  The problem of increasing the centrality of a network node arises in many
practical applications. In this paper, we study the optimization problem of
maximizing the information centrality $I_v$ of a given node $v$ in a network
with $n$ nodes and $m$ edges, by creating $k$ new edges incident to $v$. Since
$I_v$ is the reciprocal of the sum of resistance distance $\mathcal{R}_v$
between $v$ and all nodes, we alternatively consider the problem of minimizing
$\mathcal{R}_v$ by adding $k$ new edges linked to $v$. We show that the
objective function is monotone and supermodular. We provide a simple greedy
algorithm with an approximation factor $\left(1-\frac{1}{e}\right)$ and
$O(n^3)$ running time. To speed up the computation, we also present an
algorithm to compute $\left(1-\frac{1}{e}-\epsilon\right)$-approximate
resistance distance $\mathcal{R}_v$ after iteratively adding $k$ edges, the
running time of which is $\widetilde{O} (mk\epsilon^{-2})$ for any
$\epsilon>0$, where the $\widetilde{O} (\cdot)$ notation suppresses the ${\rm
poly} (\log n)$ factors. We experimentally demonstrate the effectiveness and
efficiency of our proposed algorithms.
