Utility-Based Link Recommendation for Online Social Networks

Published Online:https://doi.org/10.1287/mnsc.2016.2446

References

  • Adali S, Sisenda F, Magdon-Ismail M (2012) Actions speak as loud as words: Predicting relationships from social behavior data. Proc. 21st Internat. Conf. World Wide Web (ACM, New York), 689–698.CrossrefGoogle Scholar
  • Adamic LA, Adar E (2003) Friends and neighbors on the web. Soc. Networks 25(3):211–230.CrossrefGoogle Scholar
  • Al Hasan M, Chaoji V, Salem S, Zaki M (2006) Link prediction using supervised learning. Proc. SIAM Workshop on Link Anal., Counterterrorism Security, Society for Industrial and Applied Mathematics, Philadelphia.Google Scholar
  • Backstrom L, Leskovec J (2011) Supervised random walks: Predicting and recommending links in social networks. Proc. 4th ACM Internat. Conf. Web Search Data Mining (ACM, New York),635–644.CrossrefGoogle Scholar
  • Ballester C, Calvó-Armengol A, Zenou Y (2006) Who’s who in networks. Wanted: The key player. Econometrica 74(5):1403–1417.CrossrefGoogle Scholar
  • Barabási A, Albert R (1999) Emergence of scaling in random networks. Science 286(5439):509–512.CrossrefGoogle Scholar
  • Barabási A, Jeong H, Néda Z, Ravasz E, Schubert A, Vicsek T (2002) Evolution of the social network of scientific collaborations. Physica A: Statist. Mechanics Appl. 311(3):590–614.CrossrefGoogle Scholar
  • Benchettara N, Kanawati R, Rouveirol C (2010) Supervised machine learning applied to link prediction in bipartite social networks. Memon N, Alhajj R, eds. Proc. 2nd Internat. Conf. Adv. Soc. Network Anal. Mining (IEEE Computer Society, Washington, DC), 326–330.CrossrefGoogle Scholar
  • Bensoussan AR, Mookerjee V, Mookerjee W, Yue T (2009) Maintaining diagnostic knowledge-based systems: A control theoretic approach. Management Sci. 55(2):294–310.LinkGoogle Scholar
  • Bishop CM, Nasrabadi NM (2006) Pattern Recognition and Machine Learning (Springer, New York).Google Scholar
  • Brin S, Page L (1998) The anatomy of a large-scale hypertextual web search engine. Comput. Networks 30(1–7):107–117.Google Scholar
  • Chen J, Geyer W, Dugan C, Muller M, Guy I (2009) Make new friends, but keep the old: Recommending people on social networking sites. Greenberg S, Hudson SE, Hinckley K, Morris MR, Olsen DR Jr, eds. Proc. 27th Internat. Conf. Human Factors Comput. Systems (ACM, New York), 201–210.CrossrefGoogle Scholar
  • Crandall DJ, Backstrom L, Cosley D, Suri S, Huttenlocher D, Kleinberg J (2010) Inferring social ties from geographic coincidences. Proc. Natl. Acad. Sci. USA 107(52):22436–22441.CrossrefGoogle Scholar
  • Davenport T, Patil DJ (2012) Data scientist: The sexist job of the 21st century. Harvard Bus. Rev. 90(10):70–76.Google Scholar
  • Dempster AP, Laird NM, Rubin DB (1977) Maximum likelihood from incomplete data via the EM algorithm. J. Roy. Statist. Soc. Ser. B 39(1):1–38.Google Scholar
  • Domingos P, Richardson M (2001) Mining the network value of customers. Proc. 7th ACM SIGKDD Internat. Conf. Knowledge Discovery Data Mining (ACM, New York), 57–66.CrossrefGoogle Scholar
  • Dong Y, Tang J, Wu S, Tian J, Chawla NV, Rao J, Cao H (2012) Link prediction and recommendation across heterogeneous social networks. Zaki MJ, Siebes A, Yu JX, Goethals B, Webb G, Wu X, eds. Proc. 12th Internat. Conf. Data Mining (IEEE Computer Society, Washington, DC), 181–190.CrossrefGoogle Scholar
