Abstract
This paper presents our vision of large-scale, dynamic social network analysis in real environments, which we expect to be enabled by the introduction of large-scale heterogeneous sensors in the ambient environment. We address challenges in realizing large-scale dynamic social network analysis in real environments, and discuss several promising applications. Moreover, we present our design and implementation of a prototype system for quasi-realtime social network construction. We finally present preliminary experimental results of dynamic social network analysis for six-person social gatherings in a real environment, and discuss the feasibility of dynamic social network analysis and its effectiveness.
Highlights
In network science, and at its intersection with the social sciences, research analyzing the topological structures of social networks has been actively performed for further understanding complex social phenomena that involve interactions among a large number of people [1,2,3]
With the aid of heterogeneous sensors in the ambient environment, we anticipate that large-scale and dynamic social network analysis in real environments will soon become possible
This paper presents our vision of large-scale, dynamic social network analysis in real environments, which we expect to be enabled by the introduction of large-scale heterogeneous sensors in the ambient environment
Summary
At its intersection with the social sciences, research analyzing the topological structures of social networks has been actively performed for further understanding complex social phenomena that involve interactions among a large number of people [1,2,3]. With the aid of heterogeneous sensors in the ambient environment, we anticipate that large-scale and dynamic social network analysis in real environments will soon become possible. Trends in social network analysis are shifting from small-scale static analysis in real environments to large-scale dynamic analysis in virtual environments. This paper presents our vision of large-scale, dynamic social network analysis in real environments, which we expect to be enabled by the introduction of large-scale heterogeneous sensors in the ambient environment. We address challenges in realizing large-scale dynamic social network analysis in real environments, and discuss several promising applications.
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