Link prediction in the Twitter mention network: impacts of local structure and similarity of interest

Hadrien Hours 1, 2 Eric Fleury 1, 2 Márton Karsai 1, 2
1 IXXI - Institut Rhône-Alpin des systèmes complexes
2 DANTE - Dynamic Networks : Temporal and Structural Capture Approach
Inria Grenoble - Rhône-Alpes, LIP - Laboratoire de l'Informatique du Parallélisme, IXXI - Institut Rhône-Alpin des systèmes complexes
Abstract : 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.
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Communication dans un congrès
16th IEEE International Conference on Data Mining (ICDM) - DMHAA Workshop, Dec 2016, Barcelona, Spain
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https://hal.inria.fr/hal-01403301
Contributeur : Márton Karsai <>
Soumis le : vendredi 25 novembre 2016 - 16:41:37
Dernière modification le : vendredi 20 avril 2018 - 15:44:27

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  • HAL Id : hal-01403301, version 1

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Hadrien Hours, Eric Fleury, Márton Karsai. Link prediction in the Twitter mention network: impacts of local structure and similarity of interest . 16th IEEE International Conference on Data Mining (ICDM) - DMHAA Workshop, Dec 2016, Barcelona, Spain. 〈hal-01403301〉

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