SlideShare uma empresa Scribd logo
1 de 42
Baixar para ler offline
Petter Holme
Umeå University, Sungkyunkwan University,
Stockholm University, Institute for Future Studies

Sang Hoon Lee
Umeå University, Oxford University
How can we measure
navigability?


What does optimally
navigable networks look
like?
Full information
Shortest paths
3       t               4

        6
2                       5



1       8           7
    s           9
3       t               4

        6
2                       5



1       8           7
    s           9
3       t               4

        6
2                       5



1       8           7
    s           9
3       t               4

        6
2                       5



1       8           7
    s           9
3       t               4

        6
2                       5



1       8           7
    s           9
Partial information
Greedy navigators
3       t               4

        6
2                       5



1       8           7
    s           9
3       t               4

        6
2                       5



1       8           7
    s           9
3       t               4

        6
2                       5



1       8           7
    s           9
3       t               4

        6
2                       5



1       8           7
    s           9
3       t               4

        6
2                       5



1       8           7
    s           9
3       t               4

        6
2                       5



1       8           7
    s           9
3       t               4

        6
2                       5



1       8           7
    s           9
(Greedy navigator) navigability

          Avg. distance
Rg =
     Avg. distance for greedy
     navigators
(Greedy navigator) navigability

          Avg. distance
Rg =
     Avg. distance for random
     navigators

             random navigators
             perform a random DFS
Rg = 33%   Rr = 24%
Network N M        dg     d     dr    Rg   Rr

Boston* 88 155     6.8    5.7   30.8 84% 19%
null
                   8.6    3.7   23.2 43% 16%
model
New
         125 217   8.3    6.8   44.4 82% 15%
York*
null
                   11.7   4.0   33.5 34% 12%
model
LCM      184 194   62.8 20.6 86.2 33% 24%

* from Youn, Gastner, Jeong, PRL (2008)
Navigator essentiality
0
                  –2
                  –4
1                 –6
                  –8
                ln |e|
                   –5
    2       4      –6
        3           –7
                   –8
1           0
2
                  –5

            3     –10
    4
                ln |e|
                   –5
                  –6
                   –7
Optimizing spatial network
for greedy navigators

    Fixed vertices, growing
Boston roads
MST
graph distance
Euclidean distance
Optimizing spatial network
for greedy navigators

             Fixed vertices
Optimizing spatial network
for greedy navigators

          Not fixed vertices
0.2
                                                              BA
deviation from shortest path    0      optimized              HK
                                                              WS
                       –0.2                            Karate Club
                                                        2D square
                      –0.4                                1D ring

                      –0.6
                       –0.8
                               –1
                           –1.2
                                     0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
                                      relative position f in greedy paths
0.2
deviation from shortest path                                    BA
                                0     KK                        HK
                                                                WS
                       –0.2                              Karate Club
                                                          2D square
                      –0.4                                  1D ring
                      –0.6
                       –0.8
                               –1
                           –1.2
                                     0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
                                        relative position f in GSN paths
Thank you!
SH Lee & P Holme, 2012. Exploring maps with greedy
navigators. Phys. Rev. Lett. 108:128701.

SH Lee & P Holme, 2012. A greedy-navigator
approach to navigable city plans. To appear in Eur.
J. Phys. Spec. Top.

SH Lee & P Holme, 2012. Geometric properties of
graph layouts optimized for greedy navigation.
Under review Phys. Rev. E.

Mais conteúdo relacionado

Destaque (9)

Pollard ICMA Draft
Pollard ICMA DraftPollard ICMA Draft
Pollard ICMA Draft
 
Khairiah abdulkadir d20121061507
Khairiah abdulkadir d20121061507Khairiah abdulkadir d20121061507
Khairiah abdulkadir d20121061507
 
สิ่งที่ส่งมาด้วย2
สิ่งที่ส่งมาด้วย2สิ่งที่ส่งมาด้วย2
สิ่งที่ส่งมาด้วย2
 
Jazyk pod kůží
Jazyk pod kůžíJazyk pod kůží
Jazyk pod kůží
 
Khairiahabdulkadird20121061507hns2013
Khairiahabdulkadird20121061507hns2013Khairiahabdulkadird20121061507hns2013
Khairiahabdulkadird20121061507hns2013
 
