Here, this type of learning is called low level classification. Traditional supervised data classification considers only physical features (e.g., distance or similarity) of the input data. We also construct network taxonomies to compare the social structures of 100 Facebook networks and the growth structures produced by different types of fungi. For example we create taxonomies for similarity networks constructed from both political voting data and financial data. We also construct taxonomies within individual categories of networks, and in each case we expose non-trivial structure. ![]() After introducing the framework, we apply it to construct a taxonomy for 746 individual networks and demonstrate that our approach usefully identifies similar networks. While we use a specific method for uncovering network communities, much of the introduced framework is independent of that choice. Since the mesoscopic properties of networks are hypothesized to be important for network function, we base our comparisons on summaries of network community structures. These networks can arise from any of numerous sources: they can be empirical or synthetic, they can arise from multiple realizations of a single process, empirical or synthetic, or they can represent entirely different systems in different disciplines. ![]() ![]() Here we introduce a framework for constructing taxonomies of networks based on their structural similarities. The study of networks has grown into a substantial interdisciplinary endeavour that encompasses myriad disciplines in the natural, social, and information sciences.
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