Abstract
The problem of target set selection for large scale social networks is addressed in the paper. We describe a novel deprecation based greedy strategy to be applied over a pre-ordered (as obtained with any heuristic influence function) set of nodes. The proposed algorithm runs in iteration and has two stages, (i) Estimation: where the performance of each node is evaluated and (ii) Marking: where the nodes to be deprecated in later iterations are marked. We have theoretically proved that for any monotonic and sub-modular influence function, the algorithm correctly identifies the nodes to be deprecated. For any finite set of input nodes it is shown that the algorithm can meet the ending criteria. The worst case performance of the algorithm, both in terms of time and performance, is also analyzed. Experimental results on seven un-weighted as well as weighted social networks show that the proposed strategy improves the ranking of the input seeds in terms of the total number of nodes influenced.
