There has always been an implicit link between social network analysis and human geography. For example, proximity is a strong basis for individuals forming relations, with most relations weakening with distance. Social network analysis examines the structure of the relations connecting nodes (e.g., people, organizations, topics, events). Many of the earliest studies looked at networks of people connected by relationships such as kinship, mentoring, and works-with. These networks are represented as graphs (e.g., Figure), and matrix algebra or nonparametric network statistics are often used to assess these networks; to identify key nodes, critical dyads, and groups; and to compare and contrast networks (Wasserman and Faust, 1994). Social network analysis is a key methodology in the human geography toolkit.
Evolution. Social network analysis emerged prior to World War II, with early advances in frelds such as anthropology, sociology, and communications (Freeman, 2006). The past 10 years have seen a movement to broaden the field of social networks. Changes rnclude the transition from graph-theory-based metrics to a combination of graph-based and statistical measures, the expansion from small networks to very large-scale networks, the increased attention to communication and social media data, and the shift to geotemporal networks. This broader field is often referred to as dynamic network analysis and it is characterized as the study of the structure and evolution of complex sociotechnical systems through the assessment of weighted multimode, multilink, multilevel dynamic networks that are geo-embedded The field is supported by the quarterly journal Social Networks, the online journal of Social Structure, and an increasing number of specialty journals such as Social Network Analysis and Mining
Knowledge and Skills. The study of social networks is integral to fields such as statistics, sociology, organizational science, communication, computer science, and forensic science. However, the ubiquity of networks, the value of graphs as a representation, and the strength of structural thinking has increased the interest in networks in almost every scientific discipline. For example, network analysis has been used in sociology to study social and communications networks (Wasserman and Faust, 1994), in biology to study animal behavior (e.g., Krause et al., 2007), and in geography, civil engineering, ecology, and other disciplines to extend graphs to real or abstract space (Haggett and Chorley, 1969; Urban and Keitt, 2001; Adams et al., 2012). This increased interest has led to a proliferation of theories about how these networks form, evolve, and affect behavior. It has also led to new methods, such as dynamic networks techniques for sets of networks through time, and meta-network metrics for multimode, multilink data. Statistical approaches for assessing dynamics, information loss, and error provide the foundation for social network analysis. Social science approaches are used to study the dynamics within social networks (e.g., recrprocity, social influence, power) and the social, institutional, and historical contexts in which network ties are formed and broken.
Education and Professional Preparation Programs. Classes in social networks are taught in a number of U.S. universities, usually at the doctorate level. However, undergraduate textbooks and courses are starting to appear. Universities with multiple courses in this area include Carnegie Mellon University, University of Kentucky, Northeastern University, Northwestern University, Harvard, Stanford, Indiana University, and the University of California, Irvine. Courses are taught primarily in business and sociology departments, but also in anthropology, communication, management, organizational behavior, organizational theory, strategy, public policy, statistics, information science, and computer science departments. Network analysis in the geometric sense is taught in geography, mathematics, transportation science, computer engineering, and operations research programs.
Continuing education programs provide a primary venue for training in this area. For example, didactic seminars are conducted at the main social networks conference (the International Network for Social Network Analysis) for 2 days prior to the conference. Half-day and full-day training programs are often offered at management science, organization theory, sociology, and anthropology conferences. In addition, there are numerous multiday or week-long training programs, including the CASOS Summer Institute, the Lipari summer school, and the East Carolina University program for marine biologists.
• by facilitating search;
• by enhancing pattern recognition, often by restructuring relations within data;
• by supporting perceptual inference of structures and patterns that are otherwise invisible;
• by improving the ability to monitor large numbers of sensors and events; and
• by providing methods that support exploration and discovery.
Methods in visual analytics are based on principles drawn from cognitive engineering, design, and perceptual psychology (Scholtz et al., 2009). These methods provide a means to build systems for threat analysis, prevention, and response. Visual analytics therefore