Abstract
In this chapter we introduce link prediction methods and metrics for directed graphs. We compare well known similarity metrics and their suitability for link prediction in directed social networks. We advance existing techniques and propose mining of subgraph patterns that are used to predict links in networks such as GitHub, GooglePlus, and Twitter. Our results show that the proposed metrics and techniques yield more accurate predictions when compared with metrics not accounting for the directed nature of the underlying networks.
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Notes
- 1.
The upper bound for which the Data Layer has been tested was a network consisting of approximately 5 × 107 nodes and 1. 5 × 109 edges.
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References
L. A. Adamic and E. Adar. Friends and neighbors on the web. Social Networks, 25:211–230, 2001.
L. M. Aiello, A. Barrat, R. Schifanella, C. Cattuto, B. Markines, and F. Menczer. Friendship prediction and homophily in social media. ACM Trans. Web, 6(2):9:1–9:33, June 2012.
E. M. Airoldi, D. M. Blei, S. E. Fienberg, and E. P. Xing. Mixed membership stochastic blockmodels. J. Mach. Learn. Res., 9:1981–2014, June 2008.
U. Alon. Network motifs: theory and experimental approaches. Nature Reviews Genetics, 8(6):450–461, June 2007.
L. Backstrom and J. Leskovec. Supervised random walks: predicting and recommending links in social networks. In Proceedings of the fourth ACM international conference on Web search and data mining, WSDM ’11, pages 635–644, New York, NY, USA, 2011. ACM.
V. Batagelj and A. Mrvar. A.: A subquadratic triad census algorithm for large sparse networks with small maximum degree. Social Networks, pages 237–243, 2001.
A. P. Bradley. The use of the area under the roc curve in the evaluation of machine learning algorithms. Pattern Recognition, 30:1145–1159, 1997.
M. J. Brzozowski and D. M. Romero. Who should i follow? recommending people in directed social networks. In L. A. Adamic, R. A. Baeza-Yates, and S. Counts, editors, ICWSM. The AAAI Press, 2011.
A. Clauset, C. Moore, and M. E. J. Newman. Hierarchical structure and the prediction of missing links in networks. Nature, 453(7191):98–101, May 2008.
I. Esslimani, A. Brun, and A. Boyer. Densifying a behavioral recommender system by social networks link prediction methods. Social Netw. Analys. Mining, 1(3):159–172, 2011.
Facebook. Online: www.facebook.com (last access June 2015).
GitHub. Online: www.github.com (last access June 2015).
GitHub. Online: http://developer.github.com/ (last access June 2015).
GooglePlus. Online: www.plus.google.com/ (last access June 2015).
M. Granovetter. The strength of weak ties. The American Journal of Sociology, 78(6): 1360–1380, 1973.
P. W. Holland, K. B. Laskey, and S. Leinhardt. Stochastic blockmodels: First steps. Social Networks, 5(2):109–137, June 1983.
P. W. Holland and S. Leinhardt. A method for detecting structure in sociometric data. American Journal of Sociology, 76(3):492–513, 1970.
G. Jeh and J. Widom. Simrank: a measure of structural-context similarity. In Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining, KDD ’02, pages 538–543, New York, NY, USA, 2002. ACM.
G. Jeh and J. Widom. Scaling personalized web search. In Proceedings of the 12th international conference on World Wide Web, WWW ’03, pages 271–279, New York, NY, USA, 2003. ACM.
L. Katz. A new status index derived from sociometric analysis. Psychometrika, 18(1):39–43, Mar. 1953.
H. Kwak, C. Lee, H. Park, and S. Moon. What is twitter, a social network or a news media? In Proceedings of the 19th international conference on World wide web, WWW ’10, pages 591–600, New York, NY, USA, 2010. ACM.
E. A. Leicht, P. Holme, and M. E. J. Newman. Vertex similarity in networks. Phys. Rev. E, 73:026120, Feb 2006.
J. Leskovec, D. Huttenlocher, and J. Kleinberg. Predicting positive and negative links in online social networks. In Proceedings of the 19th international conference on World wide web, WWW ’10, pages 641–650, New York, NY, USA, 2010. ACM.
