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  • 1.
    Al Falahi, Kanna
    et al.
    UAE University, United Arab Emirates.
    Atif, Yacine
    UAE University, United Arab Emirates.
    Abraham, Ajith
    VSB-Technical University of Ostrava, Czech Republic.
    Models of Influence in Online Social Networks2014In: International Journal of Intelligent Systems, ISSN 0884-8173, E-ISSN 1098-111X, Vol. 2, no 29, p. 161-183Article in journal (Refereed)
    Abstract [en]

    Online social networks gained their popularity from relationships users can build with each other. These social ties play an important role in asserting users’ behaviors in a social network. For example, a user might purchase a product that his friend recently bought. Such phenomenon is called social influence, which is used to study users’ behavior when the action of one user can affect the behavior of his neighbors in a social network. Social influence is increasingly investigated nowadays as it can help spreading messages widely, particularly in the context of marketing, to rapidly promote products and services based on social friends’ behavior in the network. This wide interest in social influence raises the need to develop models to evaluate the rate of social influence. In this paper, we discuss metrics used to measure influence probabilities. Then, we reveal means to maximize social influence by identifying and using the most influential users in a social network. Along with these contributions, we also survey existing social influence models, and classify them into an original categorization framework. Then, based on our proposed metrics, we show the results of an experimental evaluation to compare the influence power of some of the surveyed salient models used to maximize social influence.

  • 2.
    Torra, Vicenç
    et al.
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.
    Jonsson, Annie
    University of Skövde, School of Bioscience. University of Skövde, The Systems Biology Research Centre.
    Navarro‐Arribas, Guillermo
    Universitat Autònoma de Barcelona, Spain / Center for Cybersecurity Research of Catalonia (CYBERCAT), Spain.
    Salas, Julián
    Center for Cybersecurity Research of Catalonia (CYBERCAT), Spain / Universitat Oberta de Catalunya (UOC), Barcelona, Spain.
    Synthetic generation of spatial graphs2018In: International Journal of Intelligent Systems, ISSN 0884-8173, E-ISSN 1098-111X, Vol. 32, no 12, p. 2364-2378Article in journal (Refereed)
    Abstract [en]

    Graphs can be used to model many different types of interaction networks, for example, online social networks or animal transport networks. Several algorithms have thus been introduced to build graphs according to some predefined conditions. In this paper, we present an algorithm that generates spatial graphs with a given degree sequence. In spatial graphs, nodes are located in a space equiped with a metric. Our goal is to define a graph in such a way that the nodes and edges are positioned according to an underlying metric. More particularly, we have constructed a greedy algorithm that generates nodes proportional to an underlying probability distribution from the spatial structure, and then generates edges inversely proportional to the Euclidean distance between nodes. The algorithm first generates a graph that can be a multigraph, and then corrects multiedges. Our motivation is in data privacy for social networks, where a key problem is the ability to build synthetic graphs. These graphs need to satisfy a set of required properties (e.g., the degrees of the nodes) but also be realistic, and thus, nodes (individuals) should be located according to a spatial structure and connections should be added taking into account nearness.

1 - 2 of 2
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