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Link prediction is a crucial task in graph machine learning, where the goal is to infer missing or future links within a graph.
A new status index derived from sociometric analysis
Leo Katz. 1953 · 1953
Earlier work this paper cites.
Stochastic blockmodels: First steps
Paul Holland, Kathryn B. Laskey, and Samuel Leinhardt. 1983 · 1983
Earlier work this paper cites.
Collective dynamics of ‘small-world’ networks
Duncan J. Watts and Steven H. Strogatz. 1998 · 1998
Earlier work this paper cites.
Emergence of Scaling in Random Networks
Albert-László Barabási and Réka Albert. 1999 · 1999
Earlier work this paper cites.
The PageRank Citation Ranking : Bringing Order to the Web. In The Web Conference
Lawrence Page, Sergey Brin, Rajeev Motwani, and Terry Winograd. 1999 · 1999
Earlier work this paper cites.
Measuring ISP topologies with Rocketfuel
Neil Spring, Ratul Mahajan, and David Wetherall. 2002 · 2002
Earlier work this paper cites.
Comparative assessment of large-scale data sets of protein–protein interactions
Christian Von Mering, Roland Krause, Berend Snel, Michael Cornell, Stephen G Oliver, Stanley Fields, and Peer Bork. 2002 · 2002
Earlier work this paper cites.
Friends and neighbors on the Web
Lada A. Adamic and Eytan Adar. 2003 · 2003
Earlier work this paper cites.
The link prediction problem for social networks. In Proceedings of the twelfth international conference on Information and knowledge management (CIKM ’03) . Association for Computing Machinery, New York, NY, USA, 556–559
David Liben-Nowell and Jon Kleinberg. 2003 · 2003
Earlier work this paper cites.
The political blogosphere and the 2004 U.S. election: divided they blog. In Proceedings of the 3rd international workshop on Link discovery (LinkKDD ’05) . Association for Computing Machinery, New York, NY, USA, 36–43
Lada A. Adamic and Natalie Glance. 2005 · 2004
Earlier work this paper cites.
Mapping the US political blogosphere: Are conservative bloggers more prominent?. In BlogTalk Downunder 2005 Conference, Sydney
Robert Ackland and others. 2005 · 2005
Earlier work this paper cites.
Language Models are Few-Shot Learners
Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel M. Ziegler, Jeffrey Wu, Clemens Winter, Christopher Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei. 2020 · 2005
Earlier work this paper cites.
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. 2021 · 2005
Earlier work this paper cites.
Pajek datasets website
Vladimir Batagelj and Andrej Mrvar. 2006 · 2006
Earlier work this paper cites.
Finding community structure in networks using the eigenvectors of matrices
Mark EJ Newman. 2006a · 2006
Earlier work this paper cites.
Modularity and community structure in networks
M. E. J. Newman. 2006b · 2006
Earlier work this paper cites.
Matrix factorization techniques for recommender systems
Yehuda Koren, Robert Bell, and Chris Volinsky. 2009 · 2009
Earlier work this paper cites.
Predicting missing links via local information
Tao Zhou, Linyuan Lü, and Yi-Cheng Zhang. 2009 · 2009
Earlier work this paper cites.
Networks, crowds, and markets: Reasoning about a highly connected world . Vol. 1
David Easley, Jon Kleinberg, and others. 2010 · 2010
Earlier work this paper cites.
DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition. In Proceedings of the 31st International Conference on Machine Learning (Proceedings of Machine Learning Research, Vol. 32) , Eric P. Xing and Tony Jebara (Eds.). PMLR, Bejing, China, 647–655
Jeff Donahue, Yangqing Jia, Oriol Vinyals, Judy Hoffman, Ning Zhang, Eric Tzeng, and Trevor Darrell. 2014 · 2014
Cited alongside, same era.
How transferable are features in deep neural networks?. In Proceedings of the 27th International Conference on Neural Information Processing Systems - Volume 2 (NIPS’14) . MIT Press, Cambridge, MA, USA, 3320–3328
Jason Yosinski, Jeff Clune, Yoshua Bengio, and Hod Lipson. 2014 · 2014
Cited alongside, same era.
Evaluating link prediction methods
Yang Yang, Ryan N. Lichtenwalter, and Nitesh V. Chawla. 2015 · 2015
Cited alongside, same era.
Variational Graph Auto-Encoders
Thomas N. Kipf and Max Welling. 2016 · 2016
Cited alongside, same era.
Revisiting semi-supervised learning with graph embeddings. In International conference on machine learning . PMLR, 40–48
A review on the attention mechanism of deep learning
Zhaoyang Niu, Guoqiang Zhong, and Hui Yu. 2021 · 2021
Later among the works it cites.
Multi-Scale attributed node embedding
Benedek Rozemberczki, Carl Allen, and Rik Sarkar. 2021 · 2021
Later among the works it cites.
AFEC: Active Forgetting of Negative Transfer in Continual Learning
Liyuan Wang, Mingtian Zhang, Zhongfan Jia, Qian Li, Chenglong Bao, Kaisheng Ma, Jun Zhu, and Yi Zhong. 2021 · 2021
Later among the works it cites.
