Link Prediction in the Twitter Mention Network: Impacts of Local Structure and Similarity of Interest

Hadrien Hours, Eric Fleury, Marton Karsai

Research output: Contribution to Book/Report typesConference contributionpeer-review

Abstract (may include machine translation)

The creation of social ties is driven by several factors which can arguably be related to individual preferences and to the common social environment of individuals. Effects of homophily and triadic closure mechanisms are claimed to be important in terms of initiating new social interactions and in turn to shape the global social structure. This way they eventually provide some potential to predict the creation of social ties between disconnected people sharing common friends or common subjects of interest. In this paper we analyze a large Twitter data corpus and quantify similarities between people by considering the set of their common friends and the set of their commonly shared hashtags in order to predict mention links among them. We show that these similarity measures are correlated among connected people and that the combination of contextual and local structural features provides better predictions as compared to cases where they are considered separately. These results help us to better understand the evolution of egocentric and global social networks and provide advances in the design of better recommendation systems and resource allocation plans.

Original languageEnglish
Title of host publicationProceedings - 16th IEEE International Conference on Data Mining Workshops, ICDMW 2016
EditorsCarlotta Domeniconi, Francesco Gullo, Francesco Bonchi, Francesco Bonchi, Josep Domingo-Ferrer, Ricardo Baeza-Yates, Ricardo Baeza-Yates, Ricardo Baeza-Yates, Zhi-Hua Zhou, Xindong Wu
PublisherIEEE Computer Society
Pages454-461
Number of pages8
ISBN (Electronic)9781509054725
DOIs
StatePublished - 2 Jul 2016
Externally publishedYes
Event16th IEEE International Conference on Data Mining Workshops, ICDMW 2016 - Barcelona, Spain
Duration: 12 Dec 201615 Dec 2016

Publication series

NameIEEE International Conference on Data Mining Workshops, ICDMW
Volume0
ISSN (Print)2375-9232
ISSN (Electronic)2375-9259

Conference

Conference16th IEEE International Conference on Data Mining Workshops, ICDMW 2016
Country/TerritorySpain
CityBarcelona
Period12/12/1615/12/16

Keywords

  • Evolving social networks
  • Homophily
  • Link prediction
  • Triadic closure
  • Twitter data

Fingerprint

Dive into the research topics of 'Link Prediction in the Twitter Mention Network: Impacts of Local Structure and Similarity of Interest'. Together they form a unique fingerprint.

Cite this