Abstract:
Research on Influence Maximization has gained a
lot of attention in the recent past. Part of the reason for this
is that influence maximization has applications in commercially
attractive areas such as word of mouth marketing. A majority
of works in influence maximization have relied on information
diffusion models that largely ignore the structural properties
of the social network. The problem with this is that important
attributes of user relationships which are necessary for approximating influence maximization are ignored. Parameters such as
Homophily and Topological Overlap are crucial determinants
of the level of influence that a user enjoys in the network.
This work approximates global influence power of a user by
first, considering user interactions, homophily and topological
overlap as determinants of node to node relationship strength on
a dynamic social graph. Then secondly, computes a global score
of influence for each user. We then apply a novel algorithm that
approximates influence spread for each influential user. The seed
set is built by identifying the most influential users at specific
time instances as the social graph evolves.
