Journal of information and communication convergence engineering

  • 제16권4호
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  • Pages.213-220
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  • 2018
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  • 2234-8255(pISSN)
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  • 2234-8883(eISSN)
한국정보통신학회 (The Korea Institute of Information and Commucation Engineering)
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Influence Maximization Scheme against Various Social Adversaries

  • Noh, Giseop (Division of Software Convergence, College of Engineering, Cheongju University) ;
  • Oh, Hayoung (DASAN University College in Ajou University) ;
  • Lee, Jaehoon (Department of Computer Science and Engineering, Seoul National University)
  • 투고 : 2018.06.06
  • 심사 : 2018.11.14
  • 발행 : 2018.12.31
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초록

With the exponential developments of social network, their fundamental role as a medium to spread information, ideas, and influence has gained importance. It can be expressed by the relationships and interactions within a group of individuals. Therefore, some models and researches from various domains have been in response to the influence maximization problem for the effects of "word of mouth" of new products. For example, in reality, more than two related social groups such as commercial companies and service providers exist within the same market issue. Under such a scenario, they called social adversaries competitively try to occupy their market influence against each other. To address the influence maximization (IM) problem between them, we propose a novel IM problem for social adversarial players (IM-SA) which are exploiting the social network attributes to infer the unknown adversary's network configuration. We sophisticatedly define mathematical closed form to demonstrate that the proposed scheme can have a near-optimal solution for a player.

키워드

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Fig. 1. Node connectivity in a graph.

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Fig. 2. A graphical example with three social players.

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Fig. 3. An example of local bridges (satisfying strong triadic closure property as mentioned previously [27] with strong ties (black solid lines) and weak ties (black dotted lines). The promoter (c1) knows his region c1 but does not know about the number of nodes are in the adversary region (c2) and how the nodes in c2 are configured.

E1ICAW_2018_v16n4_213_f0004.png 이미지

Fig. 4. GHC algorithm with two given inputs (l, f), where l is the cardinality of the seed set and f is the function of an information cascading model.

E1ICAW_2018_v16n4_213_f0005.png 이미지

Fig. 5. GHC-A exploiting local bridges with node characteristics (BC and CC).

E1ICAW_2018_v16n4_213_f0006.png 이미지

Fig. 6. The number of activated nodes in the adversary cluster as a function of the increasing number of Γ.

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