Social cities: Quality assessment of road infrastructures using a network motif approach

Motivated by the constantly growing interest and real-world applicability shown in complex networks, we model and analyze the network formed by road networks in cities from an innovative perspective. Inspired by similar approaches of comparing networks, we create a methodology that proposes the assessment of city road networks based on their motif distributions. To the best of our knowledge, we are the first to fully interpret the roads infrastructure by using network motifs. Based on the similarity of the motif distributions, we choose six diverse cities, create a similarity graph, and discuss the urban influences one has on each other. Through our analysis, we coin the title of Social City to any city which meets particular criteria in terms of optimal roads distribution.

[1]  S. Shen-Orr,et al.  Network motifs: simple building blocks of complex networks. , 2002, Science.

[2]  Guanrong Chen,et al.  Complex networks: small-world, scale-free and beyond , 2003 .

[3]  Bin Jiang,et al.  Topological Structure of Urban Street Networks from the Perspective of Degree Correlations , 2013, ArXiv.

[4]  S. Strogatz.  Exploring complex networks , 2001, Nature.

[5]  Mircea Vladutiu,et al.  Network Fidelity: A Metric to Quantify the Similarity and Realism of Complex Networks , 2013, 2013 International Conference on Cloud and Green Computing.

[6]  H E Stanley,et al.  Classes of small-world networks. , 2000, Proceedings of the National Academy of Sciences of the United States of America.

[7]  Vincent Labatut,et al.  Classification of Complex Networks Based on Topological Properties , 2014, 2013 International Conference on Cloud and Green Computing.

[8]  J. Kleinberg,et al.  Networks, Crowds, and Markets , 2010 .

[9]  Oskar Juhlin,et al.  TRAFFIC BEHAVIOUR AS SOCIAL INTERACTION - IMPLICATIONS FOR THE DESIGN OF ARTIFICIAL DRIVERS , 1999 .

[10]  Wei Li,et al.  Comparing Networks from a Data Analysis Perspective , 2009, Complex.

[11]  Sebastian Wernicke,et al.  Efficient Detection of Network Motifs , 2006, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[12]  Bin Jiang,et al.  Street hierarchies: a minority of streets account for a majority of traffic flow , 2008, Int. J. Geogr. Inf. Sci..

[13]  Young-Koo Lee,et al.  An Efficient Method for Computing Similarity Between Frequent Subgraphs , 2013, 2013 International Conference on Cloud and Green Computing.

[14]  Duncan J. Watts,et al.  Collective dynamics of ‘small-world’ networks , 1998, Nature.

[15]  Albert,et al.  Emergence of scaling in random networks , 1999, Science.

[16]  S. Wasserman,et al.  Advances in Social Network Analysis: Research in the Social and Behavioral Sciences , 1994 .

[17]  Peter Donnelly,et al.  Superfamilies of Evolved and Designed Networks , 2004 .

[18]  S. Havlin,et al.  Dimension of spatially embedded networks , 2011 .

[19]  Martin Tomko,et al.  Experiential hierarchies of streets , 2008, Comput. Environ. Urban Syst..

[20]  Vito Latora,et al.  The network analysis of urban streets: A dual approach , 2006 .

[21]  Stanley Wasserman,et al.  Social Network Analysis: Methods and Applications , 1994, Structural analysis in the social sciences.

[22]  Z N Oltvai,et al.  Evolutionary conservation of motif constituents in the yeast protein interaction network , 2003, Nature Genetics.

[23]  A Masoudi-Nejad,et al.  Building blocks of biological networks: a review on major network motif discovery algorithms. , 2012, IET systems biology.

[24]  P. Trigo,et al.  Comparing Complex Networks: An Application to Emergency Managers' Mental Models , 2012, 2012 Third Brazilian Workshop on Social Simulation.

[25]  U. Alon.  Network motifs: theory and experimental approaches , 2007, Nature Reviews Genetics.

[26]  S. Havlin,et al.  Self-similarity of complex networks , 2005, Nature.