Trust Management for Semantic Web

By Maryam Tahajod, Azadeh Iranmehr, Nasim Khozooyi. 2009 Second International Conference on Computer and Electrical Engineering, 2009

687 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Information Systems, Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/de837e0e18b4c695d0810f77074b9258b1b3264b

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Networks and Communications

In Information Systems

In Sociology and Political Science

Citations in the corpus

Statistical properties of community structure in large social and information networks (2008, neutral): “LiveJournal 4,843,953 42,845,684 Blog friendships [5] Epinions 75,877 405,739 Trust network [28] CA-DBLP 317,080 1,049,866 Co-authorship [5] • Information (citation) networks”
Predicting positive and negative links in signed social networks by transfer learning (2013, background): “Study on signed networks connects to works on trust management in various settings such as P2P networks [11], Semantic Web [22], and Web spam detection [7].”
Listing All Maximal Cliques in Large Sparse Real-World Graphs (2011, neutral): “soc-Epinions1 [42] 75,888 405,740 67 1,775,074 * 27.”
Kronecker Graphs: An Approach to Modeling Networks (2008, background): “We consider several different networks, like a graph of connectivity among Internet Autonomous systems (AS-ROUTEV IEWS) with N =6,474 andE =26,467 a who-trusts-whom type social network from Epinions (Richardson et al., 2003) (EPINIONS) with N =75,879 andE =508,960 and many others.”
Hierarchical block structures and high-resolution model selection in large networks (2013, background): “com trust network [79] 28 Berkeley/Stanford web graph [71] 7 Power Grid [75] 18 Slashdot [80] 29 Google web graph [71] 8 arXiv Co-Authors (hep-th) [70] 19 Flickr [81] 30 Youtube social network [73] 9 arXiv Co-Authors (hep-ph) [70] 20 Gowalla social network [78] 31 Yahoo groupse (bipartite) 10 arXiv ”
Recommender Systems (2016, neutral): “Methods for trust propagation with multiplicative methods are discussed in [241, 509].”
A Framework for Computing the Privacy Scores of Users in Online Social Networks (2009, methodology): “…is a large body of applications and research on this topic, see for example, eBay s sellers and buyers rating system, trust management for the Semantic Web [Richardson et al. 2003], etc. Trust scores could be used by social-network users to determine who can view their personal information.”
Social ties and their relevance to churn in mobile telecom networks (2008, background): “In [28], the issue of trust computation is addressed in a semantic web setting.”
Predicting positive and negative links in online social networks (2010, background): “First, our use of trust networks as a source of data connects to a large body of work on trust management in several settings, including peer-to-peer networks [12, 25], Semantic Web applications [22], and Web spam detection [10].”
A survey of trust in computer science and the Semantic Web (2007, methodology): “[55], hose goal is to provide a means of merging trust that is robust o noise.”
SybilGuard: Defending Against Sybil Attacks via Social Networks (2006, background): “Trust propagation or transitive trust is a technique that researchers often use on trust networks [7, 10, 20, 24].”
Fast exact shortest-path distance queries on large networks by pruned landmark labeling (2013, background): “com), where each vertex represents a user and each edge represents a trust relationship [33].”
Overlapping community detection in networks: The state-of-the-art and comparative study (2011, background): “Epinions: a who-trust-whom on-line social network of a consumer review site Epinions.com [Richardson et al. 2003].”
Hinge-Loss Markov Random Fields and Probabilistic Soft Logic (2015, methodology): “We reproduce their experimental setup using their sample of the signed Epinions trust network, orginally collected by Richardson et al. (2003), in which users indicate whether they trust or distrust other users.”
Community Structure in Large Networks: Natural Cluster Sizes and the Absence of Large Well-Defined Clusters (2008, neutral): “47,567 301,921 0.40 0.65 4.09 48.44 0.30 24 6.28 del.icio.us collaborative tagging social network Epinions 75,877 405,739 0.48 0.90 10.69 183.88 0.26 15 4.27 Who-trusts-whom network from epinions.com [142] Flickr 404,733 2,110,078 0.33 0.86 10.43 442.75 0.40 18 5.42 Flickr photo sharing social netwo”
Vital nodes identification in complex networks (2016, background): “Networks n m kmax k in max k out max c H Delicious 582210 1679090 11370 11168 2767 0.1509 0.2760 Email-EuAll 265009 418956 7636 7631 929 0.0564 0.1198 Epinions 75879 508837 3079 3035 1801 0.1098 0.3471 Wiki-Vote 7115 103689 1167 457 893 0.0800 0.4968 sense that the bookmarks of the leaders are often”