Using Proximity and Homophily to Connect Conference Attendees in a Mobile Social Network

Nokia Research Center

Using Proximity and Homophily to ConnectConference Attendees in a Mobile Social NetworkAlvin Chin

Mobile Social Experiences Team

NRC Growth Economies Lab



Company Confidential

Outline

• Motivation and research problem• Contributions• Find & Connect @ UbiComp 2011• User behaviour analysis• Implications• Conclusions and future work

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Motivation

• Who should I meet at the conference?• Who is this person that I met? • Why should I add this person to my social network?

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Homophily

• Social selection• We connect with people who are similar to us as friends (McPherson et al, 2001)

• User similarity using people, places, things (Guy et al, 2010)

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Proximity

• Using location and human mobility for friendship (Cho et al, 2011)

• Encounters to determine who to add as friend (Aka-Aki; Quercia and Capra, 2009)

• Introduce people and infer one’s social network (Eagle and Pentland, 2005)

• Enhancing social interactions at conferences (Barrat et al, 2010)

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Drawback

• Fail to help users create and maintain their social network at the same time to bring convenience and facilities to users

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Research problem

• Determine how to use proximity and homophily to connect attendees in a conference

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Offline Encounters Influences Online Friendship

Source: Xu et al. Social Linking and Physical Proximity in a Mobile Location-based Service, 1st International Workshop on Mobile Location-based Services, In Proc. of UbiComp 2011, 2011

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Offline Improves Friend Recommendation

Source: Xu et al. Using Physical Context in a Mobile Social Networking Application for Improving Friend Recommendations, 1st International Workshop on Sensing, Networking and Computing on Smartphones, In Proc. of CPSCom 2011, 2011

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Recording offline interactions as ephemeral social networks

Online social networks (Facebook, Twitter, Weibo, Renren…)

Act

ivit

y 1

ESN 1 Act

ivit

y 3

ESN 3 Act

ivit

y n

ESN n

Offline physical activities (Conf., Meeting, Party, Shopping, Hiking…)

Act

ivit

y 2

ESN 2

…

time10



Contributions

• Create Find & Connect, a platform combining the conference program with indoor location and proximity

• Deploy Find & Connect to UbiComp 2011 conference

• Understand user behaviour in conference using social network analysis, data mining and survey techniques

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Find & Connect @ UbiComp 2011• Allow conference attendees to connect with each other during the conference based on

• their location• Their common research interests• the sessions that they have attended• the attendees that they have encountered over the course of the conference

• Common friends

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Find & Connect system

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RFID badge RFID readers

RFID positioning with LANDMARC

algorithm

Mobile deviceFind & Connect server



Find someone nearby during session

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Company Confidential15

Find who this person is and what you have in common



Add this person as contact

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See the conference program and who attended the sessions

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See notifications of who added you as contact

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User behaviour analysis

• Demographics• Feature usage • Online connections• Offline encounters

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Demographics

• Sept. 17 to 21, 2011 at Tsinghua University• Workshops, tutorials, research papers, posters, videos, demos

• 421 registered attendees, 241 used Find & Connect (57%)

• Apple device (31.34%), Google Chrome (23.85%), Android (22.12%), Firefox (9.08%), Internet Explorer (8.29%)

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Feature usage

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• Finding people nearby (11.66%)• Notices (10.30%)• Login (6.27%)• Program (4.97%)• Finding people farther away (3.29%)



Online connections: contacts graph

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Online connections: contacts

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Contacts degree distribution

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Offline is the reason why people add friends

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Contact recommendation

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• Weight vector wi :

wi = {wci, wcf, wcs,we, | wci + wcf + wcs +we = 1, 0 < wf < 1}

•Relevance vector Ri :

Ri = {Rci, Rcf, Rcs, Re}

• Relevance Rf

Jaccard similarity of that feature f between Ui and U as

Rf = | Nf (Ui ∩ U) | / |Nf (Ui U U) |

• Recommended score FRi

FRi = wi · Ri = {wci, wcf, wcs, we}·{Rci, Rcf, Rcs, Re, }T



• 15252 total, 309 of them added by 63 users = 2% of all contact recommendations converted into contact requests

• Low conversion rate probably due to few people using the recommendations feature

Contact recommendations results

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Offline connections: encounters graph

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• 12,716,349 total encounters

Offline connections: encounters

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Encounters degree distribution

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• Find & Connect can help people build connections in a conference

• People add others as friends/contacts if have physically met them

• Recommendations need to be more visible in order to be useful

• Post survey results show features were useful and user interface as average

Implications

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• Contact and encounter networks follow social influence theory of 3 degrees of separation

Cacioppo, J.T., Fowler, J.H., and Christakis, N.A. Alone in the crowd: the structure and spread of loneliness in a large social network. Journal of Personality and Social Psychology, 97, 6 (2009), 977.

• Users add others as contacts because of homophily and proximity

• encounters

• common sessions

• common friends

• People generally find Find & Connect useful somewhat easy to use

Conclusions

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• Improve user interface, users can post to online SNS and can add friends to SNS

• Study relationship between online and offline• Create model to identify groups of encounters that indicate activity-based social networks (ephemeral social networks)

Future work

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Alvin ChinNokia Research Center, [email protected]://research.nokia.com/people/alvin_chin

Facebook: Alvin Chin ([email protected])LinkedIn: [email protected]: gadgetman4uSina Weibo: http://weibo.com/2106762242 (gadgetman)Foursquare: Alvin Chin ([email protected])Google+: [email protected]

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mailto:[email protected]

http://research.nokia.com/people/alvin_chin


http://weibo.com/2106762242



Date post:	19-Oct-2014
Category:	Technology
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Using Proximity and Homophily to Connect Conference Attendees in a Mobile Social Network

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