By Mathew Sathyajith
How can our societies be stabilized in a drawback? Why do we take pleasure in and comprehend Shakespeare? Why are fruitflies uniform? How do omnivorous consuming conduct reduction our survival? What makes the Mona Lisa’s smile attractive? How do girls retain our social constructions intact? – may perhaps there most likely be a unmarried resolution to a majority of these questions? This publication indicates that the assertion: "weak hyperlinks stabilize complicated structures" presents the foremost to knowing every one of those fascinating puzzles, and so forth too. the writer (recipient of a number of extraordinary technological know-how verbal exchange prizes) makes use of vulnerable (low affinity, low chance) interactions as a thread to introduce an unlimited number of networks from proteins to economics and ecosystems. many of us, from Nobel Laureates to high-school scholars have helped to make the booklet comprehensible to all readers. This targeted ebook and the tips it develops may have an important impression on many, likely various, fields of study.
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Additional resources for Weak Links: The Universal Key to the Stability of Networks and Complex Systems
This model was ﬁrst developed by Kohlrausch (1854) to explain the discharge in Leiden jars. The ﬁrst network with scalefree degree distribution was reported a hundred years later by de Solla Price (1965) analyzing the citations between scientiﬁc papers. Since then scale-free networks have been reported in all areas of biology, human relations and constructs appearing in every moment of our everyday lives (Barabasi, 2003). 1 summarizes the exponents of degree distribution for a few networks. The aim of this list is not to characterize networks.
The ﬁrst network with scalefree degree distribution was reported a hundred years later by de Solla Price (1965) analyzing the citations between scientiﬁc papers. Since then scale-free networks have been reported in all areas of biology, human relations and constructs appearing in every moment of our everyday lives (Barabasi, 2003). 1 summarizes the exponents of degree distribution for a few networks. The aim of this list is not to characterize networks. This would be very inappropriate for at least two reasons.
Here each top network element, each neural cell, is a network of proteins. Proteins of a single neural cell form a bottom network in this example. 2. 2. Examples of top–bottom network pairs Top network Elements of top network Bottom network Elements of bottom network World economy Social network Cellular network Protein network Countries People Cells Proteins Social network Cellular network Protein network Atomic network People Cells Proteins Atoms other words, bottom networks are nested in their top network.