Abstract
This chapter contributes toward introducing some learning automata-based algorithms for link prediction in social networks. Since one of the common link prediction methods for predicting hidden links use a deterministic and static graph where a snapshot of the network is analyzed to find hidden or future links, we study link prediction in social network which their structures are dynamic, online, and non-deterministic and introduce learning automata models as a powerful tools for such issues. The first learning automata approach for link prediction, which introduced in this chapter, is designed for stochastic social networks in which edge weights of graph are modeled as random variables. Another LA-based approach for link prediction, considered a weighted graph representation instead of a binary graph representation and the generalization of this approach is applied for fuzzy social networks. The link prediction in time series social networks is also introduced as well.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Adafre SF, de Rijke M (2005) Discovering missing links in Wikipedia. In: Proceedings of the third international workshop on link discovery, pp 90–97
Adamic LA, Adar E (2003) Friends and neighbors on the Web. Soc Netw 25:211–230. https://doi.org/10.1016/S0378-8733(03)00009-1
Al Hasan M, Zaki MJ (2011) Link prediction in social networks. Soc Netw Data Anal 243–275. https://doi.org/10.1007/978-1-4419-8462-3_9
Barabási A-L (1999) Emergence of scaling in random networks. Science (80-) 286:509–512. https://doi.org/10.1126/science.286.5439.509
Bastani S, Jafarabad AK, Zarandi MHF (2013) Fuzzy models for link prediction in social networks. Int J Intell Syst 28:768–786. https://doi.org/10.1002/int.21601
Bliss CA, Frank MR, Danforth CM, Dodds PS (2014) An evolutionary algorithm approach to link prediction in dynamic social networks. J Comput Sci 5:750–764. https://doi.org/10.1016/j.jocs.2014.01.003
Brunelli M, Fedrizzi M (2009) A fuzzy approach to social network analysis. In: 2009 International conference on advances in social network analysis and mining, pp 225–230
De Sá HR, Prudêncio RBC (2011) Supervised link prediction in weighted networks. In: Proceedings of the international joint conference on neural networks, pp 2281–2288
Dong Y, Tang J, Lou T et al (2013) How long will she call me? Distribution, social theories and duration prediction. In: Ecml/Pkdd’13, pp 16–31
Dunlavy DM, Kolda TG, Acar E (2011) Temporal link prediction using matrix and tensor factorizations. ACM Trans Knowl Discov Data 5:10
Elmagarmid AK, Member S (2007) Duplicate record detection: a survey. IEEE Trans Knowl Data Eng 19:1–16
Freschi V (2009) A graph-based semi-supervised algorithm for protein function prediction from interaction maps. In: Lecture notes in computer science (including subseries Lecture notes in artificial intelligence and Lecture notes in bioinformatics), pp 249–258
Gupte M, Eliassi-Rad T (2012) Measuring tie strength in implicit social networks. In: Proceedings of the 3rd annual ACM web science conference on—WebSci’12, pp 109–118
He YL, Liu JNK, Hu YX, Wang XZ (2015) OWA operator based link prediction ensemble for social network. Expert Syst Appl 42:21–50. https://doi.org/10.1016/j.eswa.2014.07.018
Huang Z, Lin DKJ (2009) The time-series link prediction problem with applications in communication surveillance. INFORMS J Comput 21:286–303. https://doi.org/10.1287/ijoc.1080.0292
Huang ZHZ, Li XLX, Chen HCH (2005) Link prediction approach to collaborative filtering. In: Proceedings of the 5th ACM/IEEE-CS joint conference on digital libraries (JCDL’05) 0–1. https://doi.org/10.1145/1065385.1065415
Jalali ZS, Rezvanian A, Meybodi MR (2016a) Social network sampling using spanning trees. Int J Mod Phys C 27:1650052. https://doi.org/10.1142/S0129183116500522
Jalali ZS, Rezvanian A, Meybodi MR (2016b) A two-phase sampling algorithm for social networks. In: Conference proceedings of 2015 2nd international conference on knowledge-based engineering and innovation, KBEI 2015. IEEE, pp 1165–1169
Katz L (1953) A new status index derived from sociometric analysis. Psychometrika 18:39–43. https://doi.org/10.1007/BF02289026
Li X, Chen H (2009) Recommendation as link prediction: a graph kernel-based machine learning approach. In: Proceedings of the 9th ACM/IEEE-CS joint conference on digital libraries, pp 213–216
Liben-Nowell D, Kleinberg J (2007) The link-prediction problem for social networks. J Am Soc Inf Sci Technol 58:1019–1031. https://doi.org/10.1002/asi
Lü L, Jin C-H, Zhou T (2009) Similarity index based on local paths for link prediction of complex networks. Phys Rev E 80:46122. https://doi.org/10.1103/PhysRevE.80.046122
Lu L, Zhou T (2009) Role of weak ties in link prediction of complex networks. dl.acm.org
Luo P, Li Y, Wu C, Zhang G (2015) Toward cost-efficient sampling methods. Int J Mod Phys C 26:1550050
