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Random models and heuristic algorithms for correlation clustering problems on signed social networks Wahid, Dewan Ferdous

Abstract

In social sciences, the signed directed networks are used to represent the mutual friendship and foe attitudes among the members of a social group. Recent studies show that different real-world properties (e.g. preferential attachment, copying etc.) can be observed in the web-based social networks. In this thesis, we study the positive/negative - in/out - degree distributions in three online signed directed social networks. We observe that all signed-directed degree distributions in the web-based social networks with multiple edges possibilities (in both directions) follow a power law with exponents in the range 2.0

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Attribution-NonCommercial-NoDerivatives 4.0 International