Location-Based Seeds Selection for Influence Blocking Maximization in Social Networks

Abstract

Influence blocking maximization (IBM) is a key problem for viral marketing in competitive social networks. Although the IBM problem has been extensively studied, existing works neglect the fact that the location information can play an important role in influence propagation. In this paper, we study the location-based seeds selection for IBM problem, which aims to find a positive seed set in a given query region to block the negative influence propagation in a given block region as much as possible. In order to overcome the low efficiency of the simulation-based greedy algorithm, we propose a heuristic algorithm IS-LSS and its improved version IS-LSS+, both of which are based on the maximum influence arborescence structure and Quadtree index, while IS-LSS+ further improves the efficiency of IS-LSS by using an upper bound method and Quadtree cell lists. The experimental results on real-world datasets demonstrate that our proposed algorithms are able to achieve matching blocking effect to the greedy algorithm as the increase in the number of positive seeds and often better than other heuristic algorithms, whereas they are four orders of magnitude faster than the greedy algorithm.

Minimizing the spread of misinformation in online social networks: A survey

“…The location of users is taken into account in [83,84]. In [83], the goal is to minimize the number of users who are located in a region R and are activated (influenced) by a misinformation campaign.…”

“…Most influential nodes are greedily identified to contain the spread of misinformation in R. In order to increase the efficiency of the proposed method, pruning nodes with small influence is suggested. In [84], this problem is more constrained as nodes for the truth campaign are selected from the nodes of a specific region; the solution comes through the extension of methods in [83]. User mobility is taken into account in [85] and the SIR diffusion model is extended to simulate rumour propagation in vehicular social networks.…”

Countering Misinformation on Social Networks Using Graph Alterations

“…model the competition between misinformation and credible information on a network using a novel dynamic linear threshold model and investigate the problem of finding optimal set of users on a network to initiate the propagation of credible content [13,14]. More recently there has been work on refining this approach by considering location [27] or community [16] structures of the underlying social network. Unlike these works we do not focus on minimizing the influence of misinformation by maximizing the influence of a competing diffusion model.…”

Multi-Topic Misinformation Blocking With Budget Constraint on Online Social Networks

“…In [27] the authors proposed a scalable algorithm which guarantees approximation ratio of (1 − 1/e − ) for epidemic blocking problem by edges and nodes blocking. In [28] the authors studied influence blocking which considers location of competitors. The authors in [29], [30] proposed a method for misinformation prevention by eliminating nodes in multiple contexts.…”