D. Liben-Nowell and J. Kleinberg. The link prediction problem for social networks. In Proceedings of the twelfth international conference on Information and knowledge management, CIKM ’03, pages 556–559, New York, NY, USA, 2003. ACM.
W. Liu and L. Lu. Link prediction based on local random walk. EPL (Europhysics Letters), 89(5):58007, 2010.
L. Lu and T. Zhou. Link prediction in complex networks: A survey. Physica A: Statistical Mechanics and its Applications, 390(6):1150–1170, 2011.
J. J. McAuley and J. Leskovec. Learning to discover social circles in ego networks. In P. L. Bartlett, F. C. N. Pereira, C. J. C. Burges, L. Bottou, and K. Q. Weinberger, editors, NIPS, pages 548–556, 2012.
B. Meng, H. Ke, and T. Yi. Link prediction based on a semi-local similarity index. Chinese Physics B, 20(12):128902, 2011.
R. Milo, S. Shen-Orr, S. Itzkovitz, N. Kashtan, D. Chklovskii, and U. Alon. Network motifs: Simple building blocks of complex networks. Science, 298(5594):824–827, Oct. 2002.
L. Page, S. Brin, R. Motwani, and T. Winograd. The pagerank citation ranking: Bringing order to the web, 1999.
E. Ravasz, A. L. Somera, D. A. Mongru, Z. N. Oltvai, and A. L. Barabasi. Hierarchical organization of modularity in metabolic networks. Science, 297(5586):1551–1555, Aug. 2002.
A. Rettinger, H. Wermser, Y. Huang, and V. Tresp. Context-aware tensor decomposition for relation prediction in social networks. Social Netw. Analys. Mining, 2(4):373–385, 2012.
D. M. Romero and J. M. Kleinberg. The directed closure process in hybrid social-information networks, with an analysis of link formation on twitter. In W. W. Cohen and S. Gosling, editors, ICWSM. The AAAI Press, 2010.
G. Salton and M. J. McGill. Introduction to Modern Information Retrieval. McGraw-Hill, Inc., New York, NY, USA, 1986.
G. Sautter and K. Bhm. High-throughput crowdsourcing mechanisms for complex tasks. Social Network Analysis and Mining, pages 1–16, 2013.
D. Schall. Expertise ranking using activity and contextual link measures. Data Knowl. Eng., 71(1):92–113, 2012.
D. Schall. Service Oriented Crowdsourcing: Architecture, Protocols and Algorithms. Springer Briefs in Computer Science. Springer New York, New York, NY, USA, 2012.
D. Schall and F. Skopik. Social network mining of requester communities in crowdsourcing markets. Social Netw. Analys. Mining, 2(4):329–344, 2012.
T. A. Snijders. Transitivity and Triads. University of Oxford, 2012. Online: http://www.stats.ox.ac.uk/~snijders/Trans_Triads_ha.pdf (last access 22-Feb-2013).
T. Sørensen. A method of establishing groups of equal amplitude in plant sociology based on similarity of species and its application to analyses of the vegetation on danish commons. Biologiske Skrifter / Kongelige Danske Videnskabernes Selskab, 5(4):1–34, 1957.
Stanford. Online: http://snap.stanford.edu/data/index.html (last access January 2014).
P. Symeonidis and N. Mantas. Spectral clustering for link prediction in social networks with positive and negative links. Social Network Analysis and Mining, pages 1–15, 2013.
Twitter. Online: www.twitter.com (last access June 2015).
S. Wasserman, K. Faust, and D. Iacobucci. Social Network Analysis: Methods and Applications (Structural Analysis in the Social Sciences). Cambridge University Press, Nov. 1994.
H. C. White, S. A. Boorman, and R. L. Breiger. Social structure from multiple networks. i. blockmodels of roles and positions. The American Journal of Sociology, 81(4):730–780, 1976.
T. Zhou, L. Lu, and Y.-C. Zhang. Predicting missing links via local information. The European Physical Journal B - Condensed Matter and Complex Systems, 71(4):623–630, Oct. 2009.
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Schall, D. (2015). Link Prediction for Directed Graphs. In: Social Network-Based Recommender Systems. Springer, Cham. https://doi.org/10.1007/978-3-319-22735-1_2
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DOI: https://doi.org/10.1007/978-3-319-22735-1_2
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