SLIND+: stable LINk detection
Paper
Paper/Presentation Title | SLIND+: stable LINk detection |
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Presentation Type | Paper |
Authors | Zhang, Ji (Author), Tan, Leonard (Author), Tao, Xiaohui (Author), Li, Hongzhou (Author), Chen, Fulong (Animator) and Luo, Yonglong (Author) |
Editors | Hou U, Leong, Yang, Jian, Cai, Yi, Karlapalem, Kamalakar, Liu, An and Huang, Xin |
Journal or Proceedings Title | Communications in Computer and Information Science, v. 1155 |
Journal Citation | 1155, pp. 73-80 |
Number of Pages | 8 |
Year | 2020 |
Place of Publication | Singapore |
ISBN | 9789811532801 |
9789811532818 | |
Digital Object Identifier (DOI) | https://doi.org/10.1007/978-981-15-3281-8_8 |
Web Address (URL) of Paper | https://link.springer.com/chapter/10.1007/978-981-15-3281-8_8 |
Conference/Event | 20th International Conference on Web Information Systems Engineering (WISE 2019): Workshop, Demo and Tutorial |
Event Details | 20th International Conference on Web Information Systems Engineering (WISE 2019): Workshop, Demo and Tutorial Event Date 19 to end of 22 Jan 2020 Event Location 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. |
Keywords | adversarial learning, fractal neural network, relational turbulence model |
ANZSRC Field of Research 2020 | 469999. Other information and computing sciences not elsewhere classified |
Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
Byline Affiliations | School of Sciences |
Guilin University of Electronic Technology, China | |
Anhui Normal University, China | |
Institution of Origin | University of Southern Queensland |
https://research.usq.edu.au/item/q59z1/slind-stable-link-detection
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