In:
it - Information Technology, Walter de Gruyter GmbH, Vol. 56, No. 5 ( 2014-10-28), p. 255-258
Abstract:
Complex network analysis is concerned with identifying
statistically significant patterns in large and complex networks. A complex network is an abstract model of a complex system; it
represents a well-chosen set of e ntities as nodes and one or more
types of relationships between them as edges. Methods from complex network analysis have been used to identify small molecules called miRNAs that are able to stop breast
cancer [13] , to understand possible privacy
breaches [9] , or to analyze how humans solve complex
problems [10] . As many areas in our globalized world
tend to get more interconnected, the methods from complex network analysis became more important: for complex systems with an underlying
network structure, the framework of complex network analysis provides the potential to identify central nodes, to describe deviating
substructures, and to reveal the interaction between structure and function of complex networks. Based on this potential impact of
network analysis on many fields of society, my work concentrates on understanding when to use which kind of network measure to analyze
complex networks and where their limits are, a field I call network analysis literacy . This question can easily be
generalized to data analysis literacy which can be even more
generalized to the influence of our modern IT-systems on the individual, on organizations, and on society at large, which
culminates in a new field of study called socioinformatics .
Type of Medium:
Online Resource
ISSN:
1611-2776
,
2196-7032
DOI:
10.1515/itit-2014-1054
Language:
English
Publisher:
Walter de Gruyter GmbH
Publication Date:
2014
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