World Wide Web

, Volume 22, Issue 2, pp 533–553 | Cite as

ProfitLeader: identifying leaders in networks with profit capacity

  • Zhongjing Yu
  • Junming ShaoEmail author
  • Qinli Yang
  • Zejun Sun
Part of the following topical collections:
  1. Special Issue on Deep vs. Shallow: Learning for Emerging Web-scale Data Computing and Applications


Identifying ‘Leaders’ in a network is a significant step to optimize the use of available resources, accelerate or hinder spreading information. In this paper, we propose a new measure to characterize the importance of a node, called ProfitLeader, which considers the problem of critical node identification from a novel perspective: profit capacity. Unlike established approaches to identify influential nodes in networks based on its local topology or information diffusion capacity, ProfitLeader, ranks the influence of each individual node via its profit capacity. The basic idea is to view each node as a person, and measure how much profit “the person” can offer for others. Namely, the more profit “the person” provides for others, the more important the node is. To characterize the profit capacity of each node, its available resource and sharing probability to its neighbors are investigated. Building upon the profit capacity, ProfitLeader has several remarkable advantages: (a) It provides a simple, intuitive yet effective way to rank the nodes in networks from a new viewpoint; (b) ProfitLeader allows finding the most important nodes (with high quality). (c) ProfitLeader is fully automatic, and no parameters are required to be specified by users. Extensive experiments on both small networks and large-scale networks have demonstrated its superiority over several state-of-the-art algorithms.


Network mining Critical node identification Profit capacity 



This work is supported by the National Natural Science Foundation of China (61403062, 61433014, 41601025), China Postdoctoral Science Foundation (2014M552344,2015M580786), Science-Technology Foundation for Young Scientist of Sichuan Province (2016JQ0007), National key research and development program (2016YFB0502300),and Sichuan province soft science research project (2017ZR0208).


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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Zhongjing Yu
    • 1
  • Junming Shao
    • 1
    Email author
  • Qinli Yang
    • 1
  • Zejun Sun
    • 2
  1. 1.Big Data Research Center, School of Computer Science and EngineeringUniversity of Electronic Science and Technology of ChinaChengduChina
  2. 2.School of Information Science and EngineeringCentral South UniversityChangshaChina

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