Discovering Relational Intelligence in Online Social Networks
Paper
Paper/Presentation Title | Discovering Relational Intelligence in Online Social Networks |
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Presentation Type | Paper |
Authors | Tan, Leonard, Pham, Thuan, Ho, Hang Kei and Kok, Tan Seng |
Journal or Proceedings Title | Lecture Notes in Computer Science (Book series) |
Journal Citation | 12391 LNCS, pp. 339-353 |
Number of Pages | 15 |
Year | 2020 |
Publisher | Springer |
Place of Publication | Switzerland |
ISSN | 1611-3349 |
0302-9743 | |
ISBN | 9783030590024 |
Digital Object Identifier (DOI) | https://doi.org/10.1007/978-3-030-59003-1_22 |
Web Address (URL) of Paper | https://link.springer.com/chapter/10.1007/978-3-030-59003-1_22 |
Web Address (URL) of Conference Proceedings | https://link.springer.com/book/10.1007/978-3-030-59003-1 |
Conference/Event | 31st International Conference on Database and Expert Systems Applications (DEXA 2020) |
Event Details | 31st International Conference on Database and Expert Systems Applications (DEXA 2020) Event Date 14 to end of 17 Sep 2020 Event Location Bratislava, Slovakia |
Abstract | Information networks are pivotal to the operational utility of key industries like medical, finance, governments, etc. However, applications in this area are not adequate in representing relationships between nodes[34]. Trending graph learning methodologies[9, 16] like Graph Convolutional Networks (GCNs)[6] lack both representational power and accuracy to perform abstract computational tasks like prediction, classification, recommendation, etc. on real-time social networks. Furthermore, most such approaches known to date rely on learning temporal adjacency matrices to describe shallow attributes[9, 16] like word co-occurance PMI[3] changes[6] and are unable to capture complex evolving entity relationships in real life for applications like event prediction, link prediction, topic tracking, etc.[34]. Importantly, such models ignore knowledge information geometry[1, 24, 32] completely, and sacrifices fidelity to speed of convergence. To address these challenges, a novel Relational Flux Turbulence (RFT) model was developed in this study - to identify relational turbulence in Online Social Networks (OSNs). Very good correlations between relational turbulence and sentiments exchanged within social transactions show promise in achieving these objectives. |
Keywords | Deep learning; Relational turbulence; Social recognition |
Contains Sensitive Content | Does not contain sensitive content |
Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
Byline Affiliations | School of Sciences |
University of Helsinki, Finland | |
Applipro Services, Singapore |
https://research.usq.edu.au/item/yy8w1/discovering-relational-intelligence-in-online-social-networks
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