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What kind of network are you? - Using local and global characteristics in network categorisation tasks

The amount of research done in the area of real-world networked systems is rapidly growing. Everybody knows what six degrees of separation or small-world phenomenon are. Scientists very easily give labels to the networks they analyse. If it has power law node degree distribution then it has to be scale-free network or if there is high clustering coefficient then it must be small-world network. These simplifications, although convenient, are not always very useful from the perspective of understanding phenomena existing within the network. In this paper we decided to go back to the basics and investigate whether analysis of one single measure is enough to describe a network. We analyse both local and global characteristics in order to discover the “true” nature of a network. Not only using local and/or global measures can lead to different classification of a network but we also show how significantly different interpretation can result from analysing the same data by building network models as directed/undirected and/or weighted/binary graphs.