Abstract
Online social networks (OSNs) have become the main medium for connecting people, sharing knowledge and information, and for communication. The social connections between people using these OSNs are formed as virtual links (e.g., friendship and following connections) that connect people. These links are the heart of today’s OSNs as they facilitate all of the activities that the members of a social network can do. However, many of these networks suffer from noisy links, i.e., links that do not reflect a real relationship or links that have a low intensity, that change the structure of the network and prevent accurate analysis of these networks. Hence, a process for assessing and ranking the links in a social network is crucial in order to sustain a healthy and real network. Here, we define link assessment as the process of identifying noisy and non-noisy links in a network. In this paper (The work in this paper is based on and is an extension of our previous work [2].), we address the problem of link assessment and link ranking in social networks using external interaction networks. In addition to a friendship social network, additional exogenous interaction networks are utilized to make the assessment process more meaningful. We employed machine learning classifiers for assessing and ranking the links in the social network of interest using the data from exogenous interaction networks. The method was tested with two different datasets, each containing the social network of interest, with the ground truth, along with the exogenous interaction networks. The results show that it is possible to effectively assess the links of a social network using only the structure of a single network of the exogenous interaction networks, and also using the structure of the whole set of exogenous interaction networks. The experiments showed that some classifiers do better than others regarding both link classification and link ranking. The reasons behind that as well as our recommendation about which classifiers to use are presented.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
We stick to the name \(\mathcal {CAR}\) as provided by the authors in [10].
- 2.
This may sound contradictory to what we claimed in the introduction concerning noise in social networks. However, we contacted the owner of the dataset and made sure that there are neither false-positives nor false-negatives in the Facebook network.
- 3.
A description of the law firm dataset and how it was collected can be found on the original publisher page.
- 4.
More details about the results of the random graphs can be found in our earlier work [2].
References
Abufouda, M., Zweig, K.: Interactions around social networks matter: predicting the social network from associated interaction networks. In: IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 142–145 (2014)
Abufouda, M., Zweig, K.A.: Are we really friends?: Link assessment in social networks using multiple associated interaction networks. In: Proceedings of the 24th International Conference on World Wide Web, WWW 2015 Companion, pp. 771–776. ACM, New York (2015)
Adamic, L.A., Adar, E.: Friends and neighbors on the web. Soc. Netw. 25(3), 211–230 (2003)
Al Hasan, M., Zaki, M.J.: A survey of link prediction in social networks. In: Aggarwal, C. (ed.) Social Network Data Analytics, pp. 243–275. Springer, Boston (2011). https://doi.org/10.1007/978-1-4419-8462-3_9
Altman, N.S.: An introduction to kernel and nearest-neighbor nonparametric regression. Am. Stat. 46(3), 175–185 (1992)
Bardou, P., Mariette, J., Escudié, F., Djemiel, C., Klopp, C.: jvenn: an interactive Venn diagram viewer. BMC Bioinform. 15(1), 293 (2014)
Bastian, M., Heymann, S., Jacomy, M.: Gephi: an open source software for exploring and manipulating networks (2009)
Blondel, V.D., Guillaume, J.-L., Lambiotte, R., Lefebvre, E.: Fast unfolding of communities in large networks. J. Stat. Mech. Theory Exp. 2008(10), P10008 (2008)
Boccaletti, S., et al.: The structure and dynamics of multilayer networks. Phys. Rep. 544(1), 1–122 (2014)
Cannistraci, C.V., Alanis-Lobato, G., Ravasi, T.: From link-prediction in brain connectomes and protein interactomes to the local-community-paradigm in complex networks. Sci. Rep. 3, 1613 (2013)
