| Title: |
A comparative study on network alignment techniques |
| Authors: |
Thanh Trung, Huynh; Toan, NT; Vinh, TV; Dat, HT; Thang, DC; Nguyen, Quoc Viet Hung; Sattar, A |
| Publisher Information: |
Elsevier |
| Publication Year: |
2020 |
| Collection: |
Griffith University: Griffith Research Online |
| Subject Terms: |
Mathematical sciences; Engineering |
| Description: |
Network alignment is a method to align nodes that belong to the same entity from different networks. A well-known application of network alignment is to map user accounts from different social networks that belong to the same person. As network alignment has a wide range of applications from recommendation to link prediction, there are several proposed approaches to aligning nodes from different networks. These techniques, however, have been rarely compared and analyzed under the same setting, rendering a right choice for a particular set of networks very difficult. Addressing this problem, this paper presents a benchmark that offers a comprehensive empirical study on the performance comparison of network alignment methods. Specifically, we integrate several state-of-the-art network alignment techniques in a comparable manner, and measure distinct characteristics of these techniques with various settings. We then provide in-depth analysis of the benchmark results, obtained by using both real data and synthetic data. We believe that the findings from the benchmark will serve as a practical guideline for potential applications. ; No Full Text |
| Document Type: |
article in journal/newspaper |
| Language: |
English |
| Relation: |
Expert Systems with Applications; Thanh Trung, H; Toan, NT; Vinh, TV; Dat, HT; Thang, DC; Nguyen, QVH; Sattar, A, A comparative study on network alignment techniques, Expert Systems with Applications, 2020, 140, pp. 112883: 1-112883: 17; https://hdl.handle.net/10072/388019 |
| DOI: |
10.1016/j.eswa.2019.112883 |
| Availability: |
https://hdl.handle.net/10072/388019; https://doi.org/10.1016/j.eswa.2019.112883 |
| Rights: |
open access |
| Accession Number: |
edsbas.67B17349 |
| Database: |
BASE |