Computational analysis of large and time-dependent social networks

Aalto University, P.O. Box 11000, FI-00076 Aalto www.aalto.fi Author Lauri Kovanen Name of the doctoral dissertation Computational analysis of large and time-dependent social networks Publisher School of Science Unit Department of Biomedical Engineering and Computational Science Series Aalto University publication series DOCTORAL DISSERTATIONS 81/2013 Field of research Manuscript submitted 12 February 2013 Date of the defence 16 May 2013 Permission to publish granted (date) 27 March 2013 Language English Monograph Article dissertation (summary + original articles) Abstract Complex systems consist of a large number of elements that interact in a non-trivial way; for example the human brain, society, Internet, and biological organisms can all be modelled as complex systems. Complex systems can be naturally represented as networks, mathematical objects that consist of nodes and edges connecting these nodes, and the study of large networks based on empirical data has become known as complex networks. Since the first articles on complex networks appeared in the end of the 1990's, various technological, biological, and social networks have been analyzed. In recent years introductory text books on the subject have also been published. The study of social networks of course has a longer history. Small social networks have been studied for decades in sociology, social psychology and anthropology, and the influence that social networks have on both performance and well being of individuals has been well documented. The availability of electronic communication records—mobile phone calls, emails, online social networking sites and even multiplayer computer games—have changed the scale and detail at which social networks can be analyzed. The largest data set studied so far includes over 700 million individuals, and the mobile phone call records studied in this Thesis contain information of over 6 million people. The combination of powerful computers and large data sets have enabled the emergence of computational social science. Several aspects of large social networks are studied in this Thesis. Models of social networks are commonly used as a way to gain insight about the structure of these networks. The first article studies a number of models suggested for social networks and discusses their advantages and shortcomings. The community structure of various networks has also been a subject of great interest. It is widely accepted that nearly all networks have modular structure, evidenced by local densifications of connectivity. However, identifying communities in empirical data has turned out to be difficult both theoretically and in practice. We apply three state-of-art community detections methods to a large social network and evaluate the quality of the identified communities. One important aspect of human interactions is omitted when analyzing networks: time. Temporal networks have become a common framework for studying data sets where the relations between nodes vary with time, and this framework can be readily applied to study mobile phone calls. The last part of this Thesis introduces the concept of temporal motifs— recurring patterns of events in temporal networks—that can be used to analyze the meso-scale structure of temporal networks.Complex systems consist of a large number of elements that interact in a non-trivial way; for example the human brain, society, Internet, and biological organisms can all be modelled as complex systems. Complex systems can be naturally represented as networks, mathematical objects that consist of nodes and edges connecting these nodes, and the study of large networks based on empirical data has become known as complex networks. Since the first articles on complex networks appeared in the end of the 1990's, various technological, biological, and social networks have been analyzed. In recent years introductory text books on the subject have also been published. The study of social networks of course has a longer history. Small social networks have been studied for decades in sociology, social psychology and anthropology, and the influence that social networks have on both performance and well being of individuals has been well documented. The availability of electronic communication records—mobile phone calls, emails, online social networking sites and even multiplayer computer games—have changed the scale and detail at which social networks can be analyzed. The largest data set studied so far includes over 700 million individuals, and the mobile phone call records studied in this Thesis contain information of over 6 million people. The combination of powerful computers and large data sets have enabled the emergence of computational social science. Several aspects of large social networks are studied in this Thesis. Models of social networks are commonly used as a way to gain insight about the structure of these networks. The first article studies a number of models suggested for social networks and discusses their advantages and shortcomings. The community structure of various networks has also been a subject of great interest. It is widely accepted that nearly all networks have modular structure, evidenced by local densifications of connectivity. However, identifying communities in empirical data has turned out to be difficult both theoretically and in practice. We apply three state-of-art community detections methods to a large social network and evaluate the quality of the identified communities. One important aspect of human interactions is omitted when analyzing networks: time. Temporal networks have become a common framework for studying data sets where the relations between nodes vary with time, and this framework can be readily applied to study mobile phone calls. The last part of this Thesis introduces the concept of temporal motifs— recurring patterns of events in temporal networks—that can be used to analyze the meso-scale structure of temporal networks.