Attributed Networks Formation Models with Application in Social Network Analysis

In the paper, networks characterized by vertices with a number of attributes (attributed networks) are studied. The networks cover a wide class of real-world ones, particularly social networks, making them especially attractive for further development. We analyze how the attributed networks are formed and present several mathematical models built based on their specifics and utilizing well-known classes of graphs - full ones, Erdos-Renyi and BarabasiAlbert random graphs. The computational experiment part includes simulation of test networks for all presented formation models, evaluating their metrics, and confirming their social networks properties. Barabasi-Albert graphs' based model best demonstrated these features. The results can be further used in Community Detection, Cluster Analysis, missing network attribute's restoration and other related Network Analysis problems.