  • Doreian P (1989) Two regimes of network autocorrelation. Kochen M, ed. The Small World (Ablex, Norwood, NJ), 280–295.Google Scholar
  • Duda RO, Hart PE (1973) Pattern Classification and Scene Analysis (John Wiley & Sons, New York).Google Scholar
  • Ellison NB, Steinfield C, Lampe C (2007) The benefits of Facebook “friends”: Social capital and college students’ use of online social network sites. J. Comput.-Mediated Comm. 12(4):1143–1168.CrossrefGoogle Scholar
  • eMarketer (2012) Total worldwide social network ad revenues continue strong growth. (February 24), http://www.emarketer.com/Article/Total-Worldwide-Social-Network-Ad-Revenues-Continue-Strong-Growth/1008862.Google Scholar
  • Facebook Inc. (2013) Form 10-K for the fiscal year ended December 31, 2013. Accessed February 1, 2015, https://www.sec.gov/Archives/edgar/data/1326801/000132680114000007/fb-12312013x10k.htm.Google Scholar
  • Fang X, Sheng ORL, Goes P (2013a) When is the right time to refresh knowledge discovered from data? Oper. Res. 61(1):32–44.LinkGoogle Scholar
  • Fang X, Hu P, Li Z, Tsai W (2013b) Predicting adoption probabilities in social networks. Inform. Systems Res. 24(1):128–145.LinkGoogle Scholar
  • Fouss F, Pirotte A, Renders JM, Saerens M (2007) Random-walk computation of similarities between nodes of a graph with application to collaborative recommendation. IEEE Trans. Knowledge Data Engrg. 19(3):355–369.CrossrefGoogle Scholar
  • Freund JE (1961) A bivariate extension of the exponential distribution. J. Amer. Statist. Assoc. 56(296):971–977.CrossrefGoogle Scholar
  • Friedman N (1998) The Bayesian structural EM algorithm. Cooper G, Moral S, eds. Proc. 14th Conf. Uncertainty Artificial Intelligence (Morgan Kaufmann, San Francisco), 129–138.Google Scholar
  • Gong NZ, Talwalkar A, Mackey L, Huang L, Shin ECR, Stefanov E, Shi ER, Song D (2014) Joint link prediction and attribute inference using a social-attribute network. ACM Trans. Intelligent Systems Technol. (TIST) 5(2):27.Google Scholar
  • Granovetter MS (1973) The strength of weak ties. Amer. J. Sociol. 78(6):1360–1380.CrossrefGoogle Scholar
  • Granovetter M (2005) The impact of social structure on economic outcomes. J. Econom. Perspect. 19(1):33–50.CrossrefGoogle Scholar
  • Heckerman D (2008) A tutorial on learning with Bayesian networks. Holmes D, Jain L, eds. Innovations in Bayesian Networks (Springer-Verlag, Berlin), 33–82.CrossrefGoogle Scholar
  • Heider F (1958) The Psychology of Interpersonal Relations (John Wiley & Sons, New York).CrossrefGoogle Scholar
  • Hopcroft J, Lou T, Tang J (2011) Who will follow you back? Reciprocal relationship prediction. Berendt B, de Vries A, Fan W, MacDonald G, Ounis I, Ruthven I, eds. Proc. 20th ACM Internat. Conf. Inform. Knowledge Management (ACM, New York), 1137–1146.Google Scholar
  • Huberman BA, Romero DM, Wu F (2009) Social networks that matter: Twitter under the microscope. First Monday 14(1). http://firstmonday.org/ojs/index.php/fm/article/view/2317/2063.CrossrefGoogle Scholar
  • Jackson MO (2008) Social and Economic Networks (Princeton University Press, Princeton, NJ).CrossrefGoogle Scholar
