Shabnam Hassanzadeh Sharif; Shabnam Mahmazi; Nima Jafari Navimipour; Behzad Farid Aghdam

A Review on Search and Discovery Mechanisms in Social Networks

Full Text (PDF, 479KB), PP.64-73

Views: 0 Downloads: 0

Author(s)

Shabnam Hassanzadeh Sharif ¹ Shabnam Mahmazi ¹ Nima Jafari Navimipour ^1,* Behzad Farid Aghdam ¹

1. Department of Computer Engineering, East Azarbaijan Science and Research Branch, Islamic Azad University, Tabriz, Iran

* Corresponding author.

DOI: https://doi.org/10.5815/ijieeb.2013.06.08

Received: 4 Sep. 2013 / Revised: 3 Oct. 2013 / Accepted: 7 Nov. 2013 / Published: 8 Dec. 2013

Index Terms

Social network, search, discovery, peer-to-peer, distributed algorithm, scalability

Abstract

Social search is a variant of information retrieval where a document or website is considered relevant if individuals from the searcher’s social network have interacted with it. To the best of our knowledge, there is no new detailed paper which covers discovery method in social network; therefore, in this paper we surveyed searching methods in social network which have been presented so far. We classified the existing methods in four main categories: people search, job search, keyword search and web service discovery. Also we conclude the paper with some implications for future research and practice.

Cite This Paper

Shabnam Hassanzadeh Sharif, Shabnam Mahmazi, Nima Jafari Navimipour, Behzad Farid Aghdam, "A Review on Search and Discovery Mechanisms in Social Networks", IJIEEB, vol.5, no.6, pp.64-73, 2013. DOI:10.5815/ijieeb.2013.06.08

Reference

[1]Boyd, D.M. and N.B. Ellison, Social network sites:definition, history, and scholarship. Engineering Management Review, IEEE, 2010. 38(3): p. 16-31.

[2]Mark-Shane, S., Facebook as a social search engine and the implications for libraries in the twenty-first century. Emerald, 2008. 26(0): p. 540 - 556.

[3]Hughes, D.J., et al., A tale of two sites: Twitter vs. Facebook and the personality predictors of social media usage. Computers in Human Behavior, 2012. 28(2): p. 561-569.

[4]Nettleton, D.F., Data mining of social networks represented as graphs. Computer Science Review, 2013. 7(0): p. 1-34.

[5]Koskinen, J.H., G.L. Robins, and P.E. Pattison, Analysing exponential random graph (p-star) models with missing data using Bayesian data augmentation. Statistical Methodology, 2010. 7(3): p. 366-384.

[6]Yahyaoui, H., et al., Towards a community-based, social network-driven framework for Web services management. Future Generation Computer Systems, 2013. 29(6): p. 1363-1377.

[7]Clements, M., A.P. de Vries, and M.J.T. Reinders, The influence of personalization on tag query length in social media search. Information Processing & Management, 2010. 46(4): p. 403-412.

[8]Kim, H.-N., et al., Folksonomy-based personalized search and ranking in social media services. Information Systems, 2012. 37(1): p. 61-76.

[9]Faust, K., A puzzle concerning triads in social networks: Graph constraints and the triad census. Social Networks, 2010. 32(3): p. 221-233.

[10]Zhang, X., et al., Social image tagging using graph-based reinforcement on multi-type interrelated objects. Signal Processing, 2013. 93(8): p. 2178-2189.

[11]Han, L. and H. Yan, BSN: An automatic generation algorithm of social network data. J. Syst. Softw., 2011. 84(8): p. 1261-1269.

[12]Afshar-Nadjafi, B., A. Rahimi, and H. Karimi, A genetic algorithm for mode identity and the resource constrained project scheduling problem. Scientia Iranica, (0).

[13]Xu, K. and X. Zhang, Mining Community in Mobile Social Network. Procedia Engineering, 2012. 29(0): p. 3080-3084.

[14]Ajami, R., N.A. Qirim, and N. Ramadan, Privacy Issues in Mobile Social Networks. Procedia Computer Science, 2012. 10(0): p. 672-679.

[15]Chang, C., S.N. Srirama, and S. Ling, Towards an adaptive mediation framework for Mobile Social Network in Proximity. Pervasive and Mobile Computing, (0).

[16]Eirinaki, M., S. Pisal, and J. Singh, Feature-based opinion mining and ranking. Journal of Computer and System Sciences, 2012. 78(4): p. 1175-1184.

