Article Per Year (5 Year)
p-Index From 2020 - 2025
0.23
P-Index
This Author published in this journals
All Journal IAES International Journal of Artificial Intelligence (IJ-AI)
Abu Bakar, Azuraliza
Unknown Affiliation
Computer Science & IT Engineering

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
Strategies for improving the quality of community detection based on modularity optimization Setiadi, Tedy; Yaakub, Mohd Ridzwan; Abu Bakar, Azuraliza
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 2: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i2.pp1794-1804

Abstract

Community detection is a field of interest in social networks. Many new methods have emerged for community detection solution, however the modularity optimization method is the most prominent. Community detection based on modularity optimization (CDMO) has fundamental problems in the form of solution degeneration and resolution limits. From the two problems, the resolution limit is more concerned because it affects the resulting community's quality. During the last decade, many studies have attempted to address the problems, but so far they have been carried out partially, no one has thoroughly discussed efforts to improve the quality of CDMO. In this paper, we aim to investigate works in handling resolution limit and improving the quality of CDMO, along with their strengths and limitations. We derive six categories of strategies to improve the quality of CDMO, namely developing multi-resolution modularity, creating local modularity, creating modularity density, creating new metrics as an alternative to modularity, creating new quality metrics as a substitute for modularity, involving node attributes in determining community detection, and extending the single objective function into a multi-objective function. These strategies can be used as a guide in developing community detection methods. By considering network size, network type, and community distribution, we can choose the appropriate strategy in improving the quality of community detection.
Co-Authors Tedy Setiadi Yaakub, Mohd Ridzwan