  • Jackson MO, Rogers BW (2005) The economics of small worlds. J. Eur. Econom. Assoc. 3(2–3):617–627.CrossrefGoogle Scholar
  • Jackson MO, Wolinsky A (1996) A strategic model of social and economic networks. J. Econom. Theory 71(1):44–74.CrossrefGoogle Scholar
  • Jeh G, Widom J (2002) SimRank: A measure of structural-context similarity. Proc. 8th ACM SIGKDD Internat. Conf. Knowledge Discovery Data Mining (ACM, New York), 538–543.CrossrefGoogle Scholar
  • Kaplan AM, Haenlein M (2010) Users of the world, unite! The challenges and opportunities of social media. Bus. Horizons 53(1):59–68.CrossrefGoogle Scholar
  • Katz L (1953) A new status index derived from sociometric analysis. Psychometrika 18(1):39–43.CrossrefGoogle Scholar
  • Kunegis J, De Luca EW, Albayrak S (2010) The link prediction problem in bipartite networks. Hüllermeier E, Kruse R, Hoffmann F, eds. Computational Intelligence for Knowledge-Based Systems Design, Lecture Notes in Artificial Intelligence, Vol. 6178 (Springer-Verlag, Berlin), 380–389.CrossrefGoogle Scholar
  • Kuo TT, Rui Y, Huang YY, Kung PH, Lin SD (2013) Unsupervised link prediction using aggregative statistics on heterogeneous social networks. Dhillon IS, Koren Y, Ghani R, Senator TE, Bradley P, Parekh R, He J, Grossman RL, Uthurusamy R, eds. Proc. 19th ACM SIGKDD Internat. Conf. Knowledge Discovery Data Mining (ACM, New York), 775–783.CrossrefGoogle Scholar
  • Liben-Nowell D, Kleinberg J (2007) The link prediction problem for social networks. J. Amer. Soc. Inform. Sci. Tech. 58(7):1019–1031.CrossrefGoogle Scholar
  • Lichtenwalter RN, Lussier JT, Chawla NV (2010) New perspectives and methods in link prediction. Proc. 16th ACM SIGKDD Internat. Conf. Knowledge Discovery Data Mining (ACM, New York), 243–252.CrossrefGoogle Scholar
  • LinkedIn Corporation (2014) Form 10-K for the fiscal year ended December 31, 2013. Accessed February 1, 2015, https://www.sec.gov/Archives/edgar/data/1271024/000144530514000439/a20131231-10xkdocument.htm.Google Scholar
  • McLachlan G, Krishnan T (2007) The EM Algorithm and Extensions (John Wiley & Sons, Hoboken, NJ).Google Scholar
  • McPherson JM, Smith-Lovin L, Cook JM (2001) Birds of a feather: Homophily in social networks. Annual Rev. Sociol. 27:415–444.CrossrefGoogle Scholar
  • Mitchell TM (1997) Machine Learning (McGraw-Hill, New York).Google Scholar
  • Newman MEJ (2001) The structure of scientific collaboration networks. Proc. Natl. Acad. Sci. USA 98(2):404–409.CrossrefGoogle Scholar
  • Newman MEJ, Watts DJ, Strogatz SH (2002) Random graph models of social networks. Proc. Natl. Acad. Sci. USA 99(1):2566–2572.CrossrefGoogle Scholar
  • O’Madadhain J, Hutchins J, Smyth P (2005) Prediction and ranking algorithms for event-based network data. SIGKDD Explorations 7(2):23–30.CrossrefGoogle Scholar
  • Popescul A, Ungar L (2003) Statistical relational learning for link prediction. Gottlob G, Walsh T, eds. Workshop Learn. Statist. Models Relational Data Internat. Joint Conf. Artificial Intelligence (Morgan Kaufmann, San Francisco), 81–90.Google Scholar
  • Quercia D, Capra L (2009) FriendSensing: Recommending friends using mobile phones. Proc. 3rd ACM Conf. Recommender Systems (ACM, New York), 273–276.CrossrefGoogle Scholar
  • Saar-Tsechansky M, Melville P, Provost F (2009) Active feature-value acquisition. Management Sci. 55(4):664–684.LinkGoogle Scholar
