Stochastic network motif detection in social media

Kai Liu*, Kwok Wai CHEUNG, Jiming LIU

*Corresponding author for this work

Research output: Chapter in book/report/conference proceedingConference proceedingpeer-review

6 Citations (Scopus)


Network motifs refer to recurrent patterns of interconnections which are found to be over-represented in real networks when compared with random ones. Such basic building blocks can well characterize the structure of complex networks. Extending the building blocks to stochastic ones allows for more robust motif detection networks which are stochastic in nature. Network motif analysis, commonly adopted in bioinformatics, has recently been applied to also online social media. In this paper, we propose to detect stochastic network motifs in social media with the conjecture that social interactions are of stochastic nature. In particular, we apply a stochastic motif detection algorithm based on the finite mixture model to both synthesized datasets and real online datasets to evaluate the effectiveness. Also, we discuss how the obtained stochastic motifs could be interpreted and compared qualitatively with some of the results obtained from others which are recently reported in the literature.

Original languageEnglish
Title of host publicationProceedings - 11th IEEE International Conference on Data Mining Workshops, ICDMW 2011
Number of pages8
Publication statusPublished - 2011
Event11th IEEE International Conference on Data Mining Workshops, ICDMW 2011 - Vancouver, BC, Canada
Duration: 11 Dec 201111 Dec 2011

Publication series

NameProceedings - IEEE International Conference on Data Mining, ICDM
ISSN (Print)1550-4786


Conference11th IEEE International Conference on Data Mining Workshops, ICDMW 2011
CityVancouver, BC

Scopus Subject Areas

  • Engineering(all)

User-Defined Keywords

  • Expectation-maximization algorithm
  • Mixture model
  • Social networks
  • Stochastic network motifs


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