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Measuring Serendipity: Connecting People, Locations
and Interests in a Mobile 3G Network
Ionut TrestianSupranamaya RanjanAleksandar KuzmanovicAntonio Nucci
Northwestern UniversityNarus Inc.
http://networks.cs.northwestern.edu http://www.narus.com
Ionut TrestianMeasuring Serendipity: Connecting People, Locations and
Interests in a Mobile 3G Network
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Social network websites among the most popular websites on the Internet
Online Social Networks
Ionut TrestianMeasuring Serendipity: Connecting People, Locations and
Interests in a Mobile 3G Network
Mobage Town
Japan based mobile social network
11 million users
Allows users to:– Send messages, chat in
communities, exchange music, read pocket novels, write blogs, play games etc.
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Ionut TrestianMeasuring Serendipity: Connecting People, Locations and
Interests in a Mobile 3G Network
Loopt
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Allows contacts to visualize one another’s location using mobile phones and share information
Available for Sprint, Verizon, At&t, T-Mobile on devices such as BlackBerry, iPhone and gPhone
Ionut TrestianMeasuring Serendipity: Connecting People, Locations and
Interests in a Mobile 3G Network
Other Location Based Services
Sharing your location with friends (BuddyBeacon –for iPhone)
Location based searches (EarthComber)
Notifications about places and events around you (LightPole)
Tagging locations (Metosphere)
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Ionut TrestianMeasuring Serendipity: Connecting People, Locations and
Interests in a Mobile 3G Network
Research Questions
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How likely are we to meet in our daily lives people who share common interests in the cyber domain?
What is the relationship between mobility properties, location, and application affiliation in the cyber domain?
3,162,818 packet data sessions generated by 281,394 clients in 1196 locations (Base Stations) across a large
metropolitan area
Ionut TrestianMeasuring Serendipity: Connecting People, Locations and
Interests in a Mobile 3G Network
Extracting Human Movement
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1. Intra-session movement
RADA Start(contains BSID)
RADA Update(contains BSID)
2. Inter-session movement
RADA Stop(contains BSID)
RADIUSServer
BaseStation 1
BaseStation 2
Note that we have only a sampled view of human movement.
How well can we do?
Ionut TrestianMeasuring Serendipity: Connecting People, Locations and
Interests in a Mobile 3G Network
Extracting Human Movement
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Despite sampled observations we still do a good job at understanding user movement.
The ordering of the curves accounts for the larger time span which can accommodate larger travel distances
Most human movement is over short distances.
Ionut TrestianMeasuring Serendipity: Connecting People, Locations and
Interests in a Mobile 3G Network
Extracting Application Interest
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http://www.singlesnet.com
Dating website
http://www.facebook.com
Social networkingwebsite
http://www.mp3.com
Music downloadwebsite
Interest Keywords
Dating dating, harmony, personals, single, match
Music song, mp3, audio, music, track, pandora
Social netw. facebook, myspace, blog
Keyword based URL mining
Ionut TrestianMeasuring Serendipity: Connecting People, Locations and
Interests in a Mobile 3G Network
Rule Definitions
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Ionut TrestianMeasuring Serendipity: Connecting People, Locations and
Interests in a Mobile 3G Network
Rule Mining
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Location A
Location B(A, B, w, δ)
Rule support:Number of people
present at A
Rule confidence:Number of people that
move from A to B
Rule confidence probability:confidence/support
Wδ
Ionut TrestianMeasuring Serendipity: Connecting People, Locations and
Interests in a Mobile 3G Network
12
Rule Statistics
Total co
nfid
ence o
f rules
Increase in number of active users at commute hours (8AM and 5PM)
Movement rules are more active during day time, also less active during weekend
Ionut TrestianMeasuring Serendipity: Connecting People, Locations and
Interests in a Mobile 3G Network
Location Rank – Application Accesses
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Music downloads – anti-correlation with mobility spanMail – correlation with mobility spanSocial netw. – dominates the medium mobility range
