Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/79345
Title: Multiple social role embedding
Authors: Xu, L 
Wei, X
Cao, J 
Yu, PS
Keywords: Data mining
Network embedding
Social networks
Issue Date: 2018
Publisher: Institute of Electrical and Electronics Engineers Inc.
Source: Proceedings - 2017 International Conference on Data Science and Advanced Analytics, DSAA 2017, 2018, v. 2018-January, p. 581-589 How to cite?
Abstract: Network embedding has been increasingly employed in networked data mining applications as it is effective to learn node embeddings that encode the network structure. Existing network models usually learn a single embedding for each node. In practice, a person may interact with others in different roles, such as interacting with schoolmates as a student, and with colleagues as an employee. Obviously, different roles exhibit different characteristics or features. Hence, only learning a single embedding responsible for all roles is not appropriate. In this paper, we thus introduce a concept of multiple social role (MSR) into social network embedding for the first time. The MSR models multiple roles people play in society, such as student and employee. To make the embedding more versatile, we thus propose a multiple social role embedding (MSRE) model to preserve both the network structure and social roles. Empirical evaluation on various real-world social networks demonstrates advantages of the proposed MSRE over the state-of-the-art embedding models in link prediction and multi-label classification.
Description: 4th International Conference on Data Science and Advanced Analytics, DSAA 2017, Tokyo, Japan, 19-21 October 2017
URI: http://hdl.handle.net/10397/79345
ISBN: 9781509050048
DOI: 10.1109/DSAA.2017.23
Appears in Collections:Conference Paper

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