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• W2940927634 abstract "The state of relationships between actors ion the internet is constantly changing and fluctuating to a social system of constant shocks. Link prediction, community detection, recommendation systems were built from around this fundamentally unstable system. Stable relational states - which hold important and latent deterministic knowledge have often been overlooked in this regard. In this paper, we propose a novel method of quantifying and detecting stability in the relationship between a given pair of actors. Our main algorithm (MVVA) establishes relational stability from a multivariate, autoregressive link feature dynamics perspective. Under our experimental design, we provide another built-in module based on the Hamiltonian Monte Carlo technique to provide a comprehensive cross-validation on the performance and accuracy of our proposed MVVA model." @default.
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• W2940927634 date "2018-11-01" @default.
• W2940927634 modified "2023-10-16" @default.
• W2940927634 title "Detecting Relational States in Online Social Networks" @default.
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• W2940927634 doi "https://doi.org/10.1109/besc.2018.8697237" @default.
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