Name
Abstract | ||
|---|---|---|
In this paper we propose a generalized Markov Graph model for social networks and evaluate its application in social network synthesis, and in social network classification. The model reveals that the degree distribution, the clustering coefficient distribution as well as a newly discovered feature, a crowding coefficient distribution, are fundamental to characterizing a social network. The application of this model to social network synthesis leads to a capacity to generate networks dominated by the degree distribution and the clustering coefficient distribution. Another application is a new social network classification method based on comparing the statistics of their degree distributions and clustering coefficient distributions as well as their crowding coefficient distributions. In contrast to the widely held belief that a social network graph is solely defined by its degree distribution, the novelty of this paper consists in establishing the strong dependence of social networks on the degree distribution, the clustering coefficient distribution and the crowding coefficient distribution, and in demonstrating that they form minimal information to classify social networks as well as to design a new social network synthesis tool. We provide numerous experiments with published data and demonstrate very good performance on both counts. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/JSTSP.2013.2246767 | J. Sel. Topics Signal Processing |
Keywords | Field | DocType |
statistical distributions,social sciences,pattern clustering,network generation,clustering coefficient distribution,pattern recognition,complex networks,social network characterization,pattern classification,degree distribution statistics,markov graph model,social network classification,social network graph,generalized markov graph model,social network synthesis,graph theory,classification,crowding coefficient distribution,social network analysis,markov processes,network theory (graphs),physics,mathematical model,signal processing | Network science,Social network,Computer science,Theoretical computer science,Hierarchical network model,Artificial intelligence,Complex network,Degree distribution,Clustering coefficient,Dynamic network analysis,Mathematical optimization,Pattern recognition,Scale-free network | Journal |
Volume | Issue | ISSN |
7 | 2 | 1932-4553 |
Citations | PageRank | References |
15 | 0.87 | 2 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tian Wang | 1 | 21 | 1.79 |
| Hamid Krim | 2 | 520 | 59.69 |
| yannis viniotis | 3 | 436 | 52.11 |