Abstract
Basics of PageRank algorithm have been widely adopted in its variations, tailored for specific scenarios. In this work, we consider the Black Hole metric, an extension of the original PageRank that leverages a (bogus) black hole node to reduce the arc weights normalization effect. We further extend this approach by introducing several black holes to investigate on the cohesiveness of the network, a measure of the strength among nodes belonging to the network. First experiments on real networks show the effectiveness of the proposed approach.
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References
Brin, S., Page, L.: The anatomy of a large-scale hypertextual web search engine. In: Seventh International World-Wide Web Conference (WWW 1998) (1998)
Bianchini, M., Gori, M., Scarselli, F.: Inside pagerank. ACM Trans. Internet Technol. 5(1), 92–128 (2005)
Langville, A.N., Meyer, C.D.: Deeper inside pagerank. Internet Math. 1, 335–380 (2004)
Lee, H.C., Borodin, A.: Perturbation of the hyper-linked environment. In: Warnow, T., Zhu, B. (eds.) COCOON 2003. LNCS, vol. 2697, pp. 272–283. Springer, Heidelberg (2003). https://doi.org/10.1007/3-540-45071-8_29
Richardson, M., Domingos, P.: The intelligent surfer: probabilistic combination of link and content information in PageRank. In: Advances in Neural Information Processing Systems 14. MIT Press (2002)
Zhirov, A.O., Zhirov, O.V., Shepelyansky, D.L.: Two-dimensional ranking of Wikipedia articles. CoRR abs/1006.4270 (2010)
Gupta, P., Goel, A., Lin, J., Sharma, A., Wang, D., Zadeh, R.: WTF: the who to follow service at Twitter. In: Proceedings of the 22nd International Conference on World Wide Web, WWW ’13, Republic and Canton of Geneva, Switzerland, International World Wide Web Conferences Steering Committee, pp. 505–514 (2013)
Chen, L., Chen, G., Wang, F.: Recommender systems based on user reviews: the state of the art. User Model. User-Adap. Interact. 25(2), 99–154 (2015)
Carchiolo, V., Longheu, A., Malgeri, M., Mangioni, G.: Searching for experts in a context-aware recommendation network. Comput. Hum. Behav. 51, 1086–1091 (2015). Computing for Human Learning, Behaviour and Collaboration in the Social and Mobile Networks Era
Kim, Y.A., Phalak, R.: A trust prediction framework in rating-based experience sharing social networks without a web of trust. Inf. Sci. 191, 128–145 (2012)
Carchiolo, V., Longheu, A., Malgeri, M.: Reliable peers and useful resources: searching for the best personalised learning path in a trust- and recommendation-aware environment. Inf. Sci. 180(10), 1893–1907 (2010). Special Issue on Intelligent Distributed Information Systems
Serrano-Guerrero, J., Romero, F., Olivas, J.: Hiperion: a fuzzy approach for recommending educational activities based on the acquisition of competences. Inf. Sci. 248, 114–129 (2013)
Roa-Valverde, A.J., Sicilia, M.A.: A survey of approaches for ranking on the web of data. Inf. Retr. 17(4), 295–325 (2014)
Senanayake, U., Piraveenan, M., Zomaya, A.: The PageRank-index: going beyond citation counts in quantifying scientific impact of researchers. PLoS ONE 10(8), e0134794 (2015)
Wang, X., Tao, T., Sun, J.T., Shakery, A., Zhai, C.: DirichletRank: solving the zero-one gap problem of pagerank. ACM Trans. Inf. Syst. 26(2), 1–29 (2008)
Bahmani, B., Chowdhury, A., Goel, A.: Fast incremental and personalized PageRank. Proc. VLDB Endow. 4(3), 173–184 (2010)
Zhu, Y., Li, X.: Distributed PageRank computation based on iterative aggregation-disaggregation methods. In: Proceedings of the 14th ACM International Conference on Information and Knowledge Management, pp. 578–585 (2005)
Buzzanca, M., Carchiolo, V., Longheu, A., Malgeri, M., Mangioni, G.: Black hole metric: overcoming the PageRank normalization problem. Inf. Sci. 438, 58–72 (2018)
Buzzanca, M., Carchiolo, V., Longheu, A., Malgeri, M., Mangioni, G.: Direct trust assignment using social reputation and aging. J. Ambient Intell. Hum. Comput. 8(2), 167–175 (2017)
Carchiolo, V., Longheu, A., Malgeri, M., Mangioni, G.: Trusting evaluation by social reputation. In: Badica, C., Mangioni, G., Carchiolo, V., Burdescu, D.D. (eds.) Intelligent Distributed Computing, Systems and Applications, pp. 75–84. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-85257-5_8
Advogato network dataset - KONECT, April 2017
Massa, P., Salvetti, M., Tomasoni, D.: Bowling alone and trust decline in social network sites. In: Proceedings of the International Conference Dependable, Autonomic and Secure Computing, pp. 658–663 (2009)
Batagelj, V., Mrvar, A.: Pajek - program for large network analysis. Connections 21(2), 47–57 (1998)
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This work was supported in part by the Piano per la Ricerca 2016/2018 DIEEI Universitá degli Studi di Catania.
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Carchiolo, V., Grassia, M., Longheu, A., Malgeri, M., Mangioni, G. (2019). A PageRank Inspired Approach to Measure Network Cohesiveness. In: Montella, R., Ciaramella, A., Fortino, G., Guerrieri, A., Liotta, A. (eds) Internet and Distributed Computing Systems . IDCS 2019. Lecture Notes in Computer Science(), vol 11874. Springer, Cham. https://doi.org/10.1007/978-3-030-34914-1_33
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