Name
Title | ||
|---|---|---|
Communities Found by Users--not Algorithms: Comparing Human and Algorithmically Generated Communities |
Abstract | ||
|---|---|---|
Many algorithms have been created to automatically detect community structures in social networks. These algorithms have been studied from the perspective of optimisation extensively. However, which community finding algorithm most closely matches the human notion of communities? In this paper, we conduct a user study to address this question. In our experiment, users collected their own Facebook network and manually annotated it, indicating their social communities. Given this annotation, we run state-of-the-art community finding algorithms on the network and use Normalised Mutual Information (NMI) to compare annotated communities with automatically detected ones. Our results show that the Infomap algorithm has the greatest similarity to user defined communities, with Girvan-Newman and Louvain algorithms also performing well.
|
Year | DOI | Venue |
|---|---|---|
2016 | 10.1145/2858036.2858071 | human factors in computing systems |
Keywords | Field | DocType |
Social Media/Online Communities, Visualisation, Quantitative Methods, Lab Study, Empirical study that tells us about people | Community finding,World Wide Web,Annotation,Social network,Computer science,Visualization,Algorithm,Mutual information,Multimedia | Conference |
ISBN | Citations | PageRank |
978-1-4503-3362-7 | 2 | 0.36 |
References | Authors | |
9 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Alexandra Lee | 1 | 6 | 2.07 |
| Daniel Archambault | 2 | 705 | 39.10 |