In this paper by Kempe-Kleinberg-Tardos, the Authors propose a greedy algorithms based on submodular functions to determine the $k$ most influential nodes in a graph, with applications to social networks.

Basically the algorithm goes as follows:

1. $S = {\rm empty~set}$
2. pick the node with highest degree, call it $v_1$; $S = S\cup v_1$
3. remove $v_1$ and all edges connecting $v_1$ to the rest of the network
4. repeat until $S$ has $k$ vertices

I have two questions about influential nodes in social networks.
a) Is there any algorithm to find the solution, or an approximation of it in a decentralized fashion?
b) Did anyone apply other algorithms, such as Page-Rank and similar, to solve the same problem?

In this paper by Kempe-Kleinberg-Tardos, the Authors propose a greedy algorithms based on submodular functions to determine the $k$ most influential nodes in a graph, with applications to social networks.

Basically the algorithm goes as follows:

1. $S = {\rm empty~set}$
2. pick the node with highest individual influence, call it $v_1$; $S = S\cup v_1$
3. remove $v_1$ and all edges connecting $v_1$ to the rest of the network
4. repeat until $S$ has $k$ vertices

I have two questions about influential nodes in social networks.
a) Is there any algorithm to find the solution, or an approximation of it in a decentralized fashion?
b) Did anyone apply other algorithms, such as Page-Rank and similar, to solve the same problem?