SLIND+: stable LINk detection

Zhang, Ji and Tan, Leonard and Tao, Xiaohui ORCID: https://orcid.org/0000-0002-0020-077X and Li, Hongzhou and Chen, Fulong and Luo, Yonglong (2020) SLIND+: stable LINk detection. In: 20th International Conference on Web Information Systems Engineering (WISE 2019): Workshop, Demo and Tutorial, 19-22 Jan 2020, Hong Kong, China.


Abstract

Evolutionary behavior of Online Social Networks (OSNs) has not been well understood in many different aspects. Although there have been many developments around social applications like recommendation, prediction, detection and identification which take advantage of past observations of structural patterns, they lack the necessary representative power to adequately account for the sophistication contained within relationships between actors of a social network in real life. In this demo, we extend the innovative developments of SLIND [17] (Stable LINk Detection) to include a novel generative adversarial architecture and the Relational Turbulence Model (RTM) [15] using relational features extracted from real-time twitter streaming data. Test results show that SLIND + is capable of detecting relational turbulence profiles learned from prior feature evolutionary patterns in the social data stream. Representing turbulence profiles as a pivotal set of relational features improves detection accuracy and performance of well-known application approaches in this area of research.


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Item Type: Conference or Workshop Item (Commonwealth Reporting Category E) (Paper)
Refereed: Yes
Item Status: Live Archive
Additional Information: c. Springer Nature Singapore Pte Ltd. 2020. WISE 2019 was postponed until January 2020 because of the problems in Hong Kong. Permanent restricted access to Published version in accordance with the copyright policy of the publisher.
Faculty/School / Institute/Centre: Current - Faculty of Health, Engineering and Sciences - School of Sciences (6 Sept 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: 02 Jun 2020 05:49
Last Modified: 05 Jun 2020 05:20
Uncontrolled Keywords: adversarial learning, fractal neural network, relational turbulence model
Fields of Research (2008): 08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080109 Pattern Recognition and Data Mining
Identification Number or DOI: 10.1007/978-981-15-3281-8_8
URI: http://eprints.usq.edu.au/id/eprint/38129

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