TY - GEN
T1 - Discovering correlation between communities and likes in Facebook
AU - Salamanos, Nikos
AU - Voudigari, Elli
AU - Papageorgiou, Theodore
AU - Vazirgiannis, Michalis
PY - 2012/12/1
Y1 - 2012/12/1
N2 - In this paper we investigate the correlation between the social network communities as defined by a community detection algorithm and the Facebook pages annotated as Likes by its users. Our goal is twofold. First, we aim to examine the relation between the underlined social dynamic, as expressed indirectly by a community structure, with the users' characteristics represented by Likes. Second, to valuate the outcome of the community detection algorithm. To the best of our knowledge this is the first study of the correlation between community structure and users' Likes in Facebook. Using a standard crawling method, such as the Breadth First Search, we collect: a) several snapshots of a subgraph of Facebook, b) the users' Likes in Web and Facebook pages and c) the pages' categories as classified by the owner of the page. We study several graph samples along with their community structure. The experimental results demonstrate that in the case of users' Likes, the correlation ranges from small to medium between communities and the whole population, while it is even smaller between communities. Moreover, there is a high correlation in terms of Likes' categories between the different communities and between communities and the whole population. This fact proves that Likes constitute a criterion of distinction among the communities and verifies the intuition that lead us towards this research.
AB - In this paper we investigate the correlation between the social network communities as defined by a community detection algorithm and the Facebook pages annotated as Likes by its users. Our goal is twofold. First, we aim to examine the relation between the underlined social dynamic, as expressed indirectly by a community structure, with the users' characteristics represented by Likes. Second, to valuate the outcome of the community detection algorithm. To the best of our knowledge this is the first study of the correlation between community structure and users' Likes in Facebook. Using a standard crawling method, such as the Breadth First Search, we collect: a) several snapshots of a subgraph of Facebook, b) the users' Likes in Web and Facebook pages and c) the pages' categories as classified by the owner of the page. We study several graph samples along with their community structure. The experimental results demonstrate that in the case of users' Likes, the correlation ranges from small to medium between communities and the whole population, while it is even smaller between communities. Moreover, there is a high correlation in terms of Likes' categories between the different communities and between communities and the whole population. This fact proves that Likes constitute a criterion of distinction among the communities and verifies the intuition that lead us towards this research.
KW - Community structure
KW - Correlation
KW - Facebook
KW - Social networks
UR - https://www.scopus.com/pages/publications/84875504836
U2 - 10.1109/GreenCom.2012.60
DO - 10.1109/GreenCom.2012.60
M3 - Conference contribution
AN - SCOPUS:84875504836
SN - 9780769548654
T3 - Proceedings - 2012 IEEE Int. Conf. on Green Computing and Communications, GreenCom 2012, Conf. on Internet of Things, iThings 2012 and Conf. on Cyber, Physical and Social Computing, CPSCom 2012
SP - 368
EP - 371
BT - Proceedings - 2012 IEEE Int. Conf. on Green Computing and Communications, GreenCom 2012, Conf. on Internet of Things, iThings 2012 and Conf. on Cyber, Physical and Social Computing, CPSCom 2012
T2 - 2012 IEEE International Conference on Green Computing and Communications, GreenCom 2012, 2012 IEEE International Conference on Internet of Things, iThings 2012 and 5th IEEE International Conference on Cyber, Physical and Social Computing, CPSCom 2012
Y2 - 20 November 2012 through 23 November 2012
ER -