Discovering Relational Intelligence in Online Social Networks

Paper


Tan, Leonard, Pham, Thuan, Ho, Hang Kei and Kok, Tan Seng. 2020. "Discovering Relational Intelligence in Online Social Networks." 31st International Conference on Database and Expert Systems Applications (DEXA 2020). Bratislava, Slovakia 14 - 17 Sep 2020 Switzerland. Springer. https://doi.org/10.1007/978-3-030-59003-1_22
Paper/Presentation Title

Discovering Relational Intelligence in Online Social Networks

Presentation TypePaper
AuthorsTan, Leonard, Pham, Thuan, Ho, Hang Kei and Kok, Tan Seng
Journal or Proceedings TitleLecture Notes in Computer Science (Book series)
Journal Citation12391 LNCS, pp. 339-353
Number of Pages15
Year2020
PublisherSpringer
Place of PublicationSwitzerland
ISSN1611-3349
0302-9743
ISBN9783030590024
Digital Object Identifier (DOI)https://doi.org/10.1007/978-3-030-59003-1_22
Web Address (URL) of Paperhttps://link.springer.com/chapter/10.1007/978-3-030-59003-1_22
Web Address (URL) of Conference Proceedingshttps://link.springer.com/book/10.1007/978-3-030-59003-1
Conference/Event31st 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.

KeywordsDeep learning; Relational turbulence; Social recognition
Contains Sensitive ContentDoes not contain sensitive content
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Byline AffiliationsSchool of Sciences
University of Helsinki, Finland
Applipro Services, Singapore
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