Detecting relational states in online social networks
Paper
Paper/Presentation Title | Detecting relational states in online social networks |
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Presentation Type | Paper |
Authors | Zhang, Ji (Author), Tan, Leonard (Author), Tao, Xiaohui (Author), Pham, Thuan (Author), Zhu, Xiaodong (Author), Li, Hongzhou (Author) and Chang, Liang (Author) |
Journal or Proceedings Title | Proceedings of the 5th International Conference on Behavioral, Economic, and Socio-Cultural Computing (BESC 2018) |
Number of Pages | 6 |
Year | 2018 |
Place of Publication | United States |
ISBN | 9781728102078 |
Digital Object Identifier (DOI) | https://doi.org/10.1109/BESC.2018.8697237 |
Web Address (URL) of Paper | https://ieeexplore.ieee.org/document/8697237 |
Web Address (URL) of Conference Proceedings | https://ieeexplore.ieee.org/xpl/conhome/8683941/proceeding |
Conference/Event | 5th International Conference on Behavioral, Economic, and Socio-Cultural Computing (BESC 2018) |
Event Details | 5th International Conference on Behavioral, Economic, and Socio-Cultural Computing (BESC 2018) Parent International Conference on Behavioral, Economic and Socio-cultural Computing (BESC) Event Date 12 to end of 14 Nov 2018 Event Location Kaohsiung, Taiwan |
Abstract | The state of relationships between actors ion the internet is constantly changing and fluctuating to a social system of constant shocks. Link prediction, community detection, recommendation systems were built from around this fundamentally unstable system. Stable relational states-which hold important and latent deterministic knowledge have often been overlooked in this regard. In this paper, we propose a novel method of quantifying and detecting stability in the relationship between a given pair of actors. Our main algorithm (MVVA) establishes relational stability from a multivariate, autoregressive link feature dynamics perspective. Under our experimental design, we provide another built-in module based on the Hamiltonian Monte Carlo technique to provide a comprehensive cross-validation on the performance and accuracy of our proposed MVVA model. |
Keywords | social networking |
Contains Sensitive Content | Does not contain sensitive content |
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 |
School of Management and Enterprise | |
Nanjing University of Information Science and Technology, China | |
Guilin University of Electronic Technology, China | |
Institution of Origin | University of Southern Queensland |
https://research.usq.edu.au/item/q59z9/detecting-relational-states-in-online-social-networks
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