  Given a task $\mathcal{T}$, a set of experts $V$ with multiple skills and a
social network $G(V, W)$ reflecting the compatibility among the experts, team
formation is the problem of identifying a team $C \subseteq V$ that is both
competent in performing the task $\mathcal{T}$ and compatible in working
together. Existing methods for this problem make too restrictive assumptions
and thus cannot model practical scenarios. The goal of this paper is to
consider the team formation problem in a realistic setting and present a novel
formulation based on densest subgraphs. Our formulation allows modeling of many
natural requirements such as (i) inclusion of a designated team leader and/or a
group of given experts, (ii) restriction of the size or more generally cost of
the team (iii) enforcing locality of the team, e.g., in a geographical sense or
social sense, etc.
  The proposed formulation leads to a generalized version of the classical
densest subgraph problem with cardinality constraints (DSP), which is an NP
hard problem and has many applications in social network analysis. In this
paper, we present a new method for (approximately) solving the generalized DSP
(GDSP). Our method, FORTE, is based on solving an equivalent continuous
relaxation of GDSP. The solution found by our method has a quality guarantee
and always satisfies the constraints of GDSP. Experiments show that the
proposed formulation (GDSP) is useful in modeling a broader range of team
formation problems and that our method produces more coherent and compact teams
of high quality. We also show, with the help of an LP relaxation of GDSP, that
our method gives close to optimal solutions to GDSP.
