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Influence maximization serves as the main goal of a variety of social networkactivities such as viral marketing and campaign advertising. The independentcascade model for the influence spread assumes a one-time chance for eachactivated node to influence its neighbors. This reasonable assumption cannot bebypassed, since otherwise the influence probabilities of the nodes, modeled bythe edge weights, would be altered. On the other hand, the manually activatedseed set nodes can be reselected without violating the model parameters orassumptions. The reselection of a seed set node, simply means paying extrabudget to a previously paid node in order for it to retry its influentialskills on its uninfluenced neighbors. This view divides the influencemaximization process into two cases: the simple case where the reselection ofthe nodes is not considered and the reselection case. In this study we willanalyze the behavior of real world networks on the difference between these twoinfluence maximization cases. First we will show that the difference betweenthe simple and the reselection cases constitutes a wide spectrum of networksranging from the reselection-independent ones, where the reselection case hasno noticeable advantage to the simple case, to the reselection-friendly ones,where the influence spread in the reselection case is twice the one in thesimple case. Then we will correlate this dynamic to other influencemaximization dynamics of the network. Finally, a significant entanglementbetween this dynamic and the network structure is shown and verified by theexperiments. In other words, a series of conditions on the network structure isspecified whose fulfilment is a sign for a reselection-friendly network. As aresult of this entanglement, reselection-friendly networks can be spottedwithout performing the time consuming influence maximization algorithms.

A Behavioral Analysis on the Reselection of Seed Nodes in Independent Cascade Based Influence Maximization