On link stability detection for online social networks

Zhang, Ji and Tan, Leonard and Tao, Xiaohui ORCID: https://orcid.org/0000-0002-0020-077X and Lin, Jerry Chun-Wei and Li, Hongzhou and Chang, Liang (2018) On link stability detection for online social networks. In: 29th International Conference on Database and Expert Systems Applications (DEXA 2018), 3-6 Sept 2018, Regensburg, Germany.


Link stability detection has been an important and long-standing problem within the link prediction domain. However, it has often been overlooked as being trivial and has not been adequately dealt with in link prediction. In this paper, we present an innovative method: Multi-Variate Vector Autoregression (MVVA) analysis to determine link stability. Our method adopts link dynamics to establish stability confidence scores within a clique sized model structure observed over a period of 30 days. Our method also improves detection accuracy and representation of stable links through a user-friendly interactive interface. In addition, a good accuracy to performance trade-off in our method is achieved through the use of Random Walk Monte Carlo estimates. Experiments with Facebook datasets reveal that our method performs better than traditional univariate methods for stability identification in online social networks.

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Item Type: Conference or Workshop Item (Commonwealth Reporting Category E) (Paper)
Refereed: Yes
Item Status: Live Archive
Additional Information: Permanent restricted access to Published version in accordance with the copyright policy of the publisher.
Faculty/School / Institute/Centre: Historic - Faculty of Health, Engineering and Sciences - School of Agricultural, Computational and Environmental Sciences (1 Jul 2013 - 5 Sep 2019)
Faculty/School / Institute/Centre: Historic - Institute for Resilient Regions - Centre for Health, Informatics and Economic Research (1 Aug 2018 - 31 Mar 2020)
Date Deposited: 25 May 2020 05:42
Last Modified: 03 Jun 2020 05:46
Uncontrolled Keywords: link stability, graph theory, online social networks, Hamiltonian Monte Carlo (HMC)
Fields of Research (2008): 08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080109 Pattern Recognition and Data Mining
Fields of Research (2020): 46 INFORMATION AND COMPUTING SCIENCES > 4699 Other information and computing sciences > 469999 Other information and computing sciences not elsewhere classified
Identification Number or DOI: https://doi.org/10.1007/978-3-319-98809-2_20
URI: http://eprints.usq.edu.au/id/eprint/38132

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