  The structure of complex networks has been of interest in many scientific and
engineering disciplines over the decades. A number of studies in the field have
been focused on finding the common properties among different kinds of networks
such as heavy-tail degree distribution, small-worldness and modular structure
and they have tried to establish a theory of structural universality in complex
networks. However, there is no comprehensive study of network structure across
a diverse set of domains in order to explain the structural diversity we
observe in the real-world networks. In this paper, we study 986 real-world
networks of diverse domains ranging from ecological food webs to online social
networks along with 575 networks generated from four popular network models.
Our study utilizes a number of machine learning techniques such as random
forest and confusion matrix in order to show the relationships among network
domains in terms of network structure. Our results indicate that there are some
partitions of network categories in which networks are hard to distinguish
based purely on network structure. We have found that these partitions of
network categories tend to have similar underlying functions, constraints
and/or generative mechanisms of networks even though networks in the same
partition have different origins, e.g., biological processes, results of
engineering by human being, etc. This suggests that the origin of a network,
whether it's biological, technological or social, may not necessarily be a
decisive factor of the formation of similar network structure. Our findings
shed light on the possible direction along which we could uncover the hidden
principles for the structural diversity of complex networks.
