  Complex networks are a powerful modeling tool, allowing the study of
countless real-world systems. They have been used in very different domains
such as computer science, biology, sociology, management, etc. Authors have
been trying to characterize them using various measures such as degree
distribution, transitivity or average distance. Their goal is to detect certain
properties such as the small-world or scale-free properties. Previous works
have shown some of these properties are present in many different systems,
while others are characteristic of certain types of systems only. However, each
one of these studies generally focuses on a very small number of topological
measures and networks. In this work, we aim at using a more systematic
approach. We first constitute a dataset of 152 publicly available networks,
spanning over 7 different domains. We then process 14 different topological
measures to characterize them in the most possible complete way. Finally, we
apply standard data mining tools to analyze these data. A cluster analysis
reveals it is possible to obtain two significantly distinct clusters of
networks, corresponding roughly to a bisection of the domains modeled by the
networks. On these data, the most discriminant measures are density,
modularity, average degree and transitivity, and at a lesser extent, closeness
and edgebetweenness centralities.Abstract--Complex networks are a powerful
modeling tool, allowing the study of countless real-world systems. They have
been used in very different domains such as computer science, biology,
sociology, management, etc. Authors have been trying to characterize them using
various measures such as degree distribution, transitivity or average distance.
Their goal is to detect certain properties such as the small-world or
scale-free properties. Previous works have shown some of these properties are
present in many different systems, while others are characteristic of certain
types of systems only. However, each one of these studies generally focuses on
a very small number of topological measures and networks. In this work, we aim
at using a more systematic approach. We first constitute a dataset of 152
publicly available networks, spanning over 7 different domains. We then process
14 different topological measures to characterize them in the most possible
complete way. Finally, we apply standard data mining tools to analyze these
data. A cluster analysis reveals it is possible to obtain two significantly
distinct clusters of networks, corresponding roughly to a bisection of the
domains modeled by the networks. On these data, the most discriminant measures
are density, modularity, average degree and transitivity, and at a lesser
extent, closeness and edgebetweenness centralities.
