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Predicting Friendship Using a Unified Probability Model

  • Zhijuan Kou
  • Hua Wang
  • Pingpeng YuanEmail author
  • Hai Jin
  • Xia Xie
Conference paper
First Online:
Part of the Communications in Computer and Information Science book series (CCIS, volume 1120)

Abstract

Now, it is popular for people to share their feelings, activities tagged with geography and temporal information in Online Social Networks (OSNs). The spatial and temporal interactions occurred in OSNs contain a wealth of information to indicate friendship between persons. Existing researches generally focused on single dimension: spatial or temporal dimension. The simplified model only works in limited scenarios. Here, we aim to understand the probability of friendship and the place and time of interactions. First, spatial similarity of interactions is defined as a vector of places where persons checked in. Second, we employ exponential functions to characterize the change of strength of interactions as time goes on. Finally, a unified probability model to predict friendship between two persons is given. The model contains two sub-models based on spatial similarity and temporal similarity respectively. The experimental results on four data sets including spatial data sets (Gowalla and Weeplaces) and temporal data sets (Higgs Twitter Data set, High school Call Data set) show that our model works as expected.

Keywords

Spatial similarity Temporal similarity Models 
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Notes

Acknowledgment

The research is supported by The National Key Research & Development Program of China (No. 2018YFB1004002), NSFC (No. 61672255), Science and Technology Planning Project of Guangdong Province, China (No. 2016B030306003 and 2016B030305002), and the Fundamental Research Funds for the Central Universities, HUST.

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Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Zhijuan Kou
    • 1
  • Hua Wang
    • 1
  • Pingpeng Yuan
    • 1
    Email author
  • Hai Jin
    • 1
  • Xia Xie
    • 1
  1. 1.National Engineering Research Center for Big Data Technology and System, Service Computing Technology and System Lab, Cluster and Grid Computing Lab, School of Computer Science and TechnologyHuazhong University of Science and TechnologyWuhanChina

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