Recently, there has been tremendous interest in the phenomenon of social networks
analysis. A social network is a social structure composed of a set of social actors
and a complex set of the interactions between them. Co-authorship network is a well
known example of social networks. Most of the previous studies consider the co-
authorship relation between two or more authors as a collaboration. Co-authorship
network have been studied extensively from various perspectives such as degree dis-
tribution analysis, social community extraction and social entity ranking.
An interesting problem using co-authorship networks is formation of productive team
for a new research lab. The static version of the problem is relatively well studied
which involves hub identification and then forming a team using various combinato-
rial algorithms. A more interesting variant of this problem would be to take into ac-
count the time dimension and constraint of fixed budget (in order to hire researchers
for the team) and team size in such a way that accumulative productivity of the team
in future is maximized over research community. Productivity in case of research
community can be quantified in terms of various quantities e.g. research volume and
collaboration diversity within the research community.
Results of experiments on large co-authorship network with 2 million collaborations
with 0.6 million collaborators suggest that a good extent of information about future
productivity can be extracted from the present network topology.