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.
@inproceedings{reyhaneh2021noiseenhanced,
title = {Noise-Enhanced Unsupervised Link Prediction},
author = {Reyhaneh Abdolazimi and Reza Zafarani},
year = {2021},
keywords = {conference},
booktitle = {Proceedings of the 25th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD)},
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.},
pdf = {files/2021-PAKDD-Noise.pdf},
}