Abstract

In the last 20 years network science has become an independent scientific field. We argue that by building network models network scientists are able to tame the vagueness of propositions about complex systems and networks, that is, to make these propositions precise. This makes it possible to study important vague properties such as modularity, near-decomposability, scale-freeness or being a small world. Using an epistemic model of network science, we systematically analyse the specific nature of network models and the logic behind the taming mechanism.

Highlights

  • We argue that by building network models network scientists are able to tame the vagueness of propositions about complex systems and networks, that is, to make these propositions precise

  • Using an epistemic model of network science, we systematically analyse the specific nature of network models and the logic behind the taming mechanism

  • In the last years of the twentieth century network theory turned into an independent scientific field

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Summary

Introduction

In the last years of the twentieth century network theory turned into an independent scientific field. Watts and Strogatz, approaching from nonlinear dynamics and Albert and Barabási, approaching from statistical physics came to the same conclusion: the behavior of certain complex systems, such as the human brain, the Internet or biological or molecular structures is fundamentally determined by their network properties. In the limelight of modern network science there stood some simple elegant network models that implied hidden universal laws behind the formation of real world networks. Network science did not come out of the blue. From Jacob Moreno’s sociometry to Mark Granovetter’s “strength of the weak ties” theory, networks appeared in social science in the form of social network analysis. Several crucial notions of modern network science from betweenness centrality to small world property are deeply rooted

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What is the essence of network science from a philosophical point of view?
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The nomothetist and the idiographer
The nomothetist and the network model
How can the nomothetist measure the network?
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The idiographer and the functional representation
The vague quantitative language
Vague network properties
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Properties of network sequences
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The network algorithm and the network model
Algorithms, models and the epistemology of simulation
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Modularity
Near-decomposable networks
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Non-decomposable networks
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Organized and disorganized complexity
Network models for organized complexity
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Network models for disorganized complexity
On Rathkopf’s theses about network models
The idiographer’s view of network science
The perception of the typical
The world of the small
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The problem of scale-freeness
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Findings
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