SLIND: identifying stable links in online social networks
Paper
Paper/Presentation Title | SLIND: identifying stable links in online social networks |
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Presentation Type | Paper |
Authors | Zhang, Ji (Author), Tan, Leonard (Author), Tao, Xiaohui (Author), Zheng, Xiaoyao (Author), Luo, Yonglong (Author) and Lin, Jerry Chun-Wei (Author) |
Editors | Pei, Jian, Sadiq, Shazia, Manolopoulos, Yannis and Li, Jianxin |
Journal or Proceedings Title | Database Systems for Advanced Applications |
ERA Conference ID | 42694 |
Journal Citation | 10828 (Part II), pp. 813-816 |
Number of Pages | 4 |
Year | 2018 |
Place of Publication | Switzerland |
ISBN | 9783319914572 |
9783319914589 | |
Digital Object Identifier (DOI) | https://doi.org/10.1007/978-3-319-91458-9_54 |
Web Address (URL) of Paper | https://link.springer.com/chapter/10.1007/978-3-319-91458-9_54 |
Conference/Event | 23rd International Conference on Database Systems for Advanced Applications (DASFAA 2018) |
Database Systems for Advanced Applications | |
Event Details | Database Systems for Advanced Applications DASFAA Rank A A A A A A A A A A A A A A A A A A A A A A A A A |
Event Details | 23rd International Conference on Database Systems for Advanced Applications (DASFAA 2018) Event Date 21 to end of 24 May 2018 Event Location 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. |
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 |
Anhui Normal University, China | |
Harbin Institute of Technology, China | |
Institution of Origin | University of Southern Queensland |
https://research.usq.edu.au/item/q59z3/slind-identifying-stable-links-in-online-social-networks
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