SLIND: Identifying Stable Links in Online Social Networks

  • Ji ZhangEmail author
  • Leonard Tan
  • Xiaohui Tao
  • Xiaoyao Zheng
  • Yonglong LuoEmail author
  • Jerry Chun-Wei Lin
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10828)


Link stability detection has been an important and long-standing problem in the link prediction domain. However, it is often easily overlooked as being trivial and has not been adequately dealt with in link prediction [1]. In this demo, we introduce an innovative link stability detection system, called SLIND (Stable LINk Detection), that adopts a Multi-Variate Vector Autoregression analysis (MVVA) approach using link dynamics to establish stability confidence scores of links within a clique of nodes in online social networks (OSN) to improve detection accuracy and the representation of stable links. SLIND is also able to determine stable links through the use of partial feature information and potentially scales well to much larger datasets with very little accuracy to performance trade-offs using random walk Monte-Carlo estimates.


Link stability Graph theory Online social networks Hamiltonian Monte Carlo (HMC) 



This research is partially supported by National Science Foundation of China (No. 61672039, No. 61772034, No. 61503092) and Guangxi Key Laboratory of Trusted Software (No. kx201615).


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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.The University of Southern QueenslandToowoombaAustralia
  2. 2.Anhui Normal UniversityWuhuChina
  3. 3.Harbin Institute of Technology Shenzhen Graduate SchoolShenzhenChina

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