Christopher Homan studies computational social network analysis (CSNA). His work combines social media, network science, sociology, and advanced computational methods to study social networks and the roles they play in organizational behavior, motivated by the belief that a better understanding of those dynamics will lead to better health, productivity, and general social welfare. The emergence of social media has only increased the potential power of social dynamics to influence communities, negatively as well as positively.
Dr. Homan's research is fundamentally interdisciplinary and involves close collaboration with social scientists. His methodology is fairly broad, encompassing experimental, observational, and theoretical designs and a variety of techniques from network science, big data, and peer-driven sampling.
He has designed peer-driven algorithms for sampling online social networks. These methods use the power of social media to reach communities, such as gay men, IV drug users, or undocumented workers, that are hard to reach through traditional sampling methods. These methods respect the privacy of the respondents, a important feature when studying such groups.
Most social network datasets are samples of larger, underlying populations. A fundamental problem when studying social structure is that sampling processes may not be generally regarded as independent and identically distributed (iid). In fact, the dependencies between individuals in such populations are precisely what concern social network analysis. Dr. Homan has designed systems that control for such dependencies through the imposition of artificial networks and through non-parametric statistical methods.