Self-Similarity of Network Data Analysis
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Self-similarity is a special feature of network data. When operating network data, the traditional time series models, for example an autoregressive moving average model (ARMA(p, q)), are not appropriate. Since it only provides a finite parameters model, but the network data usually has long-range dependent structure. Thus, a self-similar process for describing the network data structure is applied. In the following paragraphs, we mention the definitions of the self-similar processes and some properties of them. At the same time, we describe some important methods for graphing and estimating the self-similarity of network data.…mehr

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Produktbeschreibung
Self-similarity is a special feature of network data. When operating network data, the traditional time series models, for example an autoregressive moving average model (ARMA(p, q)), are not appropriate. Since it only provides a finite parameters model, but the network data usually has long-range dependent structure. Thus, a self-similar process for describing the network data structure is applied. In the following paragraphs, we mention the definitions of the self-similar processes and some properties of them. At the same time, we describe some important methods for graphing and estimating the self-similarity of network data.