Abstract
With the development of the Internet, social network is changing people’s daily lives. In many social networks, the relationships between nodes can be measured. It is an important application to predict trust link, find the most reliable node and rank nodes. In order to implement those applications, it is crucial to assess the credibility of a node. The credibility of a node is denoted as the expected value, which can be evaluated by similarities between the node and its neighbors. That means the credibility of a node is high while its behaviors are reasonable. When multiple-relational networks are becoming prevalent, we observe that it is possible to apply more relations to improve the performance of assessing the credibility of nodes. We found that trust values among one type of nodes and similarity scores among different types of nodes reinforce each other towards better and more meaningful results. In this paper, we introduce a framework that computes the credibility of nodes on a multiple-relational network. The experiment result on real data shows that our framework is effective.
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
Preview
Unable to display preview. Download preview PDF.
References
International, P.S.R.A., WebWatch, C.R.: Leap of Faith: Using the Internet Despite the Dangers: Results of a National Survey for Consumer Reports WebWatch. Consumer Reports WebWatch (October 2005)
Kleinberg, J.M.: Authoritative sources in a hyperlinked environment. J. ACM 46(5), 604–632 (1999)
Page, L., Brin, S., Motwani, R., Winograd, T.: The pagerank citation ranking: Bringing order to the web. Technical report, Stanford InfoLab (November 1999)
Braunstein, A., Mézard, M., Zecchina, R.: Survey propagation: an algorithm for satisfiability. CoRR cs.CC/0212002 (2002)
Sherchan, W., Nepal, S., Paris, C.: A survey of trust in social networks. ACM Comput. Surv. 45(4), 47 (2013)
Orman, L.V.: Bayesian inference in trust networks. ACM Trans. Manage. Inf. Syst. 4(2), 7:1–7:21 (2013)
Bonacich, P.: Factoring and weighting approaches to status scores and clique identification. The Journal of Mathematical Sociology 2(1), 113–120 (1972)
Kamvar, S.D., Schlosser, M.T., Garcia-Molina, H.: The eigentrust algorithm for reputation management in p2p networks. In: WWW, pp. 640–651 (2003)
Richardson, M., Agrawal, R., Domingos, P.: Trust management for the semantic web. In: Fensel, D., Sycara, K., Mylopoulos, J. (eds.) ISWC 2003. LNCS, vol. 2870, pp. 351–368. Springer, Heidelberg (2003)
de Kerchove, C., Dooren, P.V.: The pagetrust algorithm: How to rank web pages when negative links are allowed? In: SDM, pp. 346–352 (2008)
Guha, R.V., Kumar, R., Raghavan, P., Tomkins, A.: Propagation of trust and distrust. In: WWW, pp. 403–412 (2004)
Cartwright, D., Harary, F.: Structural balance: A generalization of heider’s theory. Psychological Review 63(5), 277–293 (1956)
Heider, F.: Attitudes and cognitive organization. J. Psychology 21, 107–112 (1946)
Leskovec, J., Huttenlocher, D.P., Kleinberg, J.M.: Signed networks in social media. In: CHI, pp. 1361–1370 (2010)
Leskovec, J., Huttenlocher, D.P., Kleinberg, J.M.: Predicting positive and negative links in online social networks. In: WWW, pp. 641–650 (2010)
Mishra, A., Bhattacharya, A.: Finding the bias and prestige of nodes in networks based on trust scores. In: WWW, pp. 567–576 (2011)
Li, R.H., Yu, J.X., Huang, X., Cheng, H.: A framework of algorithms: Computing the bias and prestige of nodes in trust networks. CoRR abs/1207.5661 (2012)
Koutra, D., Ke, T.-Y., Kang, U., Chau, D.H(P.), Pao, H.-K.K., Faloutsos, C.: Unifying guilt-by-association approaches: Theorems and fast algorithms. In: Gunopulos, D., Hofmann, T., Malerba, D., Vazirgiannis, M. (eds.) ECML PKDD 2011, Part II. LNCS, vol. 6912, pp. 245–260. Springer, Heidelberg (2011)
Gyöngyi, Z., Garcia-Molina, H., Pedersen, J.O.: Combating web spam with trustrank. In: VLDB, pp. 576–587 (2004)
Wu, B., Goel, V., Davison, B.D.: Topical trustrank: using topicality to combat web spam. In: WWW, pp. 63–72 (2006)
Yin, X., Han, J., Yu, P.S.: Truth discovery with multiple conflicting information providers on the web. IEEE Trans. Knowl. Data Eng. 20(6), 796–808 (2008)
Srikantaiah, K.C., Srikanth, P.L., Tejaswi, V., Shaila, K., Venugopal, K.R., Patnaik, L.M.: Ranking search engine result pages based on trustworthiness of websites. CoRR abs/1209.5244 (2012)
Golub, G.H., Van Loan, C.F.: Matrix computations, 3rd edn. Johns Hopkins University Press, Baltimore (1996)
Jin, X., Luo, J., Yu, J., Wang, G., Joshi, D., Han, J.: Reinforced similarity integration in image-rich information networks. IEEE Trans. Knowl. Data Eng. 25(2), 448–460 (2013)
Zhao, P., Han, J., Sun, Y.: P-rank: a comprehensive structural similarity measure over information networks. In: CIKM, pp. 553–562 (2009)
Antonellis, I., Garcia-Molina, H., Chang, C.C.: Simrank++: query rewriting through link analysis of the click graph. PVLDB 1(1), 408–421 (2008)
Jeh, G., Widom, J.: Simrank: a measure of structural-context similarity. In: KDD, pp. 538–543 (2002)
Wang, G., Hu, Q., Yu, P.S.: Influence and similarity on heterogeneous networks. In: CIKM, pp. 1462–1466 (2012)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Hu, W., Gong, Z. (2014). Assessing the Credibility of Nodes on Multiple-Relational Social Networks. In: Benatallah, B., Bestavros, A., Manolopoulos, Y., Vakali, A., Zhang, Y. (eds) Web Information Systems Engineering – WISE 2014. WISE 2014. Lecture Notes in Computer Science, vol 8787. Springer, Cham. https://doi.org/10.1007/978-3-319-11746-1_5
Download citation
DOI: https://doi.org/10.1007/978-3-319-11746-1_5
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-11745-4
Online ISBN: 978-3-319-11746-1
eBook Packages: Computer ScienceComputer Science (R0)