NDLI: User profiling in an ego network: co-profiling attributes and relationships

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User profiling in an ego network: co-profiling attributes and relationships

Content Provider	ACM Digital Library
Author	Li, Rui Chang, Kevin Chen-Chuan Wang, Chi
Abstract	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%.
Starting Page	819
Ending Page	830
Page Count	12
File Format	PDF
ISBN	9781450327442
DOI	10.1145/2566486.2568045
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2014-04-07
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Ego network Social network User profiling
Content Type	Text
Resource Type	Article

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