| A. Kanavos, Y. Voutos, F. Grivokostopoulou, Ph. Mylonas |
| Evaluating Methods for Efficient Community Detection in Social Networks |
| Information, MDPI, April 2022 |
|
ABSTRACT
|
| Exploring a community is an important aspect of social network analysis because it can be seen as a crucial way to decompose specific graphs into smaller graphs based on interactions between users. The process of discovering common features between groups of users, entitled “community detection", is a fundamental feature for social network analysis, where the vertices represent the users and the edges their relationships. Our study focuses on identifying such phenomena on the Twitter graph of posts and on determining communities, which contain users with similar features. This paper presents the evaluation of six established community discovery algorithms in terms of four widely used graphs and a collection of data fetched from Twitter about man-made and physical data. Finally, the size of each community, expressed as a percentage of the total number of vertices, is identified for the six particular algorithms and corresponding results are extracted. Therefore, our findings suggest that the community detection algorithms can assist in identifying “dense" group of users.
|
| 15 April , 2022 |
| A. Kanavos, Y. Voutos, F. Grivokostopoulou, Ph. Mylonas, "Evaluating Methods for Efficient Community Detection in Social Networks", Information, MDPI, April 2022 |
[  PDF] [
BibTex] [
Print] [
 Back] |
