Abstract
Ordering nodes by rank is a benchmark used in several contexts, from recommendation-based trust networks to e-commerce, search engines and websites ranking. In these scenarios, the node rank depends on the set of links the node establishes, hence it becomes important to choose appropriately the nodes to connect to. The problem of finding which nodes to connect to in order to achieve the best possible rank is known as the best attachment problem. Since in the general case the best attachment problem is NP-hard, in this work we propose heuristics that produce near-optimal results while being computable in polynomial time; simulations on different networks show that our proposals preserve both effectiveness and feasibility in obtaining the best rank.
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References
Weng, J., Miao, C., Goh, A., Shen, Z., Gay, R.: Trust-based agent community for collaborative recommendation. In: AAMAS ’06: Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems, New York, NY, USA, ACM (2006) 1260–1262
Liu, X.: Towards context-aware social recommendation via trust networks. In Lin, X., Manolopoulos, Y., Srivastava, D., Huang, G., eds.: Web Information Systems Engineering -WISE 2013. Volume 8180 of Lecture Notes in Computer Science. Springer Berlin Heidelberg (2013) 121–134
Pan, B., Hembrooke, H., Joachims, T., Lorigo, L., Gay, G., Granka, L.: In google we trust: Users decisions on rank, position, and relevance. Journal of Computer-Mediated Communication 12(3) (2007) 801–823
Chauhan, V., Jaiswal, A., Khan, J.: Web page ranking using machine learning approach. In: Advanced Computing Communication Technologies (ACCT), 2015 Fifth International Conference on. (Feb 2015) 575–580
Fung, R., Lee, M.: Ec-trust (trust in electronic commerce): Exploring the antecedent factors. In: Proceedings of the 5th Americas Conference on Information Systems. (1999) 517–519
Sameerkhan, P., Shahrukh, K., Mohammad, A., Amir, A., Bali, A.: Comment based grading and rating system in e-commerce. International Journal of Engineering Research and General Science 3(1) (2015) 1319–1322
Carchiolo, V., Longheu, A., Malgeri, M., Mangioni, G.: Gain the best reputation in trust networks. In Brazier, F., Nieuwenhuis, K., Pavlin, G.,Warnier, M., Badica, C., eds.: Intelligent Distributed Computing V. Volume 382 of Studies in Computational Intelligence. Springer Berlin Heidelberg (2012) 213–218
Berkhin, P.: A survey on pagerank computing. Internet Mathematics 2 (2005) 73–120
Page, L., Brin, S., Motwani, R., Winograd, T.: The pagerank citation ranking: Bringing order to the web (1998)
Olsen, M., Viglas, A., Zvedeniouk, I.: An approximation algorithm for the link building problem. CoRR abs/1204.1369 (2012)
Carchiolo, V., Longheu, A., Malgeri, M., Mangioni, G.: Users attachment in trust networks: Reputation vs. effort. Int. J. Bio-Inspired Comput. 5(4) (July 2013) 199–209
Carchiolo, V., Longheu, A., Malgeri, M., Mangioni, G.: A heuristic to explore trust networks dynamics. In Zavoral, F., Jung, J.J., Badica, C., eds.: Intelligent Distributed Computing VII. Volume 511 of Studies in Computational Intelligence. Springer International Publishing (2014) 67–76
Carchiolo, V., Longheu, A., Malgeri, M., Mangioni, G.: The cost of trust in the dynamics of best attachment. Computing & Informatics 34(1) (2015)
Avrachenkov, K., Litvak, N.: The effect of new links on google pagerank. Stochastic Models 22(2) (2006) 319–331
de Kerchove, C., Ninove, L., Dooren, P.V.: Maximizing pagerank via outlinks. CoRR abs/0711.2867 (2007)
Fercoq, O., Akian, M., Bouhtou, M., Gaubert, S.: Ergodic control and polyhedral approaches to pagerank optimization. IEEE Trans. Automat. Contr. 58(1) (2013) 134–148
Sydow, M.: Can one out-link change your pagerank? In Szczepaniak, P.S., Kacprzyk, J., Niewiadomski, A., eds.: AWIC. Volume 3528 of Lecture Notes in Computer Science., Springer (2005) 408–414
Bianchini, M., Gori, M., Scarselli, F.: Inside pagerank. ACM Trans. Internet Technol. 5(1) (Febraury 2005) 92–128
Langville, A., Meyer, C.: Deeper inside pagerank. Internet Mathematics 1(3) (2004) 335–380
Nazin, A., Polyak, B.: Adaptive randomized algorithm for finding eigenvector of stochastic matrix with application to pagerank. In: Proceedings of the 48th IEEE Conference on Decision and Control, CDC/CCC 2009. (Dec 2009) 127–132
Albert, R., Barabasi, A.L.: Statistical mechanics of complex networks. Reviews of Modern Physics 74 (2002) 47
Barabasi, A.L., Albert, R.: Emergence of scaling in random networks. Science 286 (1999) 509
Pennock, D.M., Flake, G.W., Lawrence, S., Glover, E.J., Giles, C.L.: Winners don’t take all: Characterizing the competition for links on the web. Proceedings of the National Academy of Sciences 99(8) (2002) 5207–5211
Batagelj, V., Mrvar, A.: Pajek - program for large network analysis. (1999)
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Buzzanca, M., Carchiolo, V., Longheu, A., Malgeri, M., Mangioni, G. (2017). Dealing with the Best Attachment Problem via Heuristics. In: Badica, C., et al. Intelligent Distributed Computing X. IDC 2016. Studies in Computational Intelligence, vol 678. Springer, Cham. https://doi.org/10.1007/978-3-319-48829-5_20
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DOI: https://doi.org/10.1007/978-3-319-48829-5_20
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