Past continuous
Past continuousPast continuous
Past continuous
 
Luan van xay_dung_he_thong_mang_lan_cho_truong_dai_hoc
Luan van xay_dung_he_thong_mang_lan_cho_truong_dai_hocLuan van xay_dung_he_thong_mang_lan_cho_truong_dai_hoc
Luan van xay_dung_he_thong_mang_lan_cho_truong_dai_hoc
 
Kkd2063 khairiah abdulkadird20121061507kepentingan sahsiah
Kkd2063 khairiah abdulkadird20121061507kepentingan sahsiahKkd2063 khairiah abdulkadird20121061507kepentingan sahsiah
Kkd2063 khairiah abdulkadird20121061507kepentingan sahsiah
 
Q3 presentation 2015 for Vattenfall
Q3 presentation 2015 for VattenfallQ3 presentation 2015 for Vattenfall
Q3 presentation 2015 for Vattenfall
 

Semelhante a Exploring spatial networks with greedy navigators (10)

Genetic diversity and effects of stripe rust QTLs in CIMMYT Germplasm
Genetic diversity and effects of stripe rust QTLs in CIMMYT GermplasmGenetic diversity and effects of stripe rust QTLs in CIMMYT Germplasm
Genetic diversity and effects of stripe rust QTLs in CIMMYT Germplasm
 
Jag Trasgo Helsinki091002
Jag Trasgo Helsinki091002Jag Trasgo Helsinki091002
Jag Trasgo Helsinki091002
 
RubyConf Argentina 2011
RubyConf Argentina 2011RubyConf Argentina 2011
RubyConf Argentina 2011
 
Essence of of critical phenomena
Essence of of critical phenomenaEssence of of critical phenomena
Essence of of critical phenomena
 
Naist2015 dec ver1
Naist2015 dec ver1Naist2015 dec ver1
Naist2015 dec ver1
 
070817gijyutusinnsasyoumei[1]
070817gijyutusinnsasyoumei[1]070817gijyutusinnsasyoumei[1]
070817gijyutusinnsasyoumei[1]
 
Lecture+12+topology+2013 (3)
Lecture+12+topology+2013 (3)Lecture+12+topology+2013 (3)
Lecture+12+topology+2013 (3)
 
Dislocation
DislocationDislocation
Dislocation
 
Location and Mapping
Location and MappingLocation and Mapping
Location and Mapping
 
community detection
community detectioncommunity detection
community detection
 

Mais de Petter Holme

Temporal Networks of Human Interaction
Temporal Networks of Human InteractionTemporal Networks of Human Interaction
Temporal Networks of Human Interaction
Petter Holme
 
From temporal to static networks, and back
From temporal to static networks, and backFrom temporal to static networks, and back
From temporal to static networks, and back
Petter Holme
 

Mais de Petter Holme (20)

Temporal network epidemiology: Subtleties and algorithms
Temporal network epidemiology: Subtleties and algorithmsTemporal network epidemiology: Subtleties and algorithms
Temporal network epidemiology: Subtleties and algorithms
 
The big science of small networks
The big science of small networksThe big science of small networks
The big science of small networks
 
Spin models on networks revisited
Spin models on networks revisitedSpin models on networks revisited
Spin models on networks revisited
 
History of social simulations
History of social simulationsHistory of social simulations
History of social simulations
 
Optimizing
 sentinel
 surveillance 
in
 static
 and 
temporal 
networks
Optimizing
 sentinel
 surveillance 
in
 static
 and 
temporal 
networksOptimizing
 sentinel
 surveillance 
in
 static
 and 
temporal 
networks
Optimizing
 sentinel
 surveillance 
in
 static
 and 
temporal 
networks
 
Important spreaders in networks: Exact results for small graphs
Important spreaders in networks: Exact results for small graphsImportant spreaders in networks: Exact results for small graphs
Important spreaders in networks: Exact results for small graphs
 
