Propagation of trust and distrust

By Ramanathan V. Guha, Ravi Kumar, Prabhakar Raghavan, Andrew Tomkins. 2004

1,619 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Sociology and Political Science

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Citations in the corpus

SHINE: Signed Heterogeneous Information Network Embedding for Sentiment Link Prediction (2017, background): “Our problem of predicting positive and negative sentiment links connects to a large body of work on signed social networks, including trust propagation [9], spectral analysis [13], and social media mining [22].”
SoRec: social recommendation using probabilistic matrix factorization (2008, background): “(2) As presented in [6], the understanding of distrust is more complicated than trust, which indicates that the user trust latent feature space may not be the same as the user distrust latent feature space.”
Enhancing credibility judgment of web search results (2011, background): “Guha et al. developed a framework to predict trust between two people on a large trust network through link analysis [10].”
Discovering value from community activity on focused question answering sites: a case study of stack overflow (2012, background): “user voting behaviors [3, 7, 13], our work attempts to identify early community-based indicators of question and answer quality.”
A survey of attack and defense techniques for reputation systems (2009, background): “While it may seem bene.cial to only consider one type of information in order to limit the possible attacks on the system, this also limits the .exibility of the system as well as the ability for honest peers to counteract the attacks that are still relevant [Guha et al. 2004].”
Investigating interactions of trust and interest similarity (2007, methodology): “Epinions' rating and trust data have also been used by Massa and Avesani [38].”
Truth Discovery with Multiple Conflicting Information Providers on the Web (2007, methodology): “TRUTHFINDER uses iterative methods to compute the website trustworthiness and fact confidence, which is widely used in many link analysis approaches [5], [6], [7],”
A model of a trust-based recommendation system on a social network (2006, methodology): “In other words, we take the position that “if i trusts j , and j trusts k, then i should have a somewhat more positive view of k based on this knowledge” [19].”
Trusted P2P transactions with fuzzy reputation aggregation (2005, background): “However, good reputation is hard to be quantified with so many dynamic factors involved [1], [2], [4], [6], [8], [9].”
Recommender Systems (2012, background): “A different approach was proposed by [257] where the authors use the information contained in social relationships between the users (which may stem from users’ family or friendship relations), the transitivity of trust (as in [258]), and the propagation of users’ queries in social networks.”
A survey of trust in computer science and the Semantic Web (2007, background): “…(Goldbeck and Hendler, 2004b) [52] (Stewart, 1999) [53] (Stewart and Zhang, 2003) [54] (Richardson et al., 2003) [55] (Masa and Avesani, 2005) [56] (Guha et al., 2004) [57] (Advogato, 2000) [58] (Chirita et al., 2004) [59] (Ding et al., 2004) [60] Trust in P2P networks and grids (Kamvar et al.,…”
Link Prediction in Complex Networks: A Survey (2010, background): “For example, a social network may consist of positive and negative links, respectively pointing to friends and foes [161], or trusted and distrusted peers [162].”
mTrust: discerning multi-faceted trust in a connected world (2012, background): “developed a formal framework of trust propagation schemes [6].”
Collaborative Filtering beyond the User-Item Matrix (2014, background): “However, the assumption that “an enemy’s enemy is a friend” is less reliable and of the negative relationships and its predictability [Guha et al. 2004; Leskovec et al. 2010b].”
Finding high-quality content in social media (2008, background): “[14] study the problem of propagating trust and distrust among Epinions(5) users, who may assign positive (trust) and negative (distrust) ratings to each other.”