Rising Star Evaluation Based on Extreme Learning Machine in Geo-Social Networks

  • Yuliang MaEmail author
  • Ye Yuan
  • Guoren Wang
  • Xin Bi
  • Zhongqing Wang
  • Yishu Wang


In social networks, rising stars are junior individuals who may be not so charming at first but turn out to be outstanding over time. Recently, rising star evaluation has become a popular research topic in the field of social analysis, which is helpful for decision support, cognitive computation, and other practical problems. In this paper, we study the problem of rising star evaluation in geo-social networks. Specifically, given a topic keyword Q and a time point t, we aim at evaluating the latent influence of users to find rising stars, which refer to experts who have few activities and little impact currently on the underlying geo-social network but may become influential experts in the future. To efficiently evaluate future stars, we propose a novel processing framework based on extreme learning machine (ELM) called FS-ELM. FS-ELM consists of three key components. The first component constructs features by incorporating social topology and user behavior patterns. The second component extracts supervised information by discovering topic experts of Q at time (t + Δt); that is, excluding those detected at time t, topic experts obtained at time (t + Δt) can be regarded as rising stars at time t. The third component is ELM-based future star classification that leverages ELM as a departure point to evaluate whether a user is a rising star. Our experimental studies conducted on real-world datasets show that (1) FS-ELM can effectively discover rising stars with a query topic at time t and outperform other traditional methods and (2) user social characteristics have an important impact on the rising star evaluation. This paper studies a novel problem, namely, rising star evaluation in geo-social networks. We propose an advanced processing framework based on ELM by exploiting social topology characteristics and user behavior patterns. The experimental results encouragingly demonstrate the efficiency and effectiveness of the proposed approach.


Extreme learning machine Geo-social network Rising star 


Funding Information

This research is partially funded by the National Key Research and Development Program of China (Grant No. 2016YFC1401900), the National Natural Science Foundation of China (Grant Nos. 61572119, 61572121, 61622202, 61732003, 61729201, 61702086, and U1401256), the Fundamental Research Funds for the Central Universities (Grant Nos. N171604007, and N171904- 007), the Natural Science Foundation of Liaoning Province (Grant no. 201705201- 64), and the China Postdoctoral Science Foundation (Grant no. 2018M631806).

Compliance with Ethical Standards

Conflict of Interest

The authors declare that they have no conflict of interest.

Informed Consent

Informed consent was obtained from all individual participants.

Human and Animal Rights

This article does not contain any studies involving human participants and/or animals by any of the authors.


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© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.School of Computer Science and EngineeringNortheastern UniversityLiaoningChina
  2. 2.School of Computer Science and TechnologyBeijing Institute of TechnologyBeijingChina
  3. 3.Sino-Dutch Biomedical and Information Engineering SchoolNortheastern UniversityLiaoningChina
  4. 4.Information CenterThe First Hospital of China Medical UniversityShenyangChina

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