Chawla, N.V., Bowyer, K.W., Hall, L.O., Kegelmeyer, W.P.: SMOTE: synthetic minority over-sampling technique. J. Artif. Intell. Res. 16, 321–357 (2002)
Chen, J., et al.: Systematic assessment of high-throughput experimental data for reliable protein interactions using network topology. In: 16th IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2004, pp. 368–372. IEEE (2004)
Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20(3), 273–297 (1995)
Cover, T.M.: Geometrical and statistical properties of systems of linear inequalities with applications in pattern recognition. IEEE Trans. Electron. Comput. 14(3), 326–334 (1965)
Davis, D., Lichtenwalter, R., Chawla, N.V.: Multi-relational link prediction in heterogeneous information networks. In: 2011 International Conference on Advances in Social Networks Analysis and Mining, pp. 281–288, July 2011
Deane, C.M., et al.: Protein interactions: two methods for assessment of the reliability of high throughput observations. Mol. Cell. Proteomics 1(5), 349–356 (2002)
Dice, L.R.: Measures of the amount of ecologic association between species. Ecology 26(3), 297–302 (1945)
Gilbert, E.: Predicting tie strength in a new medium. In: Proceedings of the ACM 2012 Conference on Computer Supported Cooperative Work, CSCW 2012, pp. 1047–1056. ACM, New York (2012)
Gilbert, E., Karahalios, K.: Predicting tie strength with social media. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 211–220. ACM (2009)
Goldberg, D.S., Roth, F.P.: Assessing experimentally derived interactions in a small world. Proc. Natl. Acad. Sci. 100(8), 4372–4376 (2003)
Gupte, M., Eliassi-Rad, T.: Measuring tie strength in implicit social networks. In: Proceedings of the 4th Annual ACM Web Science Conference, WebSci 2012, pp. 109–118. ACM, New York (2012)
Hanley, J.A., McNeil, B.J.: The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology 143(1), 29–36 (1982)
Ho, T.K.: Random decision forests. In: Proceedings of the Third International Conference on Document Analysis and Recognition, vol. 1, pp. 278–282. IEEE (1995)
Horvat, E.-A., Hanselmann, M., Hamprecht, F.A., Zweig, K.A.: One plus one makes three (for social networks). PLOS ONE 7(4), 1–8 (2012)
Jaya Lakshmi, T., Durga Bhavani, S.: Link prediction in temporal heterogeneous networks. In: Wang, G.A., Chau, M., Chen, H. (eds.) PAISI 2017. LNCS, vol. 10241, pp. 83–98. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-57463-9_6
Jones, J.J., Settle, J.E., Bond, R.M., Fariss, C.J., Marlow, C., Fowler, J.H.: Inferring tie strength from online directed behavior. PLOS ONE 8(1), 1–6 (2013)
Kotsiantis, S., Kanellopoulos, D., Pintelas, P., et al.: Handling imbalanced datasets: a review. GESTS Int. Trans. Comput. Sci. Eng. 30(1), 25–36 (2006)
Kumar, S., Spezzano, F., Subrahmanian, V.S., Faloutsos, C.: Edge weight prediction in weighted signed networks. In: 2016 IEEE 16th International Conference on Data Mining (ICDM), pp. 221–230, December 2016
Lazega, E.: The Collegial Phenomenon: The Social Mechanisms of Cooperation among Peers in a Corporate Law Partnership. Oxford University Press, Oxford (2012)
Liben-Nowell, D., Kleinberg, J.: The link-prediction problem for social networks. J. Assoc. Inf. Sci. Technol. 58(7), 1019–1031 (2007)
Lü, L., Zhou, T.: Link prediction in complex networks: a survey. Phys. A Stat. Mech. Appl. 390(6), 1150–1170 (2011)
Magnani, M., Rossi, L.: Formation of multiple networks. In: Greenberg, A.M., Kennedy, W.G., Bos, N.D. (eds.) SBP 2013. LNCS, vol. 7812, pp. 257–264. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-37210-0_28
Martínez, V., Berzal, F., Cubero, J.-C.: A survey of link prediction in complex networks. ACM Comput. Surv. (CSUR) 49(4), 69 (2016)
McGee, J., Caverlee, J., Cheng, Z.: Location prediction in social media based on tie strength. In: Proceedings of the 22nd ACM International Conference on Information and Knowledge Management, CIKM 2013, pp. 459–468. ACM, New York (2013)
McPherson, M., Smith-Lovin, L., Cook, J.M.: Birds of a feather: homophily in social networks. Annu. Rev. Sociol. 27, 415–444 (2001)