Important spreaders in networks: exact results on small graphs
Important spreaders in networks: exact results on small graphsImportant spreaders in networks: exact results on small graphs
Important spreaders in networks: exact results on small graphs
 
Netsci 2017
Netsci 2017Netsci 2017
Netsci 2017
 
Spreading processes on temporal networks
Spreading processes on temporal networksSpreading processes on temporal networks
Spreading processes on temporal networks
 
Dynamics of Internet-mediated partnership formation
Dynamics of Internet-mediated partnership formationDynamics of Internet-mediated partnership formation
Dynamics of Internet-mediated partnership formation
 
Disease spreading & control in temporal networks
Disease spreading & control in temporal networksDisease spreading & control in temporal networks
Disease spreading & control in temporal networks
 
Modeling the evolution of the AS-level Internet: Integrating aspects of traff...
Modeling the evolution of the AS-level Internet: Integrating aspects of traff...Modeling the evolution of the AS-level Internet: Integrating aspects of traff...
Modeling the evolution of the AS-level Internet: Integrating aspects of traff...
 
Emergence of collective memories
Emergence of collective memoriesEmergence of collective memories
Emergence of collective memories
 
A paradox of importance in network epidemiology
A paradox of importance in network epidemiologyA paradox of importance in network epidemiology
A paradox of importance in network epidemiology
 
How the information content of your contact pattern representation affects pr...
How the information content of your contact pattern representation affects pr...How the information content of your contact pattern representation affects pr...
How the information content of your contact pattern representation affects pr...
 
From land use to human mobility
From land use to human mobilityFrom land use to human mobility
From land use to human mobility
 
Why do metabolic networks look like they do?
Why do metabolic networks look like they do?Why do metabolic networks look like they do?
Why do metabolic networks look like they do?
 
Temporal Networks of Human Interaction
Temporal Networks of Human InteractionTemporal Networks of Human Interaction
Temporal Networks of Human Interaction
 
Modeling the fat tails of size fluctuations in organizations
Modeling the fat tails of size fluctuations in organizationsModeling the fat tails of size fluctuations in organizations
Modeling the fat tails of size fluctuations in organizations
 
From temporal to static networks, and back
From temporal to static networks, and backFrom temporal to static networks, and back
From temporal to static networks, and back
 

Último

+971581248768>> SAFE AND ORIGINAL ABORTION PILLS FOR SALE IN DUBAI AND ABUDHA...
+971581248768>> SAFE AND ORIGINAL ABORTION PILLS FOR SALE IN DUBAI AND ABUDHA...+971581248768>> SAFE AND ORIGINAL ABORTION PILLS FOR SALE IN DUBAI AND ABUDHA...
+971581248768>> SAFE AND ORIGINAL ABORTION PILLS FOR SALE IN DUBAI AND ABUDHA...
?#DUbAI#??##{{(☎️+971_581248768%)**%*]'#abortion pills for sale in dubai@
 
Architecting Cloud Native Applications
Architecting Cloud Native ApplicationsArchitecting Cloud Native Applications
Architecting Cloud Native Applications
WSO2
 
Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FME
Cloud Frontiers:  A Deep Dive into Serverless Spatial Data and FMECloud Frontiers:  A Deep Dive into Serverless Spatial Data and FME
Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FME
Safe Software
 
Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FME
Cloud Frontiers:  A Deep Dive into Serverless Spatial Data and FMECloud Frontiers:  A Deep Dive into Serverless Spatial Data and FME
Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FME
Safe Software
 

Último (20)

Rising Above_ Dubai Floods and the Fortitude of Dubai International Airport.pdf
Rising Above_ Dubai Floods and the Fortitude of Dubai International Airport.pdfRising Above_ Dubai Floods and the Fortitude of Dubai International Airport.pdf
Rising Above_ Dubai Floods and the Fortitude of Dubai International Airport.pdf
 
WSO2's API Vision: Unifying Control, Empowering Developers
WSO2's API Vision: Unifying Control, Empowering DevelopersWSO2's API Vision: Unifying Control, Empowering Developers
WSO2's API Vision: Unifying Control, Empowering Developers
 
