Experimental Analysis of Algorithms for Community Detection
Clicks: 1
ID: 312818
2024
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This
article has not been analysed, so there is no overall score —
reader engagement is measured and shown alongside.
Reader Engagement
0.0
/100
1 views
0 readers
AI Quality Assessment
Not analyzed
Readership in this journal
Ranked #641 of 705 articles by views in Journal of Computing & Biomedical Informatics
Most read
Least read
Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 705 in total.
Mint this article as an NFT
Not yet mintedCreate a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.
5
SUSD
one-off · no wallet required
Abstract
This is an exciting new approach because by understanding the anatomy of networks, you can get a valuable framework to define related phenomena, from social and technological systems to all sorts of other complex systems found in the real world. Community structure is an essential attribute of complex networks, which has been an active field of research for decades. Understanding the structure of networks is a crucial problem, and community detection algorithms are the most utilized strategies for that. Detecting communities is fundamental for understanding their structure, function, evolution, and dynamics. As a result, the concept of community structure has attracted significant interest in recent years. When thinking about product recommendations, it is crucial to pay attention to finding sub-networks within the co-purchasing network. Keeping this in mind, we made the decision to start research that would involve analyzing four methods for community discovery. We wanted to select some real-world data for our experiment, so we used the Amazon co-purchasing network datasets to test the algorithms we had selected. In addition, we plan to investigate future work that integrates machine learning techniques with algorithms for detecting communities. In this situation, factors such as run time, efficiency, and modularity scores become significant. As a result, our project will concentrate on assessing the run time and modularity ratings of each algorithm in particular.
| Reference Key |
imported_1777057071_69ebbd2f9a601
Use this key to autocite in the manuscript while using
SciMatic Manuscript Manager or Thesis Manager
|
|---|---|
| Authors | Urooba Khalid, M. Imran Khan Khalil, Asif Nawaz, Izaz Ahmad Khan, Mian M. Aimal, Sheeraz Ahmed |
| Journal | Journal of Computing & Biomedical Informatics |
| Year | 2024 |
| DOI |
DOI not found
|
| URL | |
| Keywords | Keywords not found |
Citations
No citations found. To add a citation, contact the admin at info@scimatic.org
Comments
No comments yet. Be the first to comment on this article.