A Robust Method for Statistical Testing of Empirical Power-Law Distributions
| Autor: | Andrea Vian, Annalisa Barla, Davide Garbarino, Veronica Tozzo |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
web graph
Property (philosophy) Theoretical computer science Degree (graph theory) Computer science web graph analytics 010102 general mathematics 0211 other engineering and technologies Complex system 021107 urban & regional planning 02 engineering and technology Hyperlink Degree distribution 01 natural sciences Power law web statistical testing web graph analytics web graph web symbols.namesake statistical testing symbols Pareto distribution 0101 mathematics Statistical hypothesis testing |
| Zdroj: | Lecture Notes in Computer Science ISBN: 9783030484774 WAW |
| DOI: | 10.1007/978-3-030-48478-1_11 |
| Popis: | The World-Wide-Web is a complex system naturally represented by a directed network of documents (nodes) connected through hyperlinks (edges). In this work, we focus on one of the most relevant topological properties that characterize the network, i.e. being scale-free. A directed network is scale-free if its in-degree and out-degree distributions have an approximate and asymptotic power-law behavior. If we consider the Web as a whole, it presents empirical evidence of such property. On the other hand, when we restrict the study of the degree distributions to specific sub-categories of websites, there is no longer strong evidence for it. For this reason, many works questioned the almost universal ubiquity of the scale-free property. Moreover, existing statistical methods to test whether an empirical degree distribution follows a power law suffer from large sample sizes and/or noisy data. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|