Who spread to whom? Inferring online social networks with user features

Derek Wang, Wanlei Zhou, James Xi Zheng, Sheng Wen, Jun Zhang, Yang Xiang

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionpeer-review

2 Citations (Scopus)


Network inference has been extensively studied to better understand the information diffusion in online social networks. In this field, state-of- art widely adopted a priori knowledge related to users' infection timestamps. Researchers also assume that the smaller the time difference between two nodes, the higher the likelihood of an edge between the pair of users. However, according to our technical analyses and empirical studies, existing methods have two critical problems 1) alternative spreading paths 2) users' delivery delay, which leads to the inaccuracy of previous methods. In this paper, we developed an innovative method to address the inference inaccuracy caused by the exposed two problems. This method determined the existence of an edge between a pair of users according to part of the users' features. The experiment results suggested that our method achieved around 70% accuracy in inferring network structures while existing methods failed in the same tasks.

Original languageEnglish
Title of host publication2018 IEEE International Conference on Communications
Subtitle of host publicationProceedings
Place of PublicationPiscataway, NJ
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Number of pages6
ISBN (Print)9781538631805
Publication statusPublished - 2018
Externally publishedYes
EventIEEE International Conference on Communications (2018) - Kansas City, United States
Duration: 20 May 201824 May 2018

Publication series

NameIEEE International Conference on Communications
ISSN (Electronic)1938-1883


ConferenceIEEE International Conference on Communications (2018)
Abbreviated titleICC 2018
Country/TerritoryUnited States
CityKansas City


  • Network inference
  • Security
  • Social media


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