[17]Kandiah, V. and D.L. Shepelyansky, PageRank model of opinion formation on social networks. Physica A: Statistical Mechanics and its Applications, 2012. 391(22): p. 5779-5793.

[18]Corsaro, D., et al., Actor network pictures and networking activities in business networks: An experimental study. Industrial Marketing Management, 2011. 40(6): p. 919-932.

[19]Kahar, R., et al., Trusting the Social Media in Small Business. Procedia - Social and Behavioral Sciences, 2012. 66(0):p. 564-570.

[20]Kolberg, M., et al., Feature interaction in a federated communications-enabled collaboration platform. Computer Networks, (0).

[21]Ducatelle, F., G. Caro, and L. Gambardella, An Analysis of the Different Components of the AntHocNet Routing Algorithm, in Ant Colony Optimization and Swarm Intelligence, M. Dorigo, et al., Editors. 2006, Springer Berlin Heidelberg. p. 37-48.

[22]Erola, A., et al., Exploiting social networks to provide privacy in personalized web search. Journal of Systems and Software, 2011. 84(10): p. 1734-1745.

[23]Yang, S.J.H., et al., Improving peer-to-peer search performance through intelligent social search. Expert Systems with Applications, 2009. 36(7): p. 10312-10324.

[24]Zaharieva, A., Social welfare and wage inequality in search equilibrium with personal contacts. Labour Economics, 2013. 23(0): p. 107-121.

[25]Liu, D., et al., Types of stable network structures based on exhaustive search. Social Networks, 2013. 35(1): p. 124-129.

[26]Zhang, Z., et al., User community discovery from multi-relational networks. Decision Support Systems, 2013. 54(2): p. 870-879.

[27]del Val, E., M. Rebollo, and V. Botti, Promoting cooperation in service-oriented MAS through social plasticity and incentives. Journal of Systems and Software, 2013. 86(2): p. 520-537.

[28]Jung, J.J., Trustworthy knowledge diffusion model based on risk discovery on peer-to-peer networks. Expert Systems with Applications, 2009. 36(3, Part 2): p. 7123-7128.

[29]Liu, L., et al., A probabilistic graphical model for topic and preference discovery on social media. Neurocomputing, 2012. 95(0): p. 78-88.

[30]Bj, T.A., et al., Workload-aware indexing for keyword search in social networks, in Proceedings of the 20th ACM international conference on Information and knowledge management2011, ACM: Glasgow, Scotland, UK. p. 535-544.

[31]Mozo, A. and J. Salvachúa, Scalable tag search in social network applications. Computer Communications, 2008. 31(3): p. 423-436.

[32]Li, C.-T., M.-K. Shan, and S.-D. Lin, Context-based people search in labeled social networks, in Proceedings of the 20th ACM international conference on Information and knowledge management2011, ACM: Glasgow, Scotland, UK. p. 1607-1612.

[33]Carmel, D., et al., Personalized social search based on the user's social network, in Proceedings of the 18th ACM conference on Information and knowledge management2009, ACM: Hong Kong, China. p. 1227-1236.

[34]Fedotova, N. and L. Veltri, Reputation management algorithms for DHT-based peer-to-peer environment. Computer Communications, 2009. 32(12): p. 1400-1409.

[35]Jafari Navimipour, N., et al., Resource discovery mechanisms in grid systems: A survey. Journal of Network and Computer Applications, 2014.

[36]Xu, C., et al., It is not for fun: An examination of social network site usage. Information & Management, 2012. 49(5): p. 210-217.

[37]Maamar, Z., et al., Using Social Networks for Web Services Discovery. Internet Computing, IEEE, 2011. 15(4): p. 48-54.

[38]Maamar, Z., H. Hacid, and M.N. Huhns, Why Web Services Need Social Networks. Internet Computing, IEEE, 2011. 15(2): p. 90-94.

[39]Elnaffar, S. and Z. Maamar, Composite Web Services Formation Using a Social Network of Web Services: A Preliminary Investigation. Procedia Computer Science, 2011. 5(0): p. 466-471.

[40]Maamar, Z., et al., LinkedWS: A novel Web services discovery model based on the Metaphor of “social networks”. Simulation Modelling Practice and Theory, 2011. 19(1): p. 121-132.

International Journal of Information Engineering and Electronic Business (IJIEEB)