Name
Title | ||
|---|---|---|
Are all Social Networks Structurally Similar? A Comparative Study using Network Statistics and Metrics. |
Abstract | ||
|---|---|---|
The modern age has seen an exponential growth of social network data available on the web. Analysis of these networks reveal important structural information about these networks in particular and about our societies in general. More often than not, analysis of these networks is concerned in identifying similarities among social networks and how they are different from other networks such as protein interaction networks, computer networks and food web. In this paper, our objective is to perform a critical analysis of different social networks using structural metrics in an effort to highlight their similarities and differences. We use five different social network datasets which are contextually and semantically different from each other. We then analyze these networks using a number of different network statistics and metrics. Our results show that although these social networks have been constructed from different contexts, they are structurally similar. We also review the snowball sampling method and show its vulnerability against different network metrics. |
Year | Venue | Field |
|---|---|---|
2013 | Advances in Social Networks Analysis and Mining | Network science,Data science,Data mining,Social network,Computer science,Geometric networks,Hierarchical network model,Artificial intelligence,Complex network,Metrics,Dynamic network analysis,Statistics,Machine learning,Snowball sampling |
DocType | Volume | Citations |
Journal | abs/1311.2887 | 0 |
PageRank | References | Authors |
0.34 | 16 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Aneeq Hashmi | 1 | 9 | 0.97 |
| Faraz Zaidi | 2 | 122 | 11.21 |
| Arnaud Sallaberry | 3 | 147 | 14.86 |
| Tariq Mehmood | 4 | 10 | 1.67 |