Latencybounded Target Set Selection in Signed Networks It is welldocumented that social networks play a considerable role in information spreading. The dynamic processes governing the diffusion of information have been studied in many fields, including epidemiology, sociology, economics, and computer science. A widely studied problem in the area of viral marketing is the target set selection: in order to market a new product, hoping it will be adopted by a large fraction of individuals in the network, which set of individuals should we xe2x80x9ctargetxe2x80x9d (for instance, by offering them free samples of the product)? In this paper, we introduce a diffusion model in which some of the neighbors of a node have a negative influence on that node, namely, they induce the node to reject the feature that is supposed to be spread. We study the target set selection problem within this model, first proving a strong inapproximability result holding also when the diffusion process is required to reach all the nodes in a couple of rounds. Then, we consider a set of restrictions under which the problem is approximable to some extent.

Targeted Influence Maximization under a MultifactorBased Information Propagation Model Abstract   Information propagation modeling and influence maximization are two important research problems in viral marketing. When marketing information is given, how can the seed nodes be efficiently identified to maximize the spread of the information through the network? To answer this question, we consider multiple factors in information propagation, such as information content, social influence and user authority, and propose a multifactorbased information propagation model (MFIP). Then, we utilize the firstorder influence of the nodes to approximate their influence and propose an efficient heuristic algorithm named weighted degree decrease (WDD) to select the seed nodes under the MFIP model. Experimental evaluations with four realworld social network datasets demonstrate the effectiveness and efficiency of our algorithm.