Introduces a unified framework with full, partial and budgeted anonymization variants plus four heuristics that outperform baselines by retaining more edges and producing more anonymous nodes.
In: Proceedings of the 22nd International Conference on World Wide Web, pp
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A network generation model combining exponential probabilistic growth with vari-linear preferential attachment fits empirical degree distributions more accurately than traditional linear models and unifies several classical network properties.
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The anonymization problem in social networks
Introduces a unified framework with full, partial and budgeted anonymization variants plus four heuristics that outperform baselines by retaining more edges and producing more anonymous nodes.
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Universal Network Generation Model via Exponential Probabilistic Growth and Vari-linear Preferential Attachment
A network generation model combining exponential probabilistic growth with vari-linear preferential attachment fits empirical degree distributions more accurately than traditional linear models and unifies several classical network properties.