  \emph{Implicit Social Network} is a connected social structure among a group
of persons, where two of them are linked if they have some common interest. One
real\mbox{-}life example of such networks is the implicit social network among
the customers of an online commercial house, where there exists an edge between
two customers if they like similar items. Such networks are often useful for
different commercial applications such as \textit{target advertisement},
\textit{viral marketing}, etc. In this article, we study two fundamental
problems in this direction. The first one is that, given the user\mbox{-}item
rating data of an E\mbox{-}Commerce house, how we can design implicit social
networks among its users and the second one is at the time of designing itself
can we obtain the connectivity information among the users. Formally, we call
the first problem as the \textsc{Implicit User Network Design} Problem and the
second one as \textsc{Implicit User Network Design with Connectivity Checking}
Problem. For the first problem, we propose three different algorithms, namely
\emph{`Exhaustive Search Approach'}, \emph{`Clique Addition Approach'}, and
\textit{`Matrix Multiplication\mbox{-}Based Approach'}. For the second problem,
we propose two different approaches. The first one is the sequential approach:
designing and then connectivity checking, and the other one is a concurrent
approach, which is basically an incremental algorithm that performs designing
and connectivity checking simultaneously. Proposed methodologies have
experimented with three publicly available rating network datasets such as
\emph{Flixter}, \textit{Movielens}, and \textit{Epinions}.
