TY - GEN
T1 - User profiling in an ego network
T2 - 23rd International Conference on World Wide Web, WWW 2014
AU - Li, Rui
AU - Wang, Chi
AU - Chang, Kevin Chen Chuan
PY - 2014/4/7
Y1 - 2014/4/7
N2 - User attributes, such as occupation, education, and location, are important for many applications. In this paper, we study the problem of profiling user attributes in social network. To capture the correlation between attributes and social connections, we present a new insight that social connections are discriminatively correlated with attributes via a hidden factor - relationship type. For example, a user's colleagues are more likely to share the same employer with him than other friends. Based on the insight, we propose to co-profile users' attributes and relationship types of their connections. To achieve co-profiling, we develop an efficient algorithm based on an optimization framework. Our algorithm captures our insight effectively. It iteratively profiles attributes by propagation via certain types of connections, and profiles types of connections based on attributes and the network structure. We conduct extensive experiments to evaluate our algorithm. The results show that our algorithm profiles various attributes accurately, which improves the state-of-the-art methods by 12%. Copyright is held by the International World Wide Web Conference Committee (IW3C2).
AB - User attributes, such as occupation, education, and location, are important for many applications. In this paper, we study the problem of profiling user attributes in social network. To capture the correlation between attributes and social connections, we present a new insight that social connections are discriminatively correlated with attributes via a hidden factor - relationship type. For example, a user's colleagues are more likely to share the same employer with him than other friends. Based on the insight, we propose to co-profile users' attributes and relationship types of their connections. To achieve co-profiling, we develop an efficient algorithm based on an optimization framework. Our algorithm captures our insight effectively. It iteratively profiles attributes by propagation via certain types of connections, and profiles types of connections based on attributes and the network structure. We conduct extensive experiments to evaluate our algorithm. The results show that our algorithm profiles various attributes accurately, which improves the state-of-the-art methods by 12%. Copyright is held by the International World Wide Web Conference Committee (IW3C2).
KW - Ego network
KW - Social network
KW - User profiling
UR - http://www.scopus.com/inward/record.url?scp=84909630607&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84909630607&partnerID=8YFLogxK
U2 - 10.1145/2566486.2568045
DO - 10.1145/2566486.2568045
M3 - Conference contribution
AN - SCOPUS:84909630607
T3 - WWW 2014 - Proceedings of the 23rd International Conference on World Wide Web
SP - 819
EP - 829
BT - WWW 2014 - Proceedings of the 23rd International Conference on World Wide Web
PB - Association for Computing Machinery, Inc
Y2 - 7 April 2014 through 11 April 2014
ER -