
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Simga: A simple and effective heterophilous graph neural network with efficient global aggregation
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Neural common neighbor with completion for link prediction
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Surel+: Moving from walks to sets for scalable subgraph-based graph representation learning
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