Strategic Network Formation in a Location-Based Social Network

1. Strategic Network Formation
in a Location-Based Social Network:
A Topic Modeling Approach
Gene Moo Lee
Ph.D. Candidate
University of Texas at Austin
Joint work with Liangfei Qiu and Andrew Whinston
Workshop on Information Technology and Systems (WITS)
December 18, 2014

2. WITS 2014, Auckland, New Zealand
Social networks shape behaviours
• Social networks shape individual behaviours
• Product adoption, media consumption, etc.
• Social networks are going mobile
• Facebook 68%, Twitter 86%, Instagram 98%
• Location-based social network (LBSN)
• Sharing locations with friends
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3. WITS 2014, Auckland, New Zealand
Network formation in LBSN
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• RQ1: How mobile users form friendship?
• Structural model of strategic network formation
• RQ2: How to measure mobile user similarity?
• Novel dyadic user similarity with topic models
• RQ3: How each factor plays role empirically?
• Empirical analysis with large-scale LBSN data

4. WITS 2014, Auckland, New Zealand
Roadmap
1. Model
2. User similarity
3. Data
4. Empirical analysis
5. Conclusion and future directions
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5. WITS 2014, Auckland, New Zealand
Structural model of network formation
• Network formation procedure
1. A pair of users meet for linking opportunity
2. Each party checks the marginal utility by forming the link
3. If both parties see positive utilities, then a link is formed
• User i’s utility of forming a link with j
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Individual characteristics
Dyadic similarity

6. WITS 2014, Auckland, New Zealand
User similarity in LBSN
1. User profiles: unstructured text
2. Tweets: unstructured text
3. Geography: distance between home locations
4. Common mobility: normalized co-check-in
• Challenge: how to extract similarity from unstructured texts
• Latent Dirichlet allocation (LDA) to extract “topics”
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Business topic model
Per-word
topic
assignment
Observed
biography,
tweets
Bio/tweet
topicsPer-user
topics distribution
Topic
parameter
Proportions
parameter
K: # topics
D: # users
N: # words

8. WITS 2014, Auckland, New Zealand
LDA and user similarity
• Inputs: LBSN users’ text info (bio and tweets)
• LDA outputs:
• (1) topics in the whole corpus
• (2) topic vectors for each user
• Dyadic user similarity based on topic vectors
• Cosine similarity, Kullback–Leibler divergence
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9. WITS 2014, Auckland, New Zealand
Gowalla data
• #2 location-based social network in 2010~2011
• Acquired by Facebook in 2012
• Data
• Time: Jan 2009 ~ Jan 2012 (3 years)
• 285,306 users
• 3,101,620 spots
• 35,691,059 check-ins
• Social network snapshot in May 2011
• Tweets
• 100,946 users with Twitter
• 200 tweets from 79,979 users
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10. WITS 2014, Auckland, New Zealand
Gowalla data
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Check-in locations
Individual user’s
mobility trajectory
We are here!

11. WITS 2014, Auckland, New Zealand
Topic models from bio and tweets
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Biography
Tweet
Mobile
Family
Life
Social
Texas
Bitcoin
Music

12. WITS 2014, Auckland, New Zealand
Topic model and friendship
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Biography topic #187:
open, source, advocate, software
Tweet topic #17:
code, web, javascript

13. WITS 2014, Auckland, New Zealand
Model estimation
• User sampling by hometowns
• Utility function of each match
• Maximum Likelihood Estimation (MLE)
• Given the observed social graph G
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Formed links Unrealized links

14. WITS 2014, Auckland, New Zealand
Empirical analysis: Main results
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• Each similarity variable has expected effects
• (+): co_checkin, bio_topic_sim
• (-) : hometown_distance

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Empirical analysis: Robust check

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Counterfactual analysis
• Structural model enables counterfactual analysis
• No homophily
• 20% decrease in link formation
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17. WITS 2014, Auckland, New Zealand
Summary and future directions
1. Structural model for strategic network formation in LBSN
2. Propose TM-based user similarity measures
3. Find an evidence on homophily effect
• Directions:
• Consider network structure for linking opportunity
(meeting) sequence
• Multiple social network snapshots
• Simultaneous effects between check-ins and
friendships
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18. Thank you!
Contact Info: Gene Moo Lee
gene@cs.utexas.edu
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19. WITS 2014, Auckland, New Zealand
Social networks going mobile
• Mobile usages: Facebook 68%, Twitter 86%, Instagram 98%,
LinkedIn 26%, Snapchat 100%
• Location is an important factor in mobile social networks (LBSN)
• In this work, we study network formation in LBSN
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20. WITS 2014, Auckland, New Zealand
Literature
• Matching theory (CS, Math, Econ)
• matching in graph [MS, FoCS’04]
• Link prediction in complex networks (CS, Physics)
• social networks [LK, CIKM’03], biological networks [Yu+, Science’08]
• Empirical matching (Econ)
• kidney exchange [Roth+, QJE’04], medical interns [Roth, JPE’84]
• M&A analysis (Finance)
• 12 deals [GE, ASQ’04], geography [E+, JF’12] [KL, AEJ’13], social
networks [H+, JF’07] [C+, JF’10]
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21. WITS 2014, Auckland, New Zealand
References• M&A analysis
• M. Graebner, K. Eisenhardt, The Seller’s Side of the Story: Acquisition as Courtship and Governance as Syndicate in
Entrepreneurial Firms, Administrative Science Quarterly, 2004
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Information and Knowledge Management (CIKM), 2003.
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• M. Mucha, P. Sankowski, Maximum Matchings via Gaussian Elimination, Proc. of Foundations of Computer Science
(FOCS), 2004
• A. Roth, T. Sonmez, M Unver, Kidney Exchange, Quarterly Journal of Economics, 2004
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a. W. Kerr, Breakthrough inventions and migrating clusters of innovation, Journal of Urban Economics, 2010
2. Topic modeling
a. D. Blei, A. Ng, M. Jordan, Latent Dirichlet allocation, Journal of Machine Learning Research, 2003
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• Business
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