  • Salton G, McGill MJ (1983) Introduction to Modern Information Retrieval (McGraw-Hill, New York).Google Scholar
  • Scellato S, Noulas A, Mascolo C (2011) Exploiting place features in link prediction on location-based social networks. Proc. 17th ACM SIGKDD Internat. Conf. Knowledge Discovery Data Mining (ACM, New York), 1046–1054.CrossrefGoogle Scholar
  • Schifanella R, Barrat A, Cattuto C, Markines B, Menczer F (2010) Folks in folksonomies: Social link prediction from shared metadata. Proc. 3rd ACM Internat. Conf. Web Search Data Mining (ACM, New York), 271–280.CrossrefGoogle Scholar
  • Shen D, Sun JT, Yang Q, Chen Z (2006) Latent friend mining from blog data. Clifton CW, Zhong N, Liu J, Wah BW, Wu X, eds. Proc. 6th IEEE Internat. Conf. Data Mining (IEEE Computer Society, Washington, DC), 552–561.CrossrefGoogle Scholar
  • Tan P-N, Steinbach M, Kumar V (2005) Introduction to Data Mining (Addison-Wesley, Boston).Google Scholar
  • Tong H, Faloutsos CC, Pan JY (2006) Fast random walk with restart and its applications. Clifton CW, Zhong N, Liu J, Wah BW, Wu X, eds. Proc. 6th IEEE Internat. Conf. Data Mining (IEEE Computer Society, Washington, DC), 613–622.CrossrefGoogle Scholar
  • Wang C, Satuluri V, Parthasarathy S (2007) Local probabilistic models for link prediction. Ramakrishnan N, Zaïane OR, Shi Y, Clifton CW, Wu X, eds. Proc. 7th IEEE Internat. Conf. Data Mining (IEEE Computer Society, Washington, DC), 322–331.CrossrefGoogle Scholar
  • Wang D, Pedreschi D, Song C, Giannotti F, Barabasi AL (2011) Human mobility, social ties, and link prediction. Proc. 17th ACM SIGKDD Internat. Conf. Knowledge Discovery Data Mining (ACM, New York), 1100–1108.CrossrefGoogle Scholar
  • Watts A (2001) A dynamic model of network formation. Games Econom. Behav. 34(2):331–341.CrossrefGoogle Scholar
  • Weiss G, Zadrozny B, Saar-Tsechansky M (2008) Special issue on utility-based data mining. Data Mining Knowledge Discovery 17(2):129–135.CrossrefGoogle Scholar
  • Yang S-H, Long B, Smola A, Sadagopan N, Zheng Z, Zha H (2011) Like like alike: Joint friendship and interest propagation in social networks. Proc. 20th ACM Internat. Conf. World Wide Web (ACM, New York), 537–546.CrossrefGoogle Scholar
  • Zhang B, Thomas A, Doreian P, Krackhardt D, Krishnan R (2013) Contrasting multiple social network autocorrelations for binary outcomes, with applications to technology adoption. ACM Trans. Management Inform. Systems 3(4): Article 18.CrossrefGoogle Scholar
  • Zhao K, Yen J, Ngamassi L, Maitland C, Tapia A (2012) Simulating inter-organizational collaboration networks: A multi-relational and event-based approach. Simulation 88(5):617–633.CrossrefGoogle Scholar
  • Zheleva E, Getoor L, Golbeck J, Kuter U (2010) Using friendship ties and family circles for link prediction. Giles L, Smith M, Yen J, Zhang H, eds. Advances in Social Network Mining and Analysis, Lecture Notes in Computer Science, Vol. 5498 (Springer-Verlag, Berlin), 97–113.CrossrefGoogle Scholar
  • Zheng Z, Pavlou P (2010) Toward a causal interpretation from observational data: A new Bayesian networks method for structural models with latent variables. Inform. System Res. 21(2):365–391.LinkGoogle Scholar
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