Paper Info
Title
Characterizing the structural diversity of complex networks across domains.
Abstract
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 itu0027s 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.
Year
Venue
Field
2017
arXiv: Social and Information Networks
Social network,Confusion matrix,Computer science,Theoretical computer science,Complex network,Degree distribution,Artificial intelligence,Generative grammar,Universality (philosophy),Random forest,Machine learning,Network model
DocType
Volume
Citations 
Journal
abs/1710.11304
4
PageRank 
References 
Authors
0.57
0
2
Name
Order
Citations
PageRank
Kansuke Ikehara140.57
Aaron Clauset22033146.18