Discovering Relational Intelligence in Online Social Networks

Discovering Relational Intelligence in Online Social Networks

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.

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