Ionut TrestianMeasuring Serendipity: Connecting People, Locations and
Interests in a Mobile 3G Network
Location Ranking
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All users spend most of their time in their top 3 locations
Comfort zone
3
Ionut TrestianMeasuring Serendipity: Connecting People, Locations and
Interests in a Mobile 3G Network
Location Rank – Application Accesses
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Music downloads, Dating, Trading heavily accessed in the comfort zone
Comfort zone
Social netw. News and Mail tend to be accessed outside too
Note that Dating is accessed more in the Comfort Zone
Ionut TrestianMeasuring Serendipity: Connecting People, Locations and
Interests in a Mobile 3G Network
Home vs. Work
Ionut TrestianMeasuring Serendipity: Connecting People, Locations and
Interests in a Mobile 3G Network
Hotspots
Via rule mining we detect highly active locations
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We identify 4 types of such locations
– Noon hotspots – 28 such locations• Highly active during Noon hours
– Night hotspots – 62 such locations• Highly active during night hours
– Day-office hotspots – 23 such locations• Highly active during day hours
– Evening hotspots – 8 such locations• Highly active during the evening
Ionut TrestianMeasuring Serendipity: Connecting People, Locations and
Interests in a Mobile 3G Network
Biased Application Access at Hotspots
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Applicationaccesseshotspots
Normalized user affiliation
Despite similar userbase at hotspots during the seven day interval, application accesses are highly skewed
towards certain applications.
Ionut TrestianMeasuring Serendipity: Connecting People, Locations and
Interests in a Mobile 3G Network
Application Access - Time of Day
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Applicationaccessesnon hotspot times
Applicationaccessesnon hotspots
However the bias in application access is not entirely due to an illusive “time of day” effect !
Ionut TrestianMeasuring Serendipity: Connecting People, Locations and
Interests in a Mobile 3G Network
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Regional analysis – Spectral Clustering
Using spectral clustering we:
Cluster locations as belonging to regions
Cluster users as belonging to regions
Spectral clustering doesn’t make any assumptions on the shape of the clusters(opposed to k-means)
Ionut TrestianMeasuring Serendipity: Connecting People, Locations and
Interests in a Mobile 3G Network
Clustering Results
Ionut TrestianMeasuring Serendipity: Connecting People, Locations and
Interests in a Mobile 3G Network
Regional Analysis – Research issues
Two relevant issues for location based services:
– Time independent interactions(useful for tagging services) – part of user trajectories overlap irrespective of the time of the movement
– Time dependent interactions – same location same time
Questions:– How many distinct people with the same interests do we
meet?• Strongly dependent on userbase (probability to meet people
higher in clusters with bigger userbase)
– How often do we meet people?
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Ionut TrestianMeasuring Serendipity: Connecting People, Locations and
Interests in a Mobile 3G Network
Time Independent Interactions
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Cluster 1 has a higher number of interactions per location mainly because of larger hotspot density
27/162 (Cluster 1)> 26/257 (Cluster 4) for night hotspots
Ionut TrestianMeasuring Serendipity: Connecting People, Locations and
Interests in a Mobile 3G Network
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Who Will Win the Interaction Race?Event type Mobile users
Seen in more than 20
locations
Static users(hotspot)
Spent more than 6 hours in
a Hotspot
Static users(non-hotspot)Spent more
than 6 hours in a non-hotspot
Social netw. 704 604 424
Music 828 565 319
Dating 253 188 96
Mobile users clearly win the interaction raceHowever it pays off to spend time in popular locations
Ionut TrestianMeasuring Serendipity: Connecting People, Locations and
Interests in a Mobile 3G Network
Conclusions
First study at such a large scale aimed at correlating mobility, location, and application usage
Provided new insights from user perspective, location perspective, and provider perspective that shows the enormous location based service potential
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