Malin B, Airoldi E, Carley KM (2005) A network analysis model for disambiguation of names in lists. Comput Math Organ Theory 11:119–139. https://doi.org/10.1007/s10588-005-3940-3
McCallum M, Guillemin B (2013) Stochastic-deterministic signal modelling for the tracking of pitch in noise and speech mixtures using factorial HMMS. In: Proceedings of the annual conference of the international speech communication association INTERSPEECH, pp 3289–3293. https://doi.org/10.1109/ijcnn.2008.4634046
Moradabadi B, Meybodi MR (2016) Link prediction based on temporal similarity metrics using continuous action set learning automata. Phys A Stat Mech Appl 460:361–373. https://doi.org/10.1016/j.physa.2016.03.102
Moradabadi B, Meybodi MR (2017) A novel time series link prediction method: learning automata approach. Phys A Stat Mech Appl 482:422–432. https://doi.org/10.1016/j.physa.2017.04.019
Moradabadi B, Meybodi MR (2018a) Link prediction in weighted social networks using learning automata. Eng Appl Artif Intell 70:16–24. https://doi.org/10.1016/j.engappai.2017.12.006
Moradabadi B, Meybodi MR (2018b) Link prediction in stochastic social networks: learning automata approach. J Comput Sci 24:313–328. https://doi.org/10.1016/j.jocs.2017.08.007
Mousavian A, Rezvanian A, Meybodi MR (2014) Cellular learning automata based algorithm for solving minimum vertex cover problem. In: 2014 22nd Iranian conference on electrical engineering (ICEE). IEEE, pp 996–1000
Murata T, Moriyasu S (2007) Link prediction of social networks based on weighted proximity measures. In: Web intelligence, IEEE/WIC/ACM international conference on, pp 85–88
Murata T, Moriyasu S (2008) Link prediction based on structural properties of online social networks. In: New generation computing, pp 245–257
Nair PS (2007) Data mining through fuzzy social network analysis. Annual meeting of the North American Fuzzy Information Processing Society, pp 251–255. https://doi.org/10.1109/nafips.2007.383846
Newman MEJ (2001) Clustering and preferential attachment in growing networks. Phys Rev E 64:25102. https://doi.org/10.1103/PhysRevE.64.025102
Pan L, Zhou T, Lü L, Hu CK (2016) Predicting missing links and identifying spurious links via likelihood analysis. Sci Rep 6. https://doi.org/10.1038/srep22955
Pech R, Hao D, Pan L et al (2017) Link prediction via matrix completion. EPL 117:38002. https://doi.org/10.1209/0295-5075/117/38002
Rezvanian A, Meybodi MR (2016) Stochastic graph as a model for social networks. Comput Human Behav 64:621–640. https://doi.org/10.1016/j.chb.2016.07.032
Rossetti G, Guidotti R, Pennacchioli D et al (2015) Interaction prediction in dynamic networks exploiting community discovery. In: Proceedings of the 2015 IEEE/ACM international conference on advanced social networks analysis and mining 2015, pp 553–558. https://doi.org/10.1145/2808797.2809401
Tan F, Xia Y, Zhu B (2014) Link prediction in complex networks: a mutual information perspective. PLoS ONE 9:e107056. https://doi.org/10.1371/journal.pone.0107056
Thathachar MAL, Sastry PS (2004) Networks of learning automata, vol 49. Springer, p 6221. https://doi.org/10.1007/978-1-4419-9052-5
Wang C, Satuluri V, Parthasarathy S (2007) Local probabilistic models for link prediction. In: Seventh IEEE international conference on data mining (ICDM 2007). IEEE, pp 322–331
Wind DK, Morup M (2012) Link prediction in weighted networks. In: Present—2012 IEEE international workshop on machine learning for signal processing, pp 1–6. https://doi.org/10.1109/mlsp.2012.6349745
Xiang R, Neville J, Rogati M (2009) Modeling relationship strength in online social networks. Www 1–8. https://doi.org/10.1145/1772690.1772790
Xie Z, Dong E, Li J et al (2014) Potential links by neighbor communities. Phys A Stat Mech Appl 406:244–252. https://doi.org/10.1016/j.physa.2014.03.061
Yang L, Zhang W, Chen Y (2015) Time-series prediction based on global fuzzy measure in social networks. Front Inf Technol Electron Eng 16:805–816. https://doi.org/10.1631/FITEE.1500025
Zadeh LA (1978) Fuzzy sets as a belief for a theory of possibility. Fuzzy Sets Syst 1:3–28
Zhou T, Lü L, Zhang Y-C (2009) Predicting missing links via local information. Eur Phys J B 71:623–630. https://doi.org/10.1140/epjb/e2009-00335-8
Zhu J, Hong J, Hughes J (2002) Using Markov models for web site link prediction. In: Thirteenth ACM conference on hypertext and hypermedia, p 169
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this chapter
Cite this chapter
Rezvanian, A., Moradabadi, B., Ghavipour, M., Daliri Khomami, M.M., Meybodi, M.R. (2019). Social Link Prediction. In: Learning Automata Approach for Social Networks. Studies in Computational Intelligence, vol 820. Springer, Cham. https://doi.org/10.1007/978-3-030-10767-3_6
Download citation
DOI: https://doi.org/10.1007/978-3-030-10767-3_6
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-10766-6
Online ISBN: 978-3-030-10767-3
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)