Paper Info
Title
On the structural properties of social networks and their measurement-calibrated synthetic counterparts.
Abstract
Data-driven analysis of large social networks has attracted a great deal of research interest. In this paper, we investigate 120 real social networks and their measurement-calibrated synthetic counterparts generated by four well-known network models. We investigate the structural properties of the networks revealing the correlation profiles of graph metrics across various social domains (friendship networks, communication networks, and collaboration networks). We find that the correlation patterns differ across domains. We identify a nonredundant set of metrics to describe social networks. We study which topological characteristics of real networks the models can or cannot capture. We find that the goodness-of-fit of the network models depends on the domains. Furthermore, while 2K and stochastic block models lack the capability of generating graphs with large diameter and high clustering coefficient at the same time, they can still be used to mimic social networks relatively efficiently.
Year
DOI
Venue
2019
10.1145/3341161.3343686
ASONAM '19: International Conference on Advances in Social Networks Analysis and Mining Vancouver British Columbia Canada August, 2019
Keywords
DocType
ISBN
social network analysis (SNA), Facebook, Twitter, collaboration network, network models, model calibration
Conference
978-1-4503-6868-1
Citations 
PageRank 
References 
0
0.34
0
Authors
2
Name
Order
Citations
PageRank
Marcell Nagy100.68
Roland Molontay213.08