scholarly journals Latency-Bounded Target Set Selection in Signed Networks

Algorithms ◽  
2020 ◽  
Vol 13 (2) ◽  
pp. 32 ◽  
Author(s):  
Miriam Di Ianni ◽  
Giovanna Varricchio
Keyword(s):  
Social Networks ◽  
Diffusion Process ◽  
Large Fraction ◽  
New Product ◽  
Negative Influence ◽  
Viral Marketing ◽  
Dynamic Processes ◽  
Diffusion Of Information ◽  
Target Set ◽  
Target Set Selection

It is well-documented 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 “target” (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.

Target Set Selection in Social Networks with Influence and Activation Thresholds

2021 ◽  
pp. 371-380
Author(s):  
Zhecheng Qiang ◽  
Eduardo L. Pasiliao ◽  
Qipeng P. Zheng
Keyword(s):  
Social Networks ◽  
Target Set ◽  
Target Set Selection

Competitive diffusion in online social networks with heterogeneous users

2014 ◽  
Vol 28 (22) ◽  
pp. 1450147 ◽  
Author(s):  
Pei Li ◽  
Su He ◽  
Hui Wang ◽  
Xin Zhang
Keyword(s):  
Social Networks ◽  
Online Social Networks ◽  
Negative Influence ◽  
Viral Marketing ◽  
Diffusion Dynamics ◽  
Approach Infinity ◽  
Simulation Results ◽  
Negative Messages ◽  
Competitive Diffusion

Online social networks have attracted increasing attention since they provide various approaches for hundreds of millions of people to stay connected with their friends. However, most research on diffusion dynamics in epidemiology cannot be applied directly to characterize online social networks, where users are heterogeneous and may act differently according to their standpoints. In this paper, we propose models to characterize the competitive diffusion in online social networks with heterogeneous users. We classify messages into two types (i.e., positive and negative) and users into three types (i.e., positive, negative and neutral). We estimate the positive (negative) influence for a user generating a given type message, which is the number of times that positive (negative) messages are processed (i.e., read) incurred by this action. We then consider the diffusion threshold, above which the corresponding influence will approach infinity, and the effect threshold, above which the unexpected influence of generating a message will exceed the expected one. We verify all these results by simulations, which show the analysis results are perfectly consistent with the simulation results. These results are of importance in understanding the diffusion dynamics in online social networks, and also critical for advertisers in viral marketing where there are fans, haters and neutrals.

scholarly journals Analysis of Product Sampling for New Product Diffusion Incorporating Multiple-Unit Ownership

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Zhineng Hu ◽  
Yurong Pei ◽  
Ruikun Xie
Keyword(s):  
Sensitivity Analysis ◽  
Diffusion Process ◽  
Net Present Value ◽  
New Product ◽  
Negative Influence ◽  
Sampling Effort ◽  
Product Categories ◽  
Optimal Dynamic ◽  
Multiple Unit ◽  
Purchase Rate

Multiple-unit ownership of nondurable products is an important component of sales in many product categories. Based on the Bass model, this paper develops a new model considering the multiple-unit adoptions as a diffusion process under the influence of product sampling. Though the analysis aims to determine the optimal dynamic sampling effort for a firm and the results demonstrate that experience sampling can accelerate the diffusion process, the best time to send free samples is just before the product being launched. Multiple-unit purchasing behavior can increase sales to make more profit for a firm, and it needs more samples to make the product known much better. The local sensitivity analysis shows that the increase of both external coefficients and internal coefficients has a negative influence on the sampling level, but the internal influence on the subsequent multiple-unit adoptions has little significant influence on the sampling. Using the logistic regression along with linear regression, the global sensitivity analysis gives a whole analysis of the interaction of all factors, which manifests the external influence and multiunit purchase rate are two most important factors to influence the sampling level and net present value of the new product, and presents a two-stage method to determine the sampling level.

scholarly journals Latency-bounded target set selection in social networks

2014 ◽  
Vol 535 ◽  
pp. 1-15 ◽  
Author(s):  
Ferdinando Cicalese ◽  
Gennaro Cordasco ◽  
Luisa Gargano ◽  
Martin Milanič ◽  
Ugo Vaccaro
Keyword(s):  
Social Networks ◽  
Target Set ◽  
Target Set Selection

scholarly journals Social Influence Maximization in Hypergraphs

Entropy ◽  
2021 ◽  
Vol 23 (7) ◽  
pp. 796
Author(s):  
Alessia Antelmi ◽  
Gennaro Cordasco ◽  
Carmine Spagnuolo ◽  
Przemysław Szufel
Keyword(s):  
Diffusion Process ◽  
Diffusion Model ◽  
Real World ◽  
Information Diffusion ◽  
Input Graph ◽  
Extensive Evaluation ◽  
Linear Threshold ◽  
Algorithmic Question ◽  
Target Set ◽  
Target Set Selection

This work deals with a generalization of the minimum Target Set Selection (TSS) problem, a key algorithmic question in information diffusion research due to its potential commercial value. Firstly proposed by Kempe et al., the TSS problem is based on a linear threshold diffusion model defined on an input graph with node thresholds, quantifying the hardness to influence each node. The goal is to find the smaller set of items that can influence the whole network according to the diffusion model defined. This study generalizes the TSS problem on networks characterized by many-to-many relationships modeled via hypergraphs. Specifically, we introduce a linear threshold diffusion process on such structures, which evolves as follows. Let H=(V,E) be a hypergraph. At the beginning of the process, the nodes in a given set S⊆V are influenced. Then, at each iteration, (i) the influenced hyperedges set is augmented by all edges having a sufficiently large number of influenced nodes; (ii) consequently, the set of influenced nodes is enlarged by all the nodes having a sufficiently large number of already influenced hyperedges. The process ends when no new nodes can be influenced. Exploiting this diffusion model, we define the minimum Target Set Selection problem on hypergraphs (TSSH). Being the problem NP-hard (as it generalizes the TSS problem), we introduce four heuristics and provide an extensive evaluation on real-world networks.

scholarly journals Latency-Bounded Target Set Selection in Social Networks

2013 ◽  
pp. 65-77 ◽  
Author(s):  
Ferdinando Cicalese ◽  
Gennaro Cordasco ◽  
Luisa Gargano ◽  
Martin Milanič ◽  
Ugo Vaccaro
Keyword(s):  
Social Networks ◽  
Target Set ◽  
Target Set Selection

VIRAL MARKETING IN SOCIAL NETWORKS

2019 ◽  
Vol 5 (5) ◽  
Author(s):  
Elena Gerasikova ◽  
Milena Ischenko ◽  
Olga Saenkova ◽  
Nataliya Yasenkova
Keyword(s):  
Social Networks ◽  
Viral Marketing
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