On link stability detection for online social networks
Paper
Paper/Presentation Title | On link stability detection for online social networks |
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Presentation Type | Paper |
Authors | Zhang, Ji (Author), Tan, Leonard (Author), Tao, Xiaohui (Author), Lin, Jerry Chun-Wei (Author), Li, Hongzhou (Author) and Chang, Liang (Author) |
Editors | Hartmann, Sven, Ma, Hui, Hameurlain, Abdelkader, Pernul, Gunther and Wagner, Roland R. |
Journal or Proceedings Title | Lecture Notes in Computer Science (Book series) |
Journal Citation | 11029, pp. 320-335 |
Number of Pages | 16 |
Year | 2018 |
Publisher | Springer |
Place of Publication | Switzerland |
ISSN | 1611-3349 |
0302-9743 | |
ISBN | 9783319988085 |
9783319988092 | |
Digital Object Identifier (DOI) | https://doi.org/10.1007/978-3-319-98809-2_20 |
Web Address (URL) of Paper | https://link.springer.com/chapter/10.1007/978-3-319-98809-2_20 |
Conference/Event | 29th International Conference on Database and Expert Systems Applications (DEXA 2018) |
Event Details | 29th International Conference on Database and Expert Systems Applications (DEXA 2018) Event Date 03 to end of 06 Sep 2018 Event Location Regensburg, Germany |
Abstract | 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. |
Keywords | link stability, graph theory, online social networks, Hamiltonian Monte Carlo (HMC) |
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 Agricultural, Computational and Environmental Sciences |
Harbin Institute of Technology, China | |
Guilin University of Electronic Technology, China | |
Institution of Origin | University of Southern Queensland |
https://research.usq.edu.au/item/q59z6/on-link-stability-detection-for-online-social-networks
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