Towards efficient multilayer network data management
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Real-world multilayer networks can be very large and there can be multiple choices regarding what should be modeled as a layer. Therefore, there is a need for their effective storage and manipulation. Currently, multilayer network analysis software use different data structures and manipulation operators. We aim to categorize operators in order to assess which structures work best for certain operator classes and data features. In this work, we propose a preliminary taxonomy of layer and data manipulation operators. We also design and execute a benchmark of select software and operators to identify potential for optimization.
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