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Ecological approaches toHuman Collective Behavior
Javier Borge-Holthoefer - @CoSIN3_UOC
Barcelona, 2016V Jornada Complexitat.cat
Preliminaries
Nestedness Modularity
spectral method (largest eigenvalue )
NODF (Paired Overlap – Decreasing Fill)
Many heuristics –EO, spectral, greedy…
Olesen, Bascompte, Dupont, Jordano. The modularity of pollination networks. PNAS 104(50) 2007
Barber’s bipartite networks Q
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“Studying mesoscale network structure goes very far beyond studying community structure” (Mason Porter)http://www.slideshare.net/masonporter/mesoscale-structures-in-networks-62116874
Antecedents
Bustos et al. The Dynamics of Nestedness Predicts the Evolution of Industrial Ecosystems. PLoS One 7(11) (2012)
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Antecedents
Garas, Rozenblat and Schweitzer. The network structure of city-firms network. http://arxiv.org/abs/1512.02859 (2015)
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Consensus emergenceCooperation and Competition
Context
Meme (hashtag) aspects
Growth and survival
Evolution and mutation
Leskovec, Backstrom, Kleinberg. Meme-tracking and theDynamics of News cycle. Proc. 15th SIGKDD, 497-506 2009
Temporal dynamics modeling
Where are the users?
User aspects
Structural properties
Bursty behavior
Information cascades
Where are the memes?Barabási. The origin of bursts and heavy tails in humandynamics. Nature 435 2005
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1 1 1 1 1
1 1 1 1
1 1 1
1 1 1
1 1
1
1 1 1
1 1
Bipartite approach
From information systems…
Typically focused on one side
Users or memes (hashtags)
… to information ecosystems
Users and memes (hashtags)
Rich collection of interactions
Bipartite representation
Users compete for visibility
Memes compete for attention
Gonçalves et al. Validation of Dunbar’snumber in Twitter conversations. PLoSOne 6(8), 2011
Weng et al. Competition among memes ina world with limited attention. Sci Rep 2,2012
(mutualistic setting?)
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Álvarez-Baños et al. The role of hidden influentials in the diffusion of online informationcascades. EPJ Data Science 2(6) 2013
Bipartite temporal approach
Alarcón et al. Year-to-year variation in the topology of a plant-pollinator interaction network. Oikos 117(12) 2008
Diaz-Castelazo et al. Changes of a mutualistic network over time. Ecology 91(3) 2010
Olesen, Bascompte, Elberling, Jordano. Temporal dynamics in a pollinator network. Ecology 89(6) 2008
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Data representation
Time-resolved data
w-day sliding window scheme
Window advances w each stepδ
For each snapshot
collect N most active users
collect their M correspondinghashtags
build an N x M binary matrix
Caveat: this implies that users andhashtags may enter/exit the systemeach time step
http://arxiv.org/abs/1501.068099
Consensus build-up: 15M
Modularity Q optimization
nestedness evaluation
Fortunato. Community detection ingraphs. Phys Rep 3 2010
Staniczenko, Kopp, Allesina. The ghost ofnestedness in ecological networks. Nat. Comm. 4 2012
Bell et al. Graphs for which the least eigenvalueis minimal. Linear Algebra Appl. 429 2008
http://arxiv.org/abs/1501.0680910
Socio-message: segreggation-to-coordination cross-over
Almost perfect anti-correlation
Socio-message is intuitive… but, why?
hashtags
users
Bastolla, et al. The architecture of mutualisticnetworks minimizes competition and increasesbiodiversity. Nature 458 2009
Growing term intraguild competition term Mutualistic term
Nestedness minimizes competition
Nestedness facilitates coexistence ofindividuals
Competitive interactions favourcompartmentalised structures
Thebault, Fontaine. Stability of ecologicalcommunities and the architecture of mutualisticand trophic networks. Science 329 2010
(…) negative interactions increase,mutualism decreases
Odum. Trends expected in stressed ecosystems.BioScience 35 1985
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Socio-message is intuitive… but, why?
modular nested
diedielivelive
dielivedielive http://arxiv.org/abs/1501.06809
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Consensus emergenceCooperation and Competition
(in the city)
Data
The problem grows…
221021,Coffee Shop,40.748939,-73.99228,New York,US
49932,Department Store,40.664266,-73.720107,New York,US
209256,Gym,40.732322,-73.985359,New York,US
15235,Home (private),40.716162,-73.88307,New York,US
198359,Office,40.755881,-73.985778,New York,US
90903,Medical Center,40.745104,-73.982484,New York,US
66378,Food Truck,40.739535,-73.990817,New York,US
82300,Miscellaneous Shop,40.833007,-74.009933,New York,US
3510,Grocery Store,40.690095,-73.955077,New York,US
23164,Coffee Shop,40.751591,-73.974121,New York,US
(460 cities)
(alternative datasets: Instagram, Flickr, credit card transactions)
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1 1 1 1 1
1 1 1 1
1 1 1
1 1 1
1 1
1
1 1 1
1 1
From urban systems…
Typically focused on one side
Citizens or services or locations
… to urban ecosystems
Citizens and services and locations
Rich collection of interactions
Multilayer-bipartite representation
Citizens compete for resources
Services compete for benefit
(mutualistic setting?)
asia
n re
stau
rant
coffe
e
colle
ge
gas stat
ion
cine
ma
Urban Ecosystem approach
What do locations compete for?
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services
use
rs
Chennai (China)
Urban Ecosystem
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Three-way nestednessand/or modularity
Urban Ecosystem: open fronts
Network growth model
Methodological issues
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servicescells
citizens
1972
1987
1992
2001
2014
Doha (Qatar)
Growth and Morphology
Urban Ecosystem: open fronts
Implications for system robustnessand resilience (to what?)
Implications for systemchanges(extinctions, invasions)
Cooperation and competition
The modular-nested dichotomy
Mono- vs. multi-centric?
Innovative vs. service oriented?
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Javier Borge-Holthoefer - @CoSIN3_UOC
Barcelona, 2016
www.jbh.cat