This article is being produced via complete electronic text/image integration, to enable full database storage. It has therefore to meet specific standards regarding structure and presentation.This proof is an automatically produced, low-resolution laser-printer output. The final product will naturally meet our high-quality standards regarding resolution and page lay-out.Please use this proof solely for checking of typesetting/editing and completeness of text and figures. Changes to the article as accepted for publication will not be considered at this stage. Elsevier Science N.L. Computer Networks 00 (2000) COMPNW2288The stochastic approach for link-structure analysis (SALSA) and the TKC effect 1 R. Lempel Ł , S. Moran Department of Computer Science, The Technion, Haifa 32000, Israel AbstractToday, when searching for information on the World Wide Web, one usually performs a query through a term-based search engine. These engines return, as the query's result, a list of Web sites whose contents match the query. For broad topic queries, such searches often result in a huge set of retrieved documents, many of which are irrelevant to the user. However, much information is contained in the link-structure of the World Wide Web. Information such as which pages are linked to others can be used to augment search algorithms. In this context, Jon Kleinberg introduced the notion of two distinct types of Web sites: hubs and authorities. Kleinberg argued that hubs and authorities exhibit a mutually reinforcing relationship: a good hub will point to many authorities, and a good authority will be pointed at by many hubs. In light of this, he devised an algorithm aimed at finding authoritative sites. We present SALSA, a new stochastic approach for link structure analysis, which examines random walks on graphs derived from the link structure. We show that both SALSA and Kleinberg's mutual reinforcement approach employ the same meta-algorithm. We then prove that SALSA is equivalent to a weighted in-degree analysis of the link-structure of World Wide Web subgraphs, making it computationally more efficient than the mutual reinforcement approach. We compare the results of applying SALSA to the results derived through Kleinberg's approach. These comparisons reveal a topological phenomenon called the TKC effect (Tightly Knit Community) which, in certain cases, prevents the mutual reinforcement approach from identifying meaningful authorities. 

No abstract

No abstract

Let A be a matrix with real entries and let j(i) be the index of the leftmost column containing the maximum value in row i ofA. A is said to be monotone if il > i2 implies thatj(il) >-j(i2). A is totally monotone if all of its submatrices are monotone. We show that finding the maximum entry in each row of an arbitrary n x m monotone matrix requires O(m log n) time, whereas if the matrix is totally monotone the time is O(m) when m-> n and is O(m(l+log(n/m))) when m < n.The problem of finding the maximum value within each row of a totally monotone matrix arises in several geometric algorithms such as the all-farthest-neighbors problem for the vertices of a convex polygon. Previously only the property of monotonicity, not total monotonicity, had been used within these algorithms. We use the | bound on finding the maxima of wide totally monotone matrices to speed up these algorithms by a factor of log n.

No abstract