Exploring Multimodal Embeddings with Milvus
Exploring Multimodal Embeddings with MilvusExploring Multimodal Embeddings with Milvus
Exploring Multimodal Embeddings with Milvus
 
Mcleodganj Call Girls 🥰 8617370543 Service Offer VIP Hot Model
Mcleodganj Call Girls 🥰 8617370543 Service Offer VIP Hot ModelMcleodganj Call Girls 🥰 8617370543 Service Offer VIP Hot Model
Mcleodganj Call Girls 🥰 8617370543 Service Offer VIP Hot Model
 
Boost Fertility New Invention Ups Success Rates.pdf
Boost Fertility New Invention Ups Success Rates.pdfBoost Fertility New Invention Ups Success Rates.pdf
Boost Fertility New Invention Ups Success Rates.pdf
 
+971581248768>> SAFE AND ORIGINAL ABORTION PILLS FOR SALE IN DUBAI AND ABUDHA...
+971581248768>> SAFE AND ORIGINAL ABORTION PILLS FOR SALE IN DUBAI AND ABUDHA...+971581248768>> SAFE AND ORIGINAL ABORTION PILLS FOR SALE IN DUBAI AND ABUDHA...
+971581248768>> SAFE AND ORIGINAL ABORTION PILLS FOR SALE IN DUBAI AND ABUDHA...
 
[BuildWithAI] Introduction to Gemini.pdf
[BuildWithAI] Introduction to Gemini.pdf[BuildWithAI] Introduction to Gemini.pdf
[BuildWithAI] Introduction to Gemini.pdf
 
Architecting Cloud Native Applications
Architecting Cloud Native ApplicationsArchitecting Cloud Native Applications
Architecting Cloud Native Applications
 
Web Form Automation for Bonterra Impact Management (fka Social Solutions Apri...
Web Form Automation for Bonterra Impact Management (fka Social Solutions Apri...Web Form Automation for Bonterra Impact Management (fka Social Solutions Apri...
Web Form Automation for Bonterra Impact Management (fka Social Solutions Apri...
 
"I see eyes in my soup": How Delivery Hero implemented the safety system for ...
"I see eyes in my soup": How Delivery Hero implemented the safety system for ..."I see eyes in my soup": How Delivery Hero implemented the safety system for ...
"I see eyes in my soup": How Delivery Hero implemented the safety system for ...
 
Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FME
Cloud Frontiers:  A Deep Dive into Serverless Spatial Data and FMECloud Frontiers:  A Deep Dive into Serverless Spatial Data and FME
Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FME
 
DBX First Quarter 2024 Investor Presentation
DBX First Quarter 2024 Investor PresentationDBX First Quarter 2024 Investor Presentation
DBX First Quarter 2024 Investor Presentation
 
Vector Search -An Introduction in Oracle Database 23ai.pptx
Vector Search -An Introduction in Oracle Database 23ai.pptxVector Search -An Introduction in Oracle Database 23ai.pptx
Vector Search -An Introduction in Oracle Database 23ai.pptx
 
Connector Corner: Accelerate revenue generation using UiPath API-centric busi...
Connector Corner: Accelerate revenue generation using UiPath API-centric busi...Connector Corner: Accelerate revenue generation using UiPath API-centric busi...
Connector Corner: Accelerate revenue generation using UiPath API-centric busi...
 
MINDCTI Revenue Release Quarter One 2024
MINDCTI Revenue Release Quarter One 2024MINDCTI Revenue Release Quarter One 2024
MINDCTI Revenue Release Quarter One 2024
 
TrustArc Webinar - Unlock the Power of AI-Driven Data Discovery
TrustArc Webinar - Unlock the Power of AI-Driven Data DiscoveryTrustArc Webinar - Unlock the Power of AI-Driven Data Discovery
TrustArc Webinar - Unlock the Power of AI-Driven Data Discovery
 
