Noise-Enhanced Unsupervised Link Prediction

Reyhaneh Abdolazimi, Reza Zafarani

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish (US)
Title of host publicationAdvances in Knowledge Discovery and Data Mining - 25th Pacific-Asia Conference, PAKDD 2021, Proceedings
EditorsKamal Karlapalem, Hong Cheng, Naren Ramakrishnan, R. K. Agrawal, P. Krishna Reddy, Jaideep Srivastava, Tanmoy Chakraborty
PublisherSpringer Science and Business Media Deutschland GmbH
Pages472-487
Number of pages16
ISBN (Print)9783030757618
DOIs
StatePublished - 2021
Event25th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2021 - Virtual, Online
Duration: May 11 2021May 14 2021

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12712 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference25th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2021
CityVirtual, Online
Period5/11/215/14/21

Keywords

  • Graph algorithms
  • Link prediction
  • Noise-enhanced methods

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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