TY - GEN
T1 - Link Prediction in the Twitter Mention Network
T2 - 16th IEEE International Conference on Data Mining Workshops, ICDMW 2016
AU - Hours, Hadrien
AU - Fleury, Eric
AU - Karsai, Marton
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2016/7/2
Y1 - 2016/7/2
N2 - 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.
AB - 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.
KW - Evolving social networks
KW - Homophily
KW - Link prediction
KW - Triadic closure
KW - Twitter data
UR - http://www.scopus.com/inward/record.url?scp=85015154450&partnerID=8YFLogxK
U2 - 10.1109/ICDMW.2016.0071
DO - 10.1109/ICDMW.2016.0071
M3 - Conference contribution
AN - SCOPUS:85015154450
T3 - IEEE International Conference on Data Mining Workshops, ICDMW
SP - 454
EP - 461
BT - Proceedings - 16th IEEE International Conference on Data Mining Workshops, ICDMW 2016
A2 - Domeniconi, Carlotta
A2 - Gullo, Francesco
A2 - Bonchi, Francesco
A2 - Bonchi, Francesco
A2 - Domingo-Ferrer, Josep
A2 - Baeza-Yates, Ricardo
A2 - Baeza-Yates, Ricardo
A2 - Baeza-Yates, Ricardo
A2 - Zhou, Zhi-Hua
A2 - Wu, Xindong
PB - IEEE Computer Society
Y2 - 12 December 2016 through 15 December 2016
ER -