Partial map of the Internet based on data from November 24 rd 2003, taken from http://opte.org/maps (accessed on February 5 th 2020). Each line is drawn between two nodes, representing two IP addresses. The length of the line is indicative of the delay between the two connected nodes. Lines are colour-coded based on Class A allocation of IP space (Asia Pacific -Red Europe/Middle East/Central Asia/Africa -Green North America -Blue Latin American and Caribbean -Yellow RFC1918 IP Addresses -Cyan Unknown -White).

Partial map of the Internet based on data from November 24 rd 2003, taken from http://opte.org/maps (accessed on February 5 th 2020). Each line is drawn between two nodes, representing two IP addresses. The length of the line is indicative of the delay between the two connected nodes. Lines are colour-coded based on Class A allocation of IP space (Asia Pacific -Red Europe/Middle East/Central Asia/Africa -Green North America -Blue Latin American and Caribbean -Yellow RFC1918 IP Addresses -Cyan Unknown -White).

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Network theory provides tools which are particularly appropriate for assessing the complex interdependencies that characterise our modern connected world. This article presents an introduction to network theory, in a way that doesn't require a strong mathematical background. We explore how network theory unveils commonalities in the interdependency...

Contexts in source publication

Context 1
... us move from "natural", organic networks, to cyber-networks, which are numerous as well. A remarkable example of a digital network is the Internet (see Fig. 2 for visualisation). In this network, nodes are physical computers, linked by cables. The large-scale topology of the internet is complex, as it is a self-organizing system whose properties cannot be traced back to any global blueprint or chart [24, Chapter 3]. The World Wide Web is a virtual network of information, living on the ...
Context 2
... place vertices anywhere, provided that one connects vertices faithfully. However, one might quickly find crossing edges, and eventually the visualisation can easily become confusing or even misleading. Large network visualisations, which sometimes don't convey much information, are sometimes called "spaghetti monsters" (the reader can decide if Fig. 2 falls into this category or not). These monsters can still be analysed using other techniques, which are discussed ...

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