  This doctoral work focuses on three main problems related to social networks:
(1) Orchestrating Network Formation: We consider the problem of orchestrating
formation of a social network having a certain given topology that may be
desirable for the intended usecases. Assuming the social network nodes to be
strategic in forming relationships, we derive conditions under which a given
topology can be uniquely obtained. We also study the efficiency and robustness
of the derived conditions. (2) Multi-phase Influence Maximization: We propose
that information diffusion be carried out in multiple phases rather than in a
single instalment. With the objective of achieving better diffusion, we
discover optimal ways of splitting the available budget among the phases,
determining the time delay between consecutive phases, and also finding the
individuals to be targeted for initiating the diffusion process. (3) Scalable
Preference Aggregation: It is extremely useful to determine a small number of
representatives of a social network such that the individual preferences of
these nodes, when aggregated, reflect the aggregate preference of the entire
network. Using real-world data collected from Facebook with human subjects, we
discover a model that faithfully captures the spread of preferences in a social
network. We hence propose fast and reliable ways of computing a truly
representative aggregate preference of the entire network. In particular, we
develop models and methods for solving the above problems, which primarily deal
with formation and analysis of social networks.
