  Social networks offer users new means of accessing information, essentially
relying on "social filtering", i.e. propagation and filtering of information by
social contacts. The sheer amount of data flowing in these networks, combined
with the limited budget of attention of each user, makes it difficult to ensure
that social filtering brings relevant content to the interested users. Our
motivation in this paper is to measure to what extent self-organization of the
social network results in efficient social filtering. To this end we introduce
flow games, a simple abstraction that models network formation under selfish
user dynamics, featuring user-specific interests and budget of attention. In
the context of homogeneous user interests, we show that selfish dynamics
converge to a stable network structure (namely a pure Nash equilibrium) with
close-to-optimal information dissemination. We show in contrast, for the more
realistic case of heterogeneous interests, that convergence, if it occurs, may
lead to information dissemination that can be arbitrarily inefficient, as
captured by an unbounded "price of anarchy". Nevertheless the situation differs
when users' interests exhibit a particular structure, captured by a metric
space with low doubling dimension. In that case, natural autonomous dynamics
converge to a stable configuration. Moreover, users obtain all the information
of interest to them in the corresponding dissemination, provided their budget
of attention is logarithmic in the size of their interest set.
