SLIND: identifying stable links in online social networks

Zhang, Ji and Tan, Leonard and Tao, Xiaohui ORCID: https://orcid.org/0000-0002-0020-077X and Zheng, Xiaoyao and Luo, Yonglong and Lin, Jerry Chun-Wei (2018) SLIND: identifying stable links in online social networks. In: 23rd International Conference on Database Systems for Advanced Applications (DASFAA 2018), 21-24 May 2018, Gold Coast, Australia.


Abstract

Link stability detection has been an important and long-standing problem in the link prediction domain. However, it is often easily overlooked as being trivial and has not been adequately dealt with in link prediction [1]. In this demo, we introduce an innovative link stability detection system, called SLIND (Stable LINk Detection), that adopts a Multi-Variate Vector Autoregression analysis (MVVA) approach using link dynamics to establish stability confidence scores of links within a clique of nodes in online social networks (OSN) to improve detection accuracy and the representation of stable links. SLIND is also able to determine stable links through the use of partial feature information and potentially scales well to much larger datasets with very little accuracy to performance trade-offs using random walk Monte-Carlo estimates.


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Item Type: Conference or Workshop Item (Commonwealth Reporting Category E) (Paper)
Refereed: Yes
Item Status: Live Archive
Additional Information: © Springer International Publishing AG 2018.
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 - Faculty of Health, Engineering and Sciences - School of Agricultural, Computational and Environmental Sciences (1 Jul 2013 - 5 Sep 2019)
Date Deposited: 23 Jul 2020 04:55
Last Modified: 30 Sep 2020 22:41
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/10.1007/978-3-319-91458-9_54
URI: http://eprints.usq.edu.au/id/eprint/38131

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