On Multiplexity-Aware Influence Spread in Social Networks

Abstract

The analysis of influence spread in monoplex (single-layer) networks where all interactions are treated equally has been widely studied since the early 2000s. In complex networks, e.g., social networks, multi-agent networks, etc., the elements of networks interact in many ways, which results in multiplex (multiple-layer) networks. In this paper, we analyze the multiplexity-aware influence spread in two extended linear threshold models with two different protocols that explain how an agent updates its state combining signals from all layers. We then generalize the cohesiveness structural property to multiplex networks that explains why multiplexity facilitates or impedes a propagation. Based on this property, we present the optimal solutions by solving integer linear programming problems for influence minimization in multiplex linear threshold models. INDEX TERMS Multiplex networks, linear threshold model, influence minimization, integer linear programming.

Improvement on PDP Evaluation Performance Based on Neural Networks and SGDK-means Algorithm

“…We believe that the training of neural networks will be easier when the capability of computation is improved. The proposed method can be employed to study big data access control in social networks (Yu et al 2021a ; Yang et al 2020 ) and CoVID-19 (Yu et al 2021b , c , d ).…”

Progressive Secret Image Sharing Scheme Based on Semantic Segmentation

“…It aims to make the processed image look natural, thus, it will not be suspected by the attackers. We can also apply the proposed scheme to social networks [32], multi-agent systems [33] and automated manufacturing systems [34].…”

The Dynamical Interplay of Collective Attention, Awareness and Epidemics Spreading in the Multiplex Social Networks During COVID-19

“…A large corpus of scientific literature have studied the phenomenon of spreading deepen inside a better understanding on how it produces dynamics which depends on various structural and human-related factors [9] , [10] , suggesting that in social networks the dynamics depend on the nature of social ties [11] . Since behaviours [11] , [12] , misinformation [13] , [14] , infectious diseases [15] , [16] , distress [17] , emotions [18] 0and competing processes [19] , [20] spread through interactions of networked individuals, starting from classical epidemiological models [21] – [23] , and based on an interdisciplinary approach which involves several research fields in complex network science [24] – [27] , we are able to model the diffusion dynamics into the social networks. Taking into consideration the modeling approach of dynamics of social contagion and spreading processes in complex networks [16] , [18] , [28] , we are interested to grasp the linkage among the shape of collective phenomena and their interpersonal spreading in networks structure, in response to a triggering force as an extraordinary event able to shift the common outcomes to unexpected peaks and clusters.…”

“…By contrast, in [16] , [17] , it has been explored and quantified the impact of the co-evolution of the two processes in all the layers considering the introduction of the multiplexity dimension. Multiplex networks representation consider the same set of nodes in all the layers of interactions, and constitute the most suitable network structure to understand such dynamical processes and their complex interdependence [12] , [31] – [33] . The interplay between epidemic spreading and awareness dynamics allows to highlight the role of structures and human-related factors in the multiplex networks which influence the dynamical trend of both processes.…”