Abstract
Numerous ways have been established till the present to identify tie strength among users on online social media. Although calculating relationship strength among interconnected users has been a burning research topic, it is not also easy to find out powerful interconnections in a dynamic network as the volatility of user connections over a period of time. Mainly, the highest frequency of sharing information between two users in a network indicates strong bonding in a social network. And possible mutual connections may be established based on the strong bonding between two users. In this paper, we have proposed a novel method to calculate relationship strength among Facebook users with Pearson Correlation and Jaccard’s Coefficient. We propose two factors Analogy Profile and Analogy Friendship to obtain the final relationship strength. The performance of our proposed model is compared with the popular existing model, namely Trust Propagation-User Relationship Strength (TP-URS) using the assessment matrices Precision, Recall, and Dice Similarity Coefficient (DSC). Our proposed method provides a precision value of 0.66, recall value of 0.74, and DSC value of 0.71 which are comparatively better than the existing algorithm.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Hanneman, R.A., Riddle, M.: Introduction to social network methods (2005)
Al Hasan, M.: Optimization Challenges in Complex, Networked and Risky Systems (INFORMS), pp. 115–139 (2016)
Naderifar, M., Goli, H., Ghaljaie, F.: Snowball sampling: a purposeful method of sampling in qualitative research, Strides in Development of Medical Education 14(3) (2017)
Small, M.L.: introduction: The past and future of ego-centric network analysis mario l. small, bernice pescosolido, brea l. perry, edward (ned) smith
Granovetter, M.: Thestarch of weak ties. Am. J. Sociol. 78(6), 1360 (1973)
Manca, M., Boratto, L., Carta, S.: Using Behavioral Data Mining to Produce Friend Recommendations in a Social Bookmarking System. In: Helfert, M., Holzinger, A., Belo, O., Francalanci, C. (eds.) DATA 2014. CCIS, vol. 178, pp. 99–116. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-25936-9_7
Shang, M.S., Zhang, Z.K., Zhou, T., Zhang, Y.C.: Using behavioral data mining to Produce friend recommendations in a social bookmarking system. Phys. A 389(6), 1259 (2010)
Tang, F.: Link-prediction and its application in online social networks. Ph.D. thesis, Victoria University (2017)
Brauer, K., Sendatzki, R., Gander, F., Ruch, W., Proyer, R.T.: Profile similarities among romantic partners’ character strengths and their associations with relationship-and life satisfaction 99, 104248 (2022)
Kahanda, I., Neville, J.: Proceedings of the International AAAI Conference on Web and Social Media, vol.3, pp. 74–81 (2009)
Gilbert, E., Karahalios, K.: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 211–220 (2009)
Viswanath, B., Mislove, A., Cha, M., Gummadi, K.P.: Proceedings of the 2nd ACM Workshop on Online Social Networks, pp. 37–42 (2009)
Xiang, R., Neville, J., Rogati, M.: Proceedings of the 19th international conference on World Wide Web, pp. 981–990 (2010)
Srba, I., Bieliková, M.: 2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology. IEEE 3, pp. 13–16 (2010)
Yanagimoto, H., Yoshioka, M.: 2012 IEEE International Conference on Fuzzy Systems. IEEE, pp. 1–8 (2012)
Nasir, S.U., Kim, T.H.: Trust computation in online social networks using co-citation and transpose trust propagation. IEEE Access 8, 41362 (2020)
Adamic, L.A., Adar, E.: Friends and neighbors on the web. Soc. Netw. 25(3), 211 (2003)
Bilgic, M., Namata, G.M., Getoor, L.: 7th IEEE International Conference on Data Mining Workshops (ICDMW). IEEE, pp. 381–386 (2007)
Kashima, H., Abe, N.: 6th International Conference on Data Mining (ICDM’06) IEEE, pp. 340–349 (2006)
Khadangi, E., Zarean, A., Bagheri, A., Jafarabadi, A.B.: ICCKE 2013 IEEE, pp. 461–465 (2013)
Lu, Z., Savas, B., Tang, W., Dhillon, I.S.: 2010 IEEE international conference on data mining IEEE, pp. 923–928 (2010)
Manca, M., Boratto, L., Carta, S.: Science and Information Conference Springer, pp. 227–242 (2014)
Wang, C., Satuluri, V., Parthasarathy, S.: 7th IEEE international conference on data mining (ICDM) IEEE, pp. 322–331 (2007)
Lin, X., Shang, T., Liu, J.: An estimation method for relationship strength in weighted social network graphs. J. Comput. Commun. 2(04), 82 (2014)
Zhao, G., Lee, M.L., Hsu, W., Chen, W., Hu, H.: Proceedings of the 22nd ACM international conference on Information Knowledge Management, pp. 189–198 (2013)
Zhao, X., Yuan, J., Li, G., Chen, X., Li, Z.: Relationship strength estimation for online social networks with the study on face book. Neurocomputing 95, 89 (2012)
Tao, W., Ju, C., Xu, C.: Research on relationship strength under personalized recommendation service. Sustainability 12(4), 1459 (2020)
Ureña-Carrion, J., Saramäki, J., Kivelä, M.: Estimating tie strength in social networks using temporal communication data. EPJ Data Science 9(1), 37 (2020)
Perikos, I., Michael, L.: A survey on tie strength estimation methods in online social networks. ICAART 3, 484–491 (2022)
Nettleton, D.: Selection of variables and factor derivation, Commercial Data Mining, pp. 79–104 (2014)
Liben-Nowell, D., Kleinberg, J.: The link-prediction problem for social networks. J. Am. Soc. Inform. Sci. Technol. 58(7), 1019 (2007)
Goutte, C., Gaussier, E.: A Probabilistic Interpretation of Precision, Recall and F-Score, with Implication for Evaluation. In: Losada, D.E., Fernández-Luna, J.M. (eds.) ECIR 2005. LNCS, vol. 3408, pp. 345–359. Springer, Heidelberg (2005). https://doi.org/10.1007/978-3-540-31865-1_25
Leskovec, J., et al.: Stanford network analysis project (2010)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Choudhury, D., Acharjee, T. (2023). Effective Estimation of Relationship Strength Among Facebook Users Applying Pearson Correlation and Jaccard’s Coefficient. In: Bhattacharyya, S., Banerjee, J.S., De, D., Mahmud, M. (eds) Intelligent Human Centered Computing. Human 2023. Springer Tracts in Human-Centered Computing. Springer, Singapore. https://doi.org/10.1007/978-981-99-3478-2_2
Download citation
DOI: https://doi.org/10.1007/978-981-99-3478-2_2
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-99-3477-5
Online ISBN: 978-981-99-3478-2
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)