NDLI: Relational Learning with Social Status Analysis

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Relational Learning with Social Status Analysis

Content Provider	ACM Digital Library
Author	Hu, Xia Wu, Liang Liu, Huan
Abstract	Relational learning has been proposed to cope with the interdependency among linked instances in social network analysis, which often adopts network connectivity and social media content for prediction. A common assumption in existing relational learning methods is that data instances are equally important. The algorithms developed based on the assumption may be significantly affected by outlier data and thus less robust. In the meantime, it has been well established in social sciences that actors are naturally of different social status in a social network. Motivated by findings from social sciences, in this paper, we investigate whether social status analysis could facilitate relational learning. Particularly, we propose a novel framework RESA to model social status using the network structure. It extracts robust and intrinsic latent social dimensions for social actors, which are further exploited as features for supervised learning models. The proposed method is applicable for real-world relational learning problems where noise exists. Extensive experiments are conducted on datasets obtained from real-world social media platforms. Empirical results demonstrate the effectiveness of RESA and further experiments are conducted to help understand the effects of parameter settings to the proposed model and how local social status works.
Starting Page	513
Ending Page	522
Page Count	10
File Format	PDF
ISBN	9781450337168
DOI	10.1145/2835776.2835782
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2016-02-08
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Social dimensions Social media Relational learning
Content Type	Text
Resource Type	Article

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