Abhinav Mishra
Arnab Bhattacharya
ABSTRACT
Many real-life graphs such as social networks and peer-to-peer networks capture the relationships among the nodes by using trust scores to label the edges. Important usage of such networks includes trust prediction, finding the most reliable or trusted node in a local subgraph, etc. For many of these applications, it is crucial to assess the prestige and bias of a node. The bias of a node denotes its propensity to trust/mistrust its neighbours and is closely related to truthfulness. If a node trusts all its neighbours, its recommendation of another node as trustworthy is less reliable. It is based on the idea that the recommendation of a highly biased node should weigh less. In this paper, we propose an algorithm to compute the bias and prestige of nodes in networks where the edge weight denotes the trust score. Unlike most other graph-based algorithms, our method works even when the edge weights are not necessarily positive. The algorithm is iterative and runs in O(km) time where k is the number of iterations and m is the total number of edges in the network. The algorithm exhibits several other desirable properties. It converges to a unique value very quickly. Also, the error in bias and prestige values at any particular iteration is bounded. Further, experiments show that our model conforms well to social theories such as the balance theory (enemy of a friend is an enemy, etc.).
- P. Bonacich. Factoring and weighting approaches to status scores and clique identification. J. Mathematical Sociology, 2:113--120, 1972.Google Scholar
Cross Ref
- D. Cartwright and F. Harary. Structural balance: A generalization of Heider's theory. Psychological Review, 63:277--293, 1956.Google ScholarCross Ref
- C. de Kerchove and P. V. Dooren. The PageTrust algorithm: How to rank web pages when negative links are allowed? In SDM, pages 346--352, 2008.Google ScholarCross Ref
- G. Golub and C. F. V. Loan. Matrix Computations. Johns Hopkins University Press, 1996.Google Scholar
- R. V. Guha, R. Kumar, P. Raghavan, and A. Tomkins. Propagation of trust and distrust. In WWW, pages 403--412, 2004. Google ScholarDigital Library
- F. Heider. Attitudes and Cognitive Organization. J. Psychology, 21:107--112, 1946.Google ScholarCross Ref
- B. D. Hughes. Random Walks and Random Environments. Oxford University Press, 1996.Google Scholar
- G. Jeh and J. Widom. Simrank: A measure of structural-context similarity. In KDD, pages 538--543, 2002. Google ScholarDigital Library
- S. D. Kamvar, M. T. Schlosser, and H. Garcia-Molina. The Eigentrust algorithm for reputation management in P2P networks. In WWW, pages 640--651, 2003. Google ScholarDigital Library
- J. M. Kleinberg. Authoritative sources in a hyperlinked environment. J. ACM, 46(5):604--632, 1999. Google ScholarDigital Library
- J. Leskovec, D. P. Huttenlocher, and J. M. Kleinberg. Predicting positive and negative links in online social networks. In WWW, pages 641--650, 2010. Google ScholarDigital Library
- J. Leskovec, D. P. Huttenlocher, and J. M. Kleinberg. Signed networks in social media. In CHI, pages 1361--1370, 2010. Google ScholarDigital Library
- D. Lizorkin, P. Velikhov, M. Grinev, and D. Turdakov. Accuracy estimate and optimization techniques for SimRank computation. Proc. VLDB Endowment, 1(1):422--433, 2008. Google ScholarDigital Library
- L. Page, S. Brin, R. Motwani, and T. Winograd. The PageRank citation ranking: Bringing order to the web. Technical report, Stanford Digital Library Technologies Project, 1998.Google Scholar
- M. Richardson, R. Agrawal, and P. Domingos. Trust management for the semantic web. In ISWC, pages 351--368, 2003.Google ScholarDigital Library
Index Terms
- Finding the bias and prestige of nodes in networks based on trust scores
Recommendations
Predicting edge sign and finding prestige of nodes in networks
Recently, as a result of the popularity of online social networks, the analysis and comparison of their contents are in an incremental need. The study of social network and social interaction including both positive and negative connections, which is ...
Context-aware trust network extraction in large-scale trust-oriented social networks
In recent years, social networking sites have been used as a means for a rich variety of activities, such as movie recommendations and product recommendations. In order to evaluate the trust between a truster (i.e., the source) and a trustee (i.e., the ...
Building trust communities using social trust
UMAP'11: Proceedings of the 19th international conference on Advances in User ModelingThe growing popularity of Web based social networks has given rise to the need to build trust communities that inspire members to share their experiences, feelings and opinions in an open and honest way. In this paper, we propose a framework for building ...
Comments