Introduction to Multilingual Retrieval Augmented Generation (RAG)
Introduction to Multilingual Retrieval Augmented Generation (RAG)Introduction to Multilingual Retrieval Augmented Generation (RAG)
Introduction to Multilingual Retrieval Augmented Generation (RAG)
 
Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FME
Cloud Frontiers:  A Deep Dive into Serverless Spatial Data and FMECloud Frontiers:  A Deep Dive into Serverless Spatial Data and FME
Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FME
 
Strategies for Landing an Oracle DBA Job as a Fresher
Strategies for Landing an Oracle DBA Job as a FresherStrategies for Landing an Oracle DBA Job as a Fresher
Strategies for Landing an Oracle DBA Job as a Fresher
 
MS Copilot expands with MS Graph connectors
MS Copilot expands with MS Graph connectorsMS Copilot expands with MS Graph connectors
MS Copilot expands with MS Graph connectors
 

Exploring spatial networks with greedy navigators

  • 1. Petter Holme Umeå University, Sungkyunkwan University, Stockholm University, Institute for Future Studies Sang Hoon Lee Umeå University, Oxford University
  • 2.
  • 3.
  • 4. How can we measure navigability? What does optimally navigable networks look like?
  • 6. 3 t 4 6 2 5 1 8 7 s 9
  • 7. 3 t 4 6 2 5 1 8 7 s 9
  • 8. 3 t 4 6 2 5 1 8 7 s 9
  • 9. 3 t 4 6 2 5 1 8 7 s 9
  • 10. 3 t 4 6 2 5 1 8 7 s 9
  • 12. 3 t 4 6 2 5 1 8 7 s 9
  • 13. 3 t 4 6 2 5 1 8 7 s 9
  • 14. 3 t 4 6 2 5 1 8 7 s 9
  • 15. 3 t 4 6 2 5 1 8 7 s 9
  • 16. 3 t 4 6 2 5 1 8 7 s 9
  • 17. 3 t 4 6 2 5 1 8 7 s 9
  • 18. 3 t 4 6 2 5 1 8 7 s 9
  • 19. (Greedy navigator) navigability Avg. distance Rg = Avg. distance for greedy navigators
  • 20. (Greedy navigator) navigability Avg. distance Rg = Avg. distance for random navigators random navigators perform a random DFS
  • 21.
  • 22.
  • 23. Rg = 33% Rr = 24%
  • 24. Network N M dg d dr Rg Rr Boston* 88 155 6.8 5.7 30.8 84% 19% null 8.6 3.7 23.2 43% 16% model New 125 217 8.3 6.8 44.4 82% 15% York* null 11.7 4.0 33.5 34% 12% model LCM 184 194 62.8 20.6 86.2 33% 24% * from Youn, Gastner, Jeong, PRL (2008)
  • 26. 0 –2 –4 1 –6 –8 ln |e| –5 2 4 –6 3 –7 –8
  • 27. 1 0 2 –5 3 –10 4 ln |e| –5 –6 –7
  • 28. Optimizing spatial network for greedy navigators Fixed vertices, growing
  • 30. MST
  • 33. Optimizing spatial network for greedy navigators Fixed vertices
  • 34.
  • 35.
  • 36.
  • 37. Optimizing spatial network for greedy navigators Not fixed vertices
  • 38.
  • 39.
  • 40. 0.2 BA deviation from shortest path 0 optimized HK WS –0.2 Karate Club 2D square –0.4 1D ring –0.6 –0.8 –1 –1.2 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 relative position f in greedy paths
  • 41. 0.2 deviation from shortest path BA 0 KK HK WS –0.2 Karate Club 2D square –0.4 1D ring –0.6 –0.8 –1 –1.2 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 relative position f in GSN paths
  • 42. Thank you! SH Lee & P Holme, 2012. Exploring maps with greedy navigators. Phys. Rev. Lett. 108:128701. SH Lee & P Holme, 2012. A greedy-navigator approach to navigable city plans. To appear in Eur. J. Phys. Spec. Top. SH Lee & P Holme, 2012. Geometric properties of graph layouts optimized for greedy navigation. Under review Phys. Rev. E.