- Anastasios Noulas is a researcher at the Data Science Institute at Lancaster University who is moving to NYU in May. - He has authored or co-authored several papers on analyzing social networks and human mobility patterns using large datasets. - His research focuses on modeling networks formed by human movements between geographic locations (places), and how these place networks interact with social networks.

1. Anastasios Noulas
Data Science Institute
School of Computing & Communications
Lancaster University —> moving to NYU in May
Thanks to Prof. Cecilia Mascolo and Dr. Desislava Hristova
for providing the source of many of the slides.
8th Conference on Social Networks, Dubrovnik, Croatia, March 2017
Networks & Complexity in Mobile
Systems

3. Impact of social distance on link formation
S Scellato,A Noulas, C Mascolo "Exploiting place features in link prediction on location-based social networks" KDD’ 2011
2011
2012
2013
2014
2015
2016
2017

4. all potential pairs
Introducing Place-Friends
2011
2012
2013
2014
2015
2016
2017

5. Prediction space reduction
Thus, by focusing prediction efforts only on place-friends or friends-of-friends the
prediction space can be reduced by about 15 times, while still covering two-thirds of all new
links
Prediction space size Imbalance Ratio
2011
2012
2013
2014
2015
2016
2017
S Scellato,A Noulas, C Mascolo "Exploiting place features in link prediction on location-based social networks" KDD’ 2011

6. Place Entropy
2011
2012
2013
2014
2015
2016
2017
S Scellato,A Noulas, C Mascolo "Exploiting place features in link prediction on location-based social networks" KDD’ 2011

7. Place semantics and link probability
2011
2012
2013
2014
2015
2016
2017
C Brown,A Noulas, C Mascolo,V Blondel "A place-focused model for social networks in cities" SocialCom’ 2013

8. Step 1 : User to Place assignment based on place
popularity
places in the cityusers
2011
2012
2013
2014
2015
2016
2017
C Brown,A Noulas, C Mascolo,V Blondel "A place-focused model for social networks in cities" SocialCom’ 2013

9. Step 2 : Next Place assignment (mobility)
a user may go to more than
1 places;
next place is chosen with
a probability of attachment
P.
- probability P is proportional to the popularity of a place
- probability P is inversely proportional to the rank-distance
between origin and destination place.
2011
2012
2013
2014
2015
2016
2017
C Brown,A Noulas, C Mascolo,V Blondel "A place-focused model for social networks in cities" SocialCom’ 2013

10. Step 3 : Tie formation in the place!
Social Link
Creation
Probability
sociability
medium
low
0.15
0.08
0.01
Home, Food, Nightlife
Work, Shops
Travel, Parks, Uni, Museums
Triangle
Closing
Mechanism
Close Triangles with
Probability 0.15 (social)
place type
high
probability
For every pair
of users at a
place

11. Evaluation (1) : Model resembles empirical
social network
Degree Distribution
Clustering
Average Shortest
Path Length
Community
Structure

12. defining a place network
1.draw an edge between two places, for every user transition
2. aggregate movements and form a place network
main assumption: the place network has structure
e(i,j)
i j
w
A Noulas, B Shaw, R Lambiotte, C Mascolo "Topological properties and temporal dynamics of place networks in urban
environments" WWW’ 2015
2011
2012
2013
2014
2015
2016
2017

14. NewYork City
11-12PM

15. snapshot 1 snapshot 2
vs
3 months ~10 weeks
Network Dynamics

16. Apr’12
Jul’12
O
ct’12
Jan
’13
Apr’13
Jul’13
O
ct’13
Network Snapshot Sequence
0.60
0.65
0.70
0.75
0.80
0.85
0.90
0.95
1.00
NewEdgeProbability
random movement
~100K EDGES PER
MONTH
MILLIONS OF
POTENTIAL
EDGES
~25% of edges
“survive” for 2
years

17. 5 10 15 20
Edge Weight
0.0
0.2
0.4
0.6
0.8
1.0
EdgePersistenceProbability
popular edges are temporally stable
note however, there can exist opportunistic
high weight edges (events)

18. Apr’12
Jul’12
O
ct’12
Jan
’13
Apr’13
Jul’13
O
ct’13
Network Snapshot Sequence
0.00
0.05
0.10
0.15
0.20
0.25
0.30
0.35
0.40
NewNodeProbability
~90% of nodes
“survive” for 2
years

19. 100
101
102
103
104
Node Degree
10 7
10 6
10 5
10 4
10 3
10 2
10 1
100
PDF
Sao Paulo
Kuala Lumpur
New York
k 2.19
100
101
102
103
Edge Weight
10 8
10 7
10 6
10 5
10 4
10 3
10 2
10 1
100
PDF
Sao Paulo
Kuala Lumpur
New York
k 2.60
Topological properties of place networks

20. A Noulas, B Shaw, R Lambiotte, C Mascolo "Topological properties and temporal dynamics of place networks in urban
environments" WWW’ 2015
2011
2012
2013
2014
2015
2016
2017

21. triangle factors? i) human mobility + social net
ii) geographic embedding

22. It’s a small world!
---> Information spreads quickly, but also
disease!

23. place networks tend to be
dissasortative and in that
sense fundamentally different
to social networks.
M.Newman et. al PRL, 2002
Assortativity, or assortative
mixing is a preference for a
network's nodes to attach to
others that are similar in
some way [wikipedia]

24. < t t > t
snapshot 1 snapshot 2

25. human
mobility
5 10 15 20
Common Neighbors
0.0
0.1
0.2
0.3
0.4
0.5
0.6
LinkProbability
2 0 2 4 6 8 10
Logarithm (base 10)
10 4
10 3
10 2
10 1
100
LinkProbability
Distance
Popularity
network
form

26. dynamic gravity
static gravity
2 0 2 4 6 8 10
Logarithm (base 10)
10 4
10 3
10 2
10 1
100
LinkProbability
Distance
Popularity
awesome fact :
when T=1 and a_ij = 1
we fall back to the
static gravity model

27. the dynamic
gravity model
combines the
best information
signals.
overfitting is an issue,
especially in an
environment that
changes dynamically!

