CommentedPapers

Many are scattered at AnalyzedTrustMetrics but i want to slowly move them here. Q: Is there a wiki where I can keep a list of reference and export it in bibtex (or something similar)

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TableOfContents

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= Algorithms for Estimating Relative Importance in Networks =

www.ics.uci.edu/~scott/rel_auth.pdf

Scott White, Padhraic Smyth Information and Computer Science University of California, Irvine

ABSTRACT Large and complex graphs representing relationships among sets of entities are an increasingly common focus of inter- est in data analysis examples include social networks,Web graphs,telecommunication networks,and biological networks. In interactive analysis of such data a natural query is which entities are most important in the network relative to a par- ticular individual or set of individuals? We investigate the problem of answering such queries in this paper,focusing in particular on de ning and computing the importance of nodes in a graph relative to one or more root nodes.We de  ne a general framework and a number of di

= Scaling Personalized Web Search =

http://dbpubs.stanford.edu:8090/pub/2002-12

Jeh, Glen; Widom, Jennifer. Technical Report, Computer Science Department, Stanford University, 2002

Abstract 	Recent web search techniques augment traditional text matching with a global notion of ``importance'' based on the linkage structure of the web, such as in Google's "PageRank" algorithm. For more refined searches, this global notion of importance can be specialized to create personalized views of importance--for example, importance scores can be biased according to a user-specified set of initially-interesting pages. Computing and storing all possible personalized views in advance is impractical, as is computing personalized views at query time, since the computation of each view requires an iterative computation over the web graph. We present new graph-theoretical results, and a new technique based on these results, that encode personalized views as "partial vectors". Partial vectors are shared across multiple personalized views, and their computation and storage costs scale well with the number of views. Our approach enables incremental computation, so that the construction of personalized views from partial vectors is practical at query time. We present efficient dynamic programming algorithms for computing partial vectors, an algorithm for constructing personalized views from partial vectors, and experimental results demonstrating the effectiveness and scalability of our techniques.

check citations at http://citeseer.ist.psu.edu/jeh02scaling.html

= Local Methods for Estimating PageRank Values  =

http://citeseer.ist.psu.edu/670632.html

Yen-Yu Chen Qingqing Gan Torsten Suel CIS Department Polytechnic University...

= Exploiting the Block Structure of the Web for Computing =

http://citeseer.ist.psu.edu/678327.html PageRank Sepandar D. Kamvar Taher H. Haveliwala Christopher D. Manning Gene...

= An Analytical Comparison of Approaches to Personalizing PageRank =

http://www.stanford.edu/~taherh/papers/comparison.pdf

Taher Haveliwala, Sepandar Kamvar and Glen Jeh. Stanford University Technical Report, 2003.

= The Second Eigenvalue of the Google Matrix =

http://www.stanford.edu/~taherh/papers/secondeigenvalue.pdf

Taher H. Haveliwala and Sepandar D. Kamvar Stanford University Technical Report, March 2003.

... Spam Detection. The eigenvectors corresponding to the second eigenvalue RS7 0 are an artifact of certain structures in the web graph. In particular, each pair of leaf nodes in the SCC graph for the chain + corresponds to an eigenvector of C with eigenvalue 0. These leaf nodes in the SCC are those subgraphs in the web link graph which may have incoming edges, but have no edges to other components. Link spammers often generate such structures in attempts to hoard rank. Analysis of the nonprincipal eigenvectors of C may lead to strategies for combating link spam. ...

= The Link Prediction Problem for Social Networks =

http://citeseer.ist.psu.edu/692742.html

David Liben-Nowell Jon Kleinberg

Abstract: Given a snapshot of a social network, can we infer which new interactions among its members are likely to occur in the near future? We formalize this question as the link prediction problem, and develop approaches to link prediction based on measures for analyzing the "proximity" of nodes in a network. Experiments on large co-authorship networks suggest that information about future interactions can be extracted from network topology alone, and that fairly subtle measures for detecting... (Update)

= TO BE READ =

Semantic Web Interaction on Internet Relay Chat
http://www.mindswap.org/papers/irc.pdf

Bayesian Network-Based Trust Model
http://csdl.computer.org/comp/proceedings/wi/2003/1932/00/19320372abs.htm

readings
http://www.csee.umbc.edu/~msmith27/readings/index.html

A Computational Trust Model for Multi-Agent Interactions based on Confidence and Reputation
http://eprints.ecs.soton.ac.uk/8542/

Incentive Compatible Trading Mechanism for Trust Revelation
www.cse.buffalo.edu/~sbraynov/ publications/economic_agents.pdf

thomas
http://www.cs.uwaterloo.ca/~tt5tran/publications.html

Bayesian Network-Based Trust Model
http://www.cs.usask.ca/grads/yaw181/publications/wangy_trust.pdf

Is Trust the Result of Bayesian Learning?
http://www.phil-fak.uni-duesseldorf.de/sowi/lsi/vortraeg/trustbl.htm