Influence maximization with deactivation in social networks

Abstract

In this paper we consider an extension of the well-known Influence Maximization Problem in a social network which deals with finding a set of k nodes to initiate a diffusion process so that the total number of influenced nodes at the end of the process is maximized. The extension focuses on a competitive variant where two decision makers are involved. The first one, the leader, tries to maximize the total influence spread by selecting the most influential nodes and the second one, the follower, tries to minimize it by deactivating some of these nodes.The formulated bilevel model is solved by complete enumeration for small-sized instances and by a matheuristic for large-sized instances. In both cases, the lower level problem, which is a stochastic optimization problem, is approximated via the Sample Average Approximation method.

Analysing the Effect of Self-Efficacy and Influencers on Design Team Performance

“…Some researchers are studying the role of trust on influence in marketing (Liu et al, 2015) and on decision-making (Capuano, 2019). Others are exploring the ways to maximise SMI influence (Taninmis et al, 2019, Yerasani et al, 2019Hosseinpour et al, 2019). Efforts have been made by some researchers to study the personality traits of these SMI (Oyibo andVassileva, 2019, Freberg et al, 2011;Erz et al, 2018) .…”

A Study of Delayed Competitive Influence Propagation Based on Shortest Path Calculation

“…Clark and Poovendran [5] argued that the objective function based on the Markov diffusion model has a submodularity property. Hemmati et al [6] and Taninmis et al [7] studied the timing competition problem based on a two-layer model and argued that the selection strategy of backward nodes can be improved by using a heuristic algorithm. These earlier economic models have inspired and informed the study of competition in information propagation and provided a reference for research around applying competition strategies for backward nodes.…”

Analysis of the Shortest Path Method Application in Social Networks

“…There are also health-related areas of application, such as for predicting adolescent social networks to stop smoking in secondary schools with the authors in [16] researching the potential importance of adolescent friendships` selection, affecting the health domain. SPP is also applied in other problem-solving efforts, presented in [34], where the authors deal with a competitive Influence Maximization Problem, where two players make decisions sequentially; the first player (leader) wants to maximize the spread by activating an influential seed set, and the second player (follower) tries to minimize it by deactivating some of the activated nodes. Similar research, conducted by [27], approaches analytically to the problem of influence maximization in a social network, where two players compete by utilizing dynamic targeting strategies.…”