Attribute-Based Influence Maximization in Social Networks

  • Jiuxin Cao
  • Tao Zhou
  • Dan Dong
  • Shuai Xu
  • Ziqing Zhu
  • Zhuo Ma
  • Bo Liu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10041)


As traditional advertising model exposes its weakness of ignoring consumer interests, the concept of narrow advertising draws increasingly more attention which considers the feature of each user. Under this specific environment, effective viral marketing has to select a set of initial users to maximize their influence on the targeted customers. This paper aims at the integration of viral marketing and narrow advertising, by proposing a novel problem called attribute-based influence maximization. Firstly, the problem definition is presented with the consideration of user features. Then the influence probability between two nodes is modeled and two heuristic algorithms, Sum of Probability Covered Algorithm (SoPCA) and Community-based Algorithm (CBA), are designed. Finally, experiments on six datasets are conducted to verify the effectiveness of proposed algorithms.


Influence maximization Influence probability Social networks User attribute 



This work is supported by National Natural Science Foundation of China (61272531, 61202449, 61272054, 61370207, 61370208, 61300024, 61320106007 and 61472081), China high technology 863 program (2013AA013503), Jiangsu Technology Planning Program (SBY2014021039-10), Jiangsu Provincial Key Laboratory of Network and Information Security under Grant No. BM2003201 and Key Laboratory of Computer Network and Information Integration of Ministry of Education of China under Grant No. 93k-9.


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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Jiuxin Cao
    • 1
  • Tao Zhou
    • 1
  • Dan Dong
    • 1
  • Shuai Xu
    • 1
  • Ziqing Zhu
    • 1
  • Zhuo Ma
    • 1
  • Bo Liu
    • 1
  1. 1.School of Computer Science and EngineeringSoutheast UniversityNanjingChina

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