Reducing Cumulative Errors of Incremental CP Decomposition in Dynamic Online Social Networks

Author:

Wang Jingjing1,Jiang Wenjun1ORCID,Li Kenli1,Li Keqin2

Affiliation:

1. Hunan University, Changsha City, Hunan Province, China

2. Hunan University and State University of New York, New York, USA

Abstract

CANDECOMP/PARAFAC (CP) decomposition is widely used in various online social network (OSN) applications. However, it is inefficient when dealing with massive and incremental data. Some incremental CP decomposition (ICP) methods have been proposed to improve the efficiency and process evolving data, by updating decomposition results according to the newly added data. The ICP methods are efficient, but inaccurate because of serious error accumulation caused by approximation in the incremental updating. To promote the wide use of ICP, we strive to reduce its cumulative errors while keeping high efficiency. We first differentiate all possible errors in ICP into two types: the cumulative reconstruction error and the prediction error. Next, we formulate two optimization problems for reducing the two errors. Then, we propose several restarting strategies to address the two problems. Finally, we test the effectiveness in three typical dynamic OSN applications. To the best of our knowledge, this is the first work on reducing the cumulative errors of the ICP methods in dynamic OSNs.

Funder

NSFC

Guangdong Provincial NSF

Open Project of Zhejiang Lab

China Scholarships Council

Science and Technology Program of Changsha City

NSF

National Outstanding Youth Science Program of NSFC

Publisher

Association for Computing Machinery (ACM)

Subject

General Computer Science

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