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
- Relational turbulence
- Social recognition
- Deep learning
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Tan, L., Pham, T., Ho, H.K., Kok, T.S. (2020). Discovering Relational Intelligence in Online Social Networks. In: Hartmann, S., Küng, J., Kotsis, G., Tjoa, A.M., Khalil, I. (eds) Database and Expert Systems Applications. DEXA 2020. Lecture Notes in Computer Science(), vol 12391. Springer, Cham. https://doi.org/10.1007/978-3-030-59003-1_22
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