@inproceedings{7c937164fb7945ecbf9d5c3a5c8bbe41,
title = "Noise-Enhanced Unsupervised Link Prediction",
abstract = "Link prediction has attracted attention from multiple research areas. Although several – mostly unsupervised – link prediction methods have been proposed, improving them is still under study. In several fields of science, noise is used as an advantage to improve information processing, inspiring us to also investigate noise enhancement in link prediction. In this research, we study link prediction from a data preprocessing point of view by introducing a noise-enhanced link prediction framework that improves the links predicted by current link prediction heuristics. The framework proposes three noise methods to help predict better links. Theoretical explanation and extensive experiments on synthetic and real-world datasets show that our framework helps improve current link prediction methods.",
keywords = "Graph algorithms, Link prediction, Noise-enhanced methods",
author = "Reyhaneh Abdolazimi and Reza Zafarani",
note = "Publisher Copyright: {\textcopyright} 2021, Springer Nature Switzerland AG.; 25th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2021 ; Conference date: 11-05-2021 Through 14-05-2021",
year = "2021",
doi = "10.1007/978-3-030-75762-5_38",
language = "English (US)",
isbn = "9783030757618",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "472--487",
editor = "Kamal Karlapalem and Hong Cheng and Naren Ramakrishnan and Agrawal, {R. K.} and Reddy, {P. Krishna} and Jaideep Srivastava and Tanmoy Chakraborty",
booktitle = "Advances in Knowledge Discovery and Data Mining - 25th Pacific-Asia Conference, PAKDD 2021, Proceedings",
address = "Germany",
}