Exploring maps with greedy navigators

During the last decade of network research focusing on structural and dynamical properties of networks, the role of network users has been more or less underestimated from the bird's-eye view of global perspective. In this era of global positioning system equipped smartphones, however, a user's ability to access local geometric information and find efficient pathways on networks plays a crucial role, rather than the globally optimal pathways. We present a simple greedy spatial navigation strategy as a probe to explore spatial networks. These greedy navigators use directional information in every move they take, without being trapped in a dead end based on their memory about previous routes. We suggest that the centralities measures have to be modified to incorporate the navigators' behavior, and present the intriguing effect of navigators' greediness where removing some edges may actually enhance the routing efficiency, which is reminiscent of Braess's paradox. In addition, using samples of road structures in large cities around the world, it is shown that the navigability measure we define reflects unique structural properties, which are not easy to predict from other topological characteristics. In this respect, we believe that our routing scheme significantly moves the routing problem on networks one step closer to reality, incorporating the inevitable incompleteness of navigators' information.

[1]  Michael T. Gastner,et al.  Price of anarchy in transportation networks: efficiency and optimality control. , 2007, Physical review letters.

[2]  Angelo Arleo,et al.  Spatial orientation in navigating agents: Modeling head-direction cells , 2001, Neurocomputing.

[3]  J. Stoyanov A Guide to First‐passage Processes , 2003 .

[4]  Bernhard Nebel,et al.  Spatial Cognition IV, Reasoning, Action, Interaction , 2008 .

[5]  John Scott What is social network analysis , 2010 .

[6]  Laura A. Carlson,et al.  Getting Lost in Buildings , 2010 .

[7]  Xin-She Yang,et al.  Introduction to Algorithms , 2021, Nature-Inspired Optimization Algorithms.

[8]  Patrick Thiran,et al.  Layered complex networks. , 2006, Physical review letters.

[9]  Sang Hoon Lee,et al.  Pathlength scaling in graphs with incomplete navigational information , 2011, ArXiv.

[10]  Tom A. B. Snijders,et al.  Social Network Analysis , 2011, International Encyclopedia of Statistical Science.

[11]  Marián Boguñá,et al.  Navigating ultrasmall worlds in ultrashort time. , 2008, Physical review letters.

[12]  Albert-László Barabási,et al.  Limits of Predictability in Human Mobility , 2010, Science.

[13]  Patrick Thiran,et al.  Extraction and analysis of traffic and topologies of transportation networks. , 2006, Physical review. E, Statistical, nonlinear, and soft matter physics.

[14]  R. Rosenfeld Nature , 2009, Otolaryngology--head and neck surgery : official journal of American Academy of Otolaryngology-Head and Neck Surgery.

[15]  B. Hillier,et al.  The Social Logic of Space , 1984 .

[16]  Marc Barthelemy,et al.  Spatial Networks , 2010, Encyclopedia of Social Network Analysis and Mining.

[17]  K. Goh,et al.  Universal behavior of load distribution in scale-free networks. , 2001, Physical review letters.

[18]  Christos H. Papadimitriou,et al.  On the complexity of edge traversing , 1976, J. ACM.

[19]  Sidney Redner,et al.  A guide to first-passage processes , 2001 .

[20]  Heiko Rieger,et al.  Random walks on complex networks. , 2004, Physical review letters.

[21]  Jon M. Kleinberg,et al.  Navigation in a small world , 2000, Nature.

[22]  Mary Hegarty,et al.  What determines our navigational abilities? , 2010, Trends in Cognitive Sciences.

[23]  Marián Boguñá,et al.  Navigability of Complex Networks , 2007, ArXiv.