Negi, S., Chaudhury, S.: Link prediction in heterogeneous social networks. In: Proceedings of the 25th ACM International on Conference on Information and Knowledge Management, CIKM 2016, pp. 609–617. ACM, New York (2016)
Newman, M.E.: Clustering and preferential attachment in growing networks. Phys. Rev. E 64(2), 025102 (2001)
Pappalardo, L., Rossetti, G., Pedreschi, D.: How well do we know each other? Detecting tie strength in multidimensional social networks. In: 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), pp. 1040–1045. IEEE (2012)
Pedregosa, F., et al.: Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011)
Pratima, Kaushal, R.: Tie strength prediction in OSN. In: 2016 3rd International Conference on Computing for Sustainable Global Development (INDIACom), pp. 841–844, March 2016
Quinlan, J.R.: Induction of decision trees. Mach. Learn. 1(1), 81–106 (1986)
Ravasz, E., Somera, A.L., Mongru, D.A., Oltvai, Z.N., Barabási, A.-L.: Hierarchical organization of modularity in metabolic networks. Science 297(5586), 1551–1555 (2002)
Rotabi, R., Kamath, K., Kleinberg, J., Sharma, A.: Detecting strong ties using network motifs. In: Proceedings of the 26th International Conference on World Wide Web Companion, WWW 2017 Companion, Republic and Canton of Geneva, Switzerland, pp. 983–992. International World Wide Web Conferences Steering Committee (2017)
Salton, G., McGill, M.J.: Introduction to Modern Information Retrieval. McGraw-Hill, Inc., New York (1986)
Sibona, C.: Unfriending on facebook: context collapse and unfriending behaviors. In: 2014 47th Hawaii International Conference on System Sciences (HICSS), pp. 1676–1685, January 2014
Spitz, A., Gimmler, A., Stoeck, T., Zweig, K.A., Horvat, E.-A.: Assessing low-intensity relationships in complex networks. PLOS ONE 11(4), 1–17 (2016)
Walker, S.H., Duncan, D.B.: Estimation of the probability of an event as a function of several independent variables. Biometrika 54(1–2), 167–179 (1967)
Wang, P., Xu, B., Wu, Y., Zhou, X.: Link prediction in social networks: the state-of-the-art. Sci. China Inf. Sci. 58(1), 1–38 (2015)
Wang, X., Lu, W., Ester, M., Wang, C., Chen, C.: Social recommendation with strong and weak ties. In: Proceedings of the 25th ACM International on Conference on Information and Knowledge Management, CIKM 2016, pp. 5–14. ACM, New York (2016)
Wang, X., Sukthankar, G.: Link prediction in heterogeneous collaboration networks. In: Missaoui, R., Sarr, I. (eds.) Social Network Analysis - Community Detection and Evolution. LNSN, pp. 165–192. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-12188-8_8
Xiang, R., Neville, J., Rogati, M.: Modeling relationship strength in online social networks. In: Proceedings of the 19th International Conference on World Wide Web, WWW 2010, pp. 981–990. ACM, New York (2010)
Xie, W., Li, C., Zhu, F., Lim, E.-P., Gong, X.: When a friend in twitter is a friend in life. In: Proceedings of the 4th Annual ACM Web Science Conference, pp. 344–347. ACM (2012)
Yang, Y., Chawla, N.V., Sun, Y., Han, J.: Link prediction in heterogeneous networks: influence and time matters. In: Proceedings of the 12th IEEE International Conference on Data Mining, Brussels, Belgium (2012)
Zhang, H.: The optimality of Naive Bayes. A A 1(2), 3 (2004)
Zhao, X., et al.: Relationship strength estimation for online social networks with the study on facebook. Neurocomputing 95, 89–97 (2012)
Zhou, T., et al.: Predicting missing links via local information. Eur. Phys. J. B 71(4), 623–630 (2009)
Zweig, K.A.: Network Analysis Literacy: A Practical Approach to Networks Analysis Project Design. Springer, Springer (2014)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Abufouda, M., Zweig, K.A. (2019). Link Classification and Tie Strength Ranking in Online Social Networks with Exogenous Interaction Networks. In: Atzmueller, M., Chin, A., Lemmerich, F., Trattner, C. (eds) Behavioral Analytics in Social and Ubiquitous Environments. MUSE MSM MSM 2015 2015 2016. Lecture Notes in Computer Science(), vol 11406. Springer, Cham. https://doi.org/10.1007/978-3-030-34407-8_1
Download citation
DOI: https://doi.org/10.1007/978-3-030-34407-8_1
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-33906-7
Online ISBN: 978-3-030-34407-8
eBook Packages: Computer ScienceComputer Science (R0)