Labeling Trick: A Theory of Using Graph Neural Networks for Multi-Node Representation Learning. In Advances in Neural Information Processing Systems , M. Ranzato, A. Beygelzimer, Y. Dauphin, P. S. Liang, and J. Wortman Vaughan (Eds.), Vol. 34. Curran Associates, Inc., 9061–9073
Muhan Zhang, Pan Li, Yinglong Xia, Kai Wang, and Long Jin. 2021 · 2021
Later among the works it cites.
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Zhilin Yang, William Cohen, and Ruslan Salakhudinov. 2016 · 2016
Cited alongside, same era.
Semi-Supervised Classification with Graph Convolutional Networks
Thomas N. Kipf and Max Welling. 2017 · 2017
Cited alongside, same era.
Attention is All you Need. In Advances in Neural Information Processing Systems , Vol. 30. Curran Associates, Inc
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017 · 2017
Cited alongside, same era.
Weisfeiler-Lehman Neural Machine for Link Prediction. In Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining . ACM, Halifax NS Canada, 575–583
Muhan Zhang and Yixin Chen. 2017 · 2017
Cited alongside, same era.
Inductive Representation Learning on Large Graphs
William L. Hamilton, Rex Ying, and Jure Leskovec. 2018 · 2018
Cited alongside, same era.
Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Liò, and Yoshua Bengio. 2018 · 2018
Cited alongside, same era.
How Powerful are Graph Neural Networks?
Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka. 2018 · 2018
Cited alongside, same era.
Link Prediction Based on Graph Neural Networks. In Advances in Neural Information Processing Systems , S. Bengio, H. Wallach, H. Larochelle, K. Grauman, N. Cesa-Bianchi, and R. Garnett (Eds.), Vol. 31. Curran Associates, Inc
Muhan Zhang and Yixin Chen. 2018 · 2018
Cited alongside, same era.
Shaked Brody, Uri Alon, and Eran Yahav. 2022 · 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 Yannick Hammerla, Michael M. Bronstein, and Max Hansmire. 2022 · 2022
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Why Can GPT Learn In-Context? Language Models Implicitly Perform Gradient Descent as Meta-Optimizers
Damai Dai, Yutao Sun, Li Dong, Yaru Hao, Shuming Ma, Zhifang Sui, and Furu Wei. 2022 · 2022
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FakeEdge: Alleviate Dataset Shift in Link Prediction
Kaiwen Dong, Yijun Tian, Zhichun Guo, Yang Yang, and Nitesh Chawla. 2022 · 2022
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Understanding and Extending Subgraph GNNs by Rethinking Their Symmetries
Fabrizio Frasca, Beatrice Bevilacqua, Michael M. Bronstein, and Haggai Maron. 2022 · 2022
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Linkless Link Prediction via Relational Distillation
Zhichun Guo, William Shiao, Shichang Zhang, Yozen Liu, Nitesh Chawla, Neil Shah, and Tong Zhao. 2022 · 2022
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Kazuki Irie, Róbert Csordás, and Jürgen Schmidhuber. 2022 · 2022
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Rethinking the Role of Demonstrations: What Makes In-Context Learning Work?
Sewon Min, Xinxi Lyu, Ari Holtzman, Mikel Artetxe, Mike Lewis, Hannaneh Hajishirzi, and Luke Zettlemoyer. 2022 · 2022
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Pure Message Passing Can Estimate Common Neighbor for Link Prediction
Kaiwen Dong, Zhichun Guo, and Nitesh V. Chawla. 2023 · 2023
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PRODIGY: Enabling In-context Learning Over Graphs
Qian Huang, Hongyu Ren, Peng Chen, Gregor Kržmanc, Daniel Zeng, Percy Liang, and Jure Leskovec. 2023 · 2023
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Alexander Kirillov, Eric Mintun, Nikhila Ravi, Hanzi Mao, Chloe Rolland, Laura Gustafson, Tete Xiao, Spencer Whitehead, Alexander C. Berg, Wan-Yen Lo, Piotr Dollár, and Ross Girshick. 2023 · 2023
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Revisiting Link Prediction: A Data Perspective
Haitao Mao, Juanhui Li, Harry Shomer, Bingheng Li, Wenqi Fan, Yao Ma, Tong Zhao, Neil Shah, and Jiliang Tang. 2023 · 2023
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What In-Context Learning "Learns" In-Context: Disentangling Task Recognition and Task Learning
Jane Pan, Tianyu Gao, Howard Chen, and Danqi Chen. 2023 · 2023
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Larger language models do in-context learning differently
Jerry Wei, Jason Wei, Yi Tay, Dustin Tran, Albert Webson, Yifeng Lu, Xinyun Chen, Hanxiao Liu, Da Huang, Denny Zhou, and Tengyu Ma. 2023 · 2023
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A Data Generation Perspective to the Mechanism of In-Context Learning
Haitao Mao, Guangliang Liu, Yao Ma, Rongrong Wang, and Jiliang Tang. 2024 · 2024
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