Strategic Network Formation in a Location-Based Social Network

Strategic Network Formation

in a Location-Based Social Network:

A Topic Modeling Approach

Gene Moo LeePh.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

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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Network formation in LBSN

3

• 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


Roadmap

1. Model

2. User similarity

3. Data

4. Empirical analysis

5. Conclusion and future directions

4


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

5

Individual characteristics

Dyadic similarity


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”

6

WITS 2014, Auckland, New Zealand 7

Business topic modelPer-word

topic

assignment

Observed

biography,

tweets

Bio/tweet

topicsPer-user

topics distribution

Topic

parameter

Proportions

parameter

K: # topics

D: # users

N: # words


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

8


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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Gowalla data

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Check-in locations

Individual user’s

mobility trajectory

We are here!


Topic models from bio and tweets

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Biography

Tweet

Mobile

Family

Life

Social

Texas

Bitcoin

Music


Topic model and friendship

12

Biography topic #187:

open, source, advocate, software

Tweet topic #17:

code, web, javascript


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


Empirical analysis: Main results

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• Each similarity variable has expected effects

• (+): co_checkin, bio_topic_sim

• (-) : hometown_distance

WITS 2014, Auckland, New Zealand 15

Empirical analysis: Robust check


Counterfactual analysis

• Structural model enables counterfactual analysis

• No homophily

• 20% decrease in link formation

16


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

17

Thank you!

Contact Info: Gene Moo Lee

[email protected]

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mailto:[email protected]


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

19


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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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

• Link prediction

• D. Liben-Nowell, J. Kleinberg., The Link Prediction Problem for Social Networks. Proc. 12th International Conference on

Information and Knowledge Management (CIKM), 2003.

• H. Yu, et al., High-Quality Binary Protein Interaction Map of the Yeast Interactome Network, Science, 2008

• Matching problem

• 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

• A. E. Roth, The college admissions problem is not equivalent to the marriage problem, Journal of Economic Theory, 1985

• A. E. Roth, The evolution of the Labor Market for Medical Interns and Residents: A Case Study in Game Theory, Journal

of Political Economy, 1984

• Innovation and entrepreneurship

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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References• Random graph

• P. Erdos, A. Renyi, On random graphs, Publicationes Mathematicae, 1959

• M. Newman, The structure and function of complex networks, SIAM Reviews, 2003

• G. Robins, P. Pattison, Y. Kalish, D. Lusher, An introduction to exponential random graph models for social networks, Social

Networks, 2007

• Business

• A. Haigu, D. Yoffie, The New Patent Intermediaries: Platforms, Defensive Aggregators, and Super-Aggregators, Journal of

Economic Perspectives, 2003

• Geography

• I. Erel, R. Liao, M. Weisbach, Determinants of Cross-Border Mergers and Acquisitions, Journal of Finance, 2012

• A. Kalnins, F. Lafontaine, Too Far Away? The Effect of Distance to Headquarters on Business Establishment Performance,

American Economic Journal: Microeconomics, 2013

• Social links

• L. Cohen, A. Frazzini, C. Malloy, Sell-Side School Ties, Journal of Finance, 2010

• Y. Hochberg, A. Ljungqvist, Y. Liu, Whom You Know Matters: Venture Capital Networks and Investment Performance, Journal

of Finance, 2007

• M. Conyon, M. Muldoon, The Small World of Corporate Boards, Journal of Business Finance & Accounting, 2006

• Two-sided markets

• G. Weyl, A Price Theory of Multi-Sided Platforms, American Economic Review, 2010

• A. Haigu, Two-Sided Platforms: Product Variety and Pricing Structures, Journal of Economics & Management Strategy, 200922

Strategic Network Formation in a Location-Based Social Network

Data & Analytics

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