Study of Geo-Social Networks, Social Cascades and Applications - - PDF document

Study of Geo-Social Networks, Social Cascades and Applications

Cecilia Mascolo

Computer Laboratory, University of Cambridge

Joint work (mainly) with: Salvatore Scellato

2

Location, location, location. And social networks.

Plethora of new services: increasingly important, excitingly new.

More and more importance.

Growing levels of popularity, millions of users and the attention of media and investors.

Information, social structure and space.

What is the effect of geography

• ver social structures

And information flows?

Distance matters.

Probability of friendship decreases with distance.

Interesting questions... and potential applications

• What’s the spatial structure
• f these online social

networks?

• Can we discriminate

between users according to their attitude towards long- range ties?

• How is information

spreading across space over social links?

• Can we design applications

exploiting location information in social networks?

Flickr: Oberazzi

Geographic structure of location-based online social networks Geographic Social Network

• Given a graph G=(N,K) and the

geographic location of the nodes:

• Place all nodes in a 2D metric

space adopting great-circle distance on the Earth.

• Assign a weight to each edge

equal to the geographic distance between the two nodes.

1,070 km 1,120 km 210 km

• How close are the neighbors of a given

node to the node itself?

• How spatially inter-connected are the

neighbors of a given node?

Geo-social measures

Node locality Geographic clustering coefficient User A User D User C User B

Geographic Properties

Geo-social Metrics Geographic spreading of information on location-based online social networks

URLs spread on Twitter and drive Web traffic.

Interaction between tweets, YouTube videos and Content Delivery Networks.

Geo Social Cascades

• We studied user locality and geo clustering but how are the geographical

properties of the users participating in an information cascade? We define two measures:

• the geodiversity is the geometric mean of the distances between all users in

the tree

• the georange is the geometric mean of the distances between each user and

the root of the tree

Geographic social cascade spreading

• We have collected more than 3

millions geo-tagged tweets containing URLs of about 1 million YouTube videos.

• Around 10% of social cascade

steps cover less than 1 km, with more than 30% shorter than 1,000 km.

• the final properties of a

cascade can be estimated even from the very first users involved in the initial stages.

Geosocially inspired system design

• Location information extracted

from URL-based social cascades is used to improve cache replacement strategies for multimedia files in a CDN.

• Geographic locality of online

social interactions can be exploited to do pre-fetching of Web content, caching of normal HTTP traffic, datacenter design and storage partitioning.

Location Country Servers Location Country Servers Washington USA 552 Frankfurt Germany 314 Los Angeles USA 523 London UK 300 New York USA 438 Amsterdam Netherlands 199 Chicago USA 374 Tokyo Japan 126 San Jose USA 372 Toronto Canada 121 Dallas USA 195 Paris France 120 Seattle USA 151 Hong Kong Hong Kong 83 Atlanta USA 111 Changi Singapore 53 Miami USA 111 Sydney Australia 1 Phoenix USA 3

Results of our study

Performance gain (geocascade)

• Effect of power-law popularity: even

small cache sizes achieve high levels of cache hits

• Effect of cache size: the larger the

better but with a plateau (diminishing returns).

• Effect of geographic weights: both

Geosocial and Geocascade popularity weights provide performance gains.

• Effect of workload size: geographic

weights see higher performance gains with higher workloads.

Cache hits

Improving link prediction systems for location- based online social networks

The importance of Place-Friends

• Location-based user activity

has a great potential to improve link prediction systems.

• We have collected four monthly

snapshots of data containing user profiles, friends list and check-ins.

• We found that about 30% of

new links are added among “place-friends”, or users who check-in at the same places.

Design of a link prediction system

We make two observations:

• new links overwhelmingly appear

between people who share a friend

• places with lower entropy foster

more social ties among people going there Our design proposal builds on two key choices:

• reducing the prediction space by

focusing on friends-of-friends and place-friends only

• including check-in information in

the prediction system itself.

Conclusions

• We are studying social networks with location-based information
• We have investigated their structure with two new geo-social metrics which

take into account both social connections and geographic distance: node locality and geographic clustering coefficient. We have highlighted differences between purely location-based social network services and other

• nline social communities.
• We have presented two possible applications of these models
• information propagation over space on Twitter can be used to improve

planetary-scale CDNs.

• user check-ins at places provide invaluable information to be exploited in

link prediction systems for geographical online social networks.

References

• Exploiting Place Features in Link Prediction on Location-based Social
• Networks. Salvatore Scellato, Anastasios Noulas, Cecilia Mascolo. In Proceedings
• f 17th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD

2011). San Diego, USA. August 2011.

• Socio-spatial Properties of Online Location-based Social Networks

Salvatore Scellato, Anastasios Noulas, Ranaud Lambiotte and Cecilia Mascolo. In Proceedings of Fifth International AAAI Conference on Weblogs and Social Media (ICWSM 2011). Barcelona, Spain, July 2011.

• Track Globally, Deliver Locally: Improving Content Delivery Networks by

Tracking Geographic Social Cascades Salvatore Scellato, Cecilia Mascolo, Mirco Musolesi, Jon Crowcroft. In Proceedings of 20th International World Wide Web Conference (WWW 2011). Hyderabad, India. March 2011.

• Distance Matters: Geo-social Metrics for Online Social Networks

Salvatore Scellato, Cecilia Mascolo, Mirco Musolesi, Vito Latora. In Proceedings of the 3rd Workshop on Online Social Networks (WOSN2010). Co- located with USENIX2010. Boston, MA. June 2010.

Thanks! Questions?

Cecilia Mascolo

http://www.cl.cam.ac.uk/users/cm542/