28. Interconnected
Networks
Bob
Ann
Joe
Tim
J.P.
The
Punter
Aromi
CUSU
Fitzwilliam
Museum
Classmates
Venues
Interconnected network models can
capture interactions between
heterogeneous entities across layers and
therefore be used to study cascading
effects.
2011
2012
2013
2014
2015
2016
2017
Hristova, Desislava, et al. "Measuring urban social diversity using interconnected geo-social networks." Proceedings of the 25th
International Conference on World Wide Web, 2016.

29. Urban
Geo-Social
Network
Model
2011
2012
2013
2014
2015
2016
2017
Hristova, Desislava, et al. "Measuring urban social diversity using interconnected geo-social networks." Proceedings of the 25th
International Conference on World Wide Web, 2016.

30. Deriving the
social
brokerage
of places
from
Foursquare
Category Bridging role Bonding role
Travel Motel B&B
Shops Mall Laundry
Residences Apartment
Building
Home
Professional Courthouse Emergency
Room
Outdoors Bridge Vineyard
Nightlife Gay Bar Strip Club
Food Dumplings Fried Chicken
Study Bookstore Classroom
Arts Art Museum Football
2011
2012
2013
2014
2015
2016
2017
Hristova, Desislava, et al. "Measuring urban social diversity using interconnected geo-social networks." Proceedings of the 25th
International Conference on World Wide Web, 2016.

31. Gentrification:
Diversity &
Deprivation?
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Barking and Dagenham
Barnet
Bexley
Brent
Bromley
Camden
City of London
Croydon
Ealing
Enfield
Greenwich
Hackney
Hammersmith and Fulham
Haringey
Harrow
Havering
Hillingdon
Hounslow
Islington
Kensington and Chelsea
Kingston upon Thames
Lambeth
Lewisham
Merton
Newham
Redbridge
Richmond upon Thames
Southwark
Sutton
Tower Hamlets
Waltham Forest
Wandsworth
Westminster
0
10
20
30
0 10 20 30
Brokerage RankIMDRank
High deprivation and high diversity in
2010 signal gentrification in 2015.
The rank of IMD vs Brokerage for 2010.
Node size indicates the percent change in IMD over the next 5 years.
Hristova, Desislava, et al. "Measuring urban social diversity using interconnected geo-social networks." Proceedings of the 25th
International Conference on World Wide Web, 2016.
2011
2012
2013
2014
2015
2016
2017
Hristova, Desislava, et al. "Measuring urban social diversity using interconnected geo-social networks." Proceedings of the 25th
International Conference on World Wide Web, 2016.

32. 2011
2012
2013
2014
2015
2016
2017
A Noulas,V Salnikov, R Lambiotte, C Mascolo
"Mining open datasets for transparency in taxi transport in metropolitan environments" EPJ Data Science 2015

36. And later on

38. Mon 12h Tue 12h Wed 12h Thu 12h Fri 12h Sat 12h Sun 12h
Hour of the Week
0
50
100
150
200
250
PriceinUSDollars

39. Radipole rd sw6 to 148 Harley st when I
get there I will streak naked through
London if my meter agrees with your £29
estimate! Will let u know , guess what I'm
here and keeping my clothes on £22 in the
real world !! Exactly the same as uber but
twice as quick !
Mark, black cab driver

42. 2011
2012
2013
2014
2015
2016
2017
A Noulas,V Salnikov, D Hristova, C Mascolo, R Lambiotte
"Developing and Deploying aTaxi Price Comparison Mobile App in the Wild: Insights and Challenges" Arxiv 2017

43. ●
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●
5.0
7.5
10.0
12.5
15.0
17.5
5.0 7.5 10.0 12.5 15.0 17.5
Price Estimate (GBP)
ActualPrice(GBP)
provider
● Black
Uber
29 journeys
Experiment
Pick Uber near Black Cab ranks
or use Hailo!
3 days: 11 am - 11 pm
> 300 km covered
Budget: 1.5K - 2K GBP
2011
2012
2013
2014
2015
2016
2017

44. Urban Complexity & Performance
4 2 0 2 4 6 8 10 12
Price Difference [GBP]
10
5
0
5
TimeDifference[mins]
Uber faster
Black Cab faster
Journey duration tie
200 0 200 400 600 800 1000 1200 1400 1600
Average Place Density
0.1
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
FractionofBlackCabWins
A Noulas,V Salnikov, D Hristova, C Mascolo, R Lambiotte
"Developing and Deploying aTaxi Price Comparison Mobile App in the Wild: Insights and Challenges" Arxiv 2017

45. Drivers:Black Cab vs Yellow vs Uber
Maguire, Eleanor A., et al. "Navigation-related structural change in the hippocampi of taxi drivers." Proceedings o
Academy of Sciences 97.8 (2000): 4398-4403.
uses his (big) brain
Does not know where
is Brooklyn!
blindly follows the GPS

46. ?
Email: A.Noulas@lancaster.ac.uk
Questions
THANK YOU!
@taslanous
contact me for slides
and Papers!