Homophily is the theory behind the formation of social ties between individuals with similar characteristics or interests. Based on homophily, in a social network it is expected to observe a higher degree of homogeneity among connected than disconnected people. Many researchers use this simple yet effective principal to infer users' missing information and interests based on the information provided by their neighbors. In a directed social network, the neighbors can be further divided into followers and followees. In this work, we investigate the homophily effect in a directed network. To explore the homophily effect in a directed network, we study if a user's personal preferences can be inferred from those of users connected to her (followers or followees). We also study the effectiveness of each of these two groups on prediction one's preferences.
@inproceedings{mohammad2014more,
title = {Am I More Similar to My Followers or Followees? Analyzing Homophily Effect in Directed Social Networks},
author = {Mohammad Ali Abbasi and Reza Zafarani and Jiliang Tang and Huan Liu},
year = {2014},
keywords = {conference},
booktitle = {Proceedings of the 25th ACM Conference on Hypertext and Social Media (HT)},
abstract = {Homophily is the theory behind the formation of social ties between individuals with similar characteristics or interests. Based on homophily, in a social network it is expected to observe a higher degree of homogeneity among connected than disconnected people. Many researchers use this simple yet effective principal to infer users' missing information and interests based on the information provided by their neighbors. In a directed social network, the neighbors can be further divided into followers and followees. In this work, we investigate the homophily effect in a directed network. To explore the homophily effect in a directed network, we study if a user's personal preferences can be inferred from those of users connected to her (followers or followees). We also study the effectiveness of each of these two groups on prediction one's preferences.},
url = {http://ht.acm.org/ht2014/},
pdf = {files/ht90s-abbasiA1.pdf},
}