Randomized algorithms
Authoritative sources in a hyperlinked environment
Journal of the ACM (JACM)
WebBase: a repository of Web pages
Proceedings of the 9th international World Wide Web conference on Computer networks : the international journal of computer and telecommunications netowrking
Proceedings of the 9th international World Wide Web conference on Computer networks : the international journal of computer and telecommunications netowrking
SALSA: the stochastic approach for link-structure analysis
ACM Transactions on Information Systems (TOIS)
Stable algorithms for link analysis
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
PageRank, HITS and a unified framework for link analysis
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
Web Structure, Dynamics and Page Quality
SPIRE 2002 Proceedings of the 9th International Symposium on String Processing and Information Retrieval
The webgraph framework I: compression techniques
Proceedings of the 13th international conference on World Wide Web
Lucene in Action (In Action series)
Lucene in Action (In Action series)
ACM Transactions on Internet Technology (TOIT)
PageRank as a function of the damping factor
WWW '05 Proceedings of the 14th international conference on World Wide Web
The political blogosphere and the 2004 U.S. election: divided they blog
Proceedings of the 3rd international workshop on Link discovery
Google's PageRank and Beyond: The Science of Search Engine Rankings
Google's PageRank and Beyond: The Science of Search Engine Rankings
A tutorial on spectral clustering
Statistics and Computing
Link analysis, eigenvectors and stability
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
Distribution of PageRank mass among principle components of the web
WAW'07 Proceedings of the 5th international conference on Algorithms and models for the web-graph
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We study the properties of the principal eigenvector for the adjacency matrix (and related matrices) for a general directed graph. In particular--motivated by the use of the eigenvector for estimating the "importance" of the nodes in the graph--we focus on the distribution of positive weight in this eigenvector, and give a coherent picture which builds upon and unites earlier results. We also propose a simple method--"T-Rank"--for generating importance scores. T-Rank generates authority scores via a one-level, non-normalized matrix, and is thus distinct from known methods such as PageRank (normalized), HITS (two-level), and SALSA (two-level and normalized). We show, using our understanding of the principal eigenvector, that T-Rank has a much less severe "sink problem" than does PageRank. Also, we offer numerical results which quantify the "tightly-knit community" or TKC effect. We find that T-Rank has a stronger TKC effect than PageRank, and we offer a novel interpolation method which allows for continuous tuning of the strength of this TKC effect. Finally, we propose two new "sink remedies", i.e., methods for ensuring that the principal eigenvector is positive everywhere. One of our sink remedies (source pumping) is unique among sink remedies, in that it gives a positive eigenvector without rendering the graph strongly connected. We offer a preliminary evaluation of the effects and possible applications of these new sink remedies.