Topolojik özelliklerine bağlı olarak karmaşık ağ sınıflandırması

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 try 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. However, each one of these studies generally focuses on a very small number of measures and networks. In this work, we aim at using a more systematic approach. We first constitute a corpus 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. We apply standard data mining tools to study correlation between the properties and identify which ones are discriminant or non-discriminant. An ANOVA completed by Tukey?s test reveals two groups of domains can be distinguished in terms of average degree, modularity, transitivity and density. We apply cluster analysis tools to confirm these results, and find two more precisely defined clusters, in which the 7 domains are clearly separated (3 in a cluster, 4 in the other). An additional ANOVA confirms the previously mentioned measures are discriminant indeed, and additionally identifies diameter, average distance, closeness centrality, local transitivity and edgebetweenness centrality.