Abstract
Due to the vast number of online business transactions on World Wide Web, mining and analyzing relevant data from the web log data for the users navigational behavior is a challenging task. Finding similar objects and mining top-k objects has a great significance in web recommender systems and social networks. In this paper, we define similar behavior of users about different categories and some propositions in the context of structural similar behavior of nodes in a network. We present an efficient algorithm for top-k categories based on early associates notion (NATBEAN) that mines top-k categories with most similar IP addresses in a descending order. NATBEAN is useful to forecast similar visiting behavior of the users through IP addresses for different categories in the structural context of a bipartite network. This leads to find popular products and less influenceable products in a network of web log data. Initially, we run both Naive approach and NATBEAN for finding top-k categories on a clickstream dataset whose attributes are IP addresses and product categories, then we run our algorithm on three other datasets and compare running times of both the algorithms.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Boldi, P., Leonardi, S., Mascolo, C., Vazirgiannis, M.: Web and social graph mining. IEEE Internet Comput. 18(05), 9–10 (2014)
Liben-Nowell, D., Kleinberg, J.: The link-prediction problem for social networks. J. Am. Soc. Inform. Sci. Technol. 58(7), 1019–1031 (2007)
Lorrain, F., White, H.C.: Structural equivalence of individuals in social networks. J. Math. Sociol. 1(1), 49–80 (1971)
Lin, D.: An information-theoretic definition of similarity. In: ICML, vol. 98, pp. 296–304. Citeseer (1998)
Jeh, G., Widom, J.: SimRank: a measure of structural-context similarity. In: Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 538–543. ACM (2002)
Ganesan, P., Garcia-Molina, H., Widom, J.: Exploiting hierarchical domain structure to compute similarity. ACM Trans. Inf. Syst. (TOIS) 21(1), 64–93 (2003)
Fagin, R., Lotem, A., Naor, M.: Optimal aggregation algorithms for middleware. J. Comput. Syst. Sci. 66(4), 614–656 (2003)
Deshpande, M., Karypis, G.: Item-based top-n recommendation algorithms. ACM Trans. Inf. Syst. (TOIS) 22(1), 143–177 (2004)
Holme, P., Huss, M.: Role-similarity based functional prediction in networked systems: application to the yeast proteome. J. R. Soc. Interface 2(4), 327–333 (2005)
Leicht, E.A., Holme, P., Newman, M.E.: Vertex similarity in networks. Phys. Rev. E 73(2), 026120 (2006)
Sun, J., Qu, H., Chakrabarti, D., Faloutsos, C.: Neighborhood formation and anomaly detection in bipartite graphs. In: Proceedings of the Fifth IEEE International Conference on Data Mining (ICDM 2005), 8-p. IEEE (2005)
Rossi, R.A., McDowell, L.K., Aha, D.W., Neville, J.: Transforming graph data for statistical relational learning. J. Artif. Intell. Res. 45(1), 363–441 (2012)
Cai, J., Yu, S.Z., Wang, Y.: The community analysis of user behaviors network for web traffic. J. Softw. 6(11), 2217–2224 (2011)
Zweig, K.A., Kaufmann, M.: A systematic approach to the one-mode projection of bipartite graphs. Soc. Netw. Anal. Min. 1(3), 187–218 (2011)
Xu, K., Wang, F., Gu, L.: Network-aware behavior clustering of internet end hosts. In: 2011 Proceedings IEEE INFOCOM, pp. 2078–2086. IEEE (2011)
Xu, K., Wang, F., Gu, L.: Behavior analysis of internet traffic via bipartite graphs and one-mode projections. IEEE/ACM Trans. Netw. 22(3), 931–942 (2014)
Jakalan, A., Gong, J., Su, Q., Hu, X., Abdelgder, A.M.: Social relationship discovery of IP addresses in the managed IP networks by observing traffic at network boundary. Comput. Netw. 100, 12–27 (2016)
Taheri, S.M., Mahyar, H., Firouzi, M., Ghalebi, E., Grosu, R., Movaghar, A.: HellRank: a Hellinger-based centrality measure for bipartite social networks. Soc. Netw. Anal. Min. 7(1), 22 (2017)
Rossi, R.A., Ahmed, N.K.: Role discovery in networks. IEEE Trans. Knowl. Data Eng. 27(4), 1112–1131 (2015)
Jin, R., Lee, V.E., Li, L.: Scalable and axiomatic ranking of network role similarity. ACM Trans. Knowl. Discov. Data (TKDD) 8(1), 3 (2014)
Li, J., Li, H., Soh, D., Wong, L.: A correspondence between maximal complete bipartite subgraphs and closed patterns. In: Jorge, A.M., Torgo, L., Brazdil, P., Camacho, R., Gama, J. (eds.) PKDD 2005. LNCS (LNAI), vol. 3721, pp. 146–156. Springer, Heidelberg (2005). https://doi.org/10.1007/11564126_18
Rossi, R.A., Ahmed, N.K.: Web-Google - Web Graphs (2013)
Rossi, R.A., Ahmed, N.K.: ca-GrQc - Miscellaneous Networks (2013)
Acknowledgement
The authors would like to thank the anonymous reviewers of this paper for their valuable comments and suggestions.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Thottempudi, R.R., Mitra, P., Adrijit, G. (2018). Top-k Category Search for an IP Address-Product Network. In: Venkataramani, G., Sankaranarayanan, K., Mukherjee, S., Arputharaj, K., Sankara Narayanan, S. (eds) Smart Secure Systems – IoT and Analytics Perspective. ICIIT 2017. Communications in Computer and Information Science, vol 808. Springer, Singapore. https://doi.org/10.1007/978-981-10-7635-0_23
Download citation
DOI: https://doi.org/10.1007/978-981-10-7635-0_23
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-7634-3
Online ISBN: 978-981-10-7635-0
eBook Packages: Computer ScienceComputer Science (R0)