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- W4285197822 abstract "AbstractEven though Social Network Analysis is quite helpful in studying the structural properties of interconnected systems, real-world networks reveal much more hidden characteristics from interacting domain-specific features. In this study, we designed an Agent-based Vector-label PRopagation Algorithm (AVPRA), which captures both structural properties and domain-specific features of a given network by assigning vectors of features to constituting agents. Experimental analysis proves that our algorithm is accurate in revealing the structural properties of a network in an explainable fashion. Furthermore, the resulting vector-labels are suitable for downstream machine learning tasks.KeywordsVector-label propagationSocial network analysisExplainability" @default.
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- W4285197822 date "2022-01-01" @default.
- W4285197822 modified "2023-09-25" @default.
- W4285197822 title "Agent-Based Vector-Label Propagation for Explaining Social Network Structures" @default.
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- W4285197822 doi "https://doi.org/10.1007/978-3-031-07920-7_24" @default.
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