  Understanding the forces governing human behavior and social dynamics is a
challenging problem. Individuals' decisions and actions are affected by
interlaced factors, such as physical location, homophily, and social ties. In
this paper, we propose to examine the role that distinct communities, linked to
these factors, play as sources of social influence. The ego network is
typically used in the social influence analysis. Our hypothesis is that
individuals are embedded in communities not only related to their direct social
relationships, but that involve different and complex forces. We analyze
physical, homophily, and social communities to evaluate their relation with
subjects' behavior. We prove that social influence is correlated with these
communities, and each one of them is (differently) significant for individuals.
We define community-based features, which reflect the subject involvement in
these groups, and we use them with a supervised learning algorithm to predict
subject participation in social events. Results indicate that both communities
and ego network are relevant sources of social influence, confirming that the
ego network alone is not sufficient to explain this phenomenon. Moreover, we
classify users according to the degree of social influence they experienced
with respect to their groups, recognizing classes of behavioral phenotypes. To
our knowledge, this is the first work that proves the existence of phenotypes
related to the social influence phenomenon.
