Community identification algorithms have been used to enhance the quality of the services perceived by its users. Although algorithms for community have a widespread use in the Web, their application to portals or specific subsets of the Web has not been much studied. In this paper, we propose a technique for local community identification that takes into account user access behavior derived from access logs of servers in the Web. The technique takes a departure from the existing community algorithms since it changes the focus of in terest, moving from authors to users. Our approach does not use relations imposed by authors (e.g. hyperlinks in the case of Web pages). It uses information derived from user accesses to a service in order to infer relationships. The communities identified are of great interest to content providers since they can be used to improve quality of their services. We also propose an evaluation methodology for analyzing the results obtained by the algorithm. We present two case studies based on actual data from two services: an online bookstore and an online radio. The case of the online radio is particularly relevant, because it emphasizes the contribution of the proposed algorithm to find out communities in an environment (i.e., streaming media service) without links, that represent the relations imposed by authors (e.g. hyperlinks in the case of Web pages).
 Disclosed is a novel and improved magnetic separator or filter, as well as an apparatus for making such a magnetic separator. Each filter cell comprises a base plate and a plurality of fibrous filter elements made of magnetizable material and secured to the base plate. The filter elements extend in a mutually parallel spaced relation and substantially perpendicularly to the direction of flow of the fluid and to the plane of the magnetic field. Topic distillation is the analysis of hyperlink graph structure to identify mutually reinforcing authorities (popular pages) and hubs (comprehensive lists of links to authorities). Topic distillation is becoming common in Web search engines, but the best-known algorithms model the Web graph at a coarse grain, with whole pages as single nodes. Such models may lose vital details in the markup tag structure of the pages, and thus lead to a tightly linked irrelevant subgraph winning over a relatively sparse relevant subgraph, a phenomenon called topic drift or contamination . The problem gets especially severe in the face of increasingly complex pages with navigation panels and advertisement links. We present an enhanced topic distillation algorithm which analyzes text, the markup tag trees that constitute HTML pages, and hyperlinks between pages. It thereby identifies subtrees which have high text- and hyperlink-based coherence w.r.t. the query. These subtrees get preferential treatment in the mutual reinforcement process. Using over 50 queries, 28 from earlier topic distillation work, we analyzed over 700,000 pages and obtained quantitative and anecdotal evidence that the new algorithm reduces topic drift. This paper addresses the problem of topic distillation on the World Wide Web, namely, given a typical user query to find quality documents related to the query topic. Connectivity analysis has been shown to be useful in identifying high quality pages within a topic specific graph of hyperlinked documents. The essence of our approach is to augment a previous connectivity analysis based algorithm with content analysis. We identify three problems with the existing approach and devise algorithms to tackle them. The results of a user evaluation are reported that show an improvement of precision at 10 documents by at least 45 over pure connectivity analysis. The World Wide Web grows through a decentralized, almost anarchic process, and this has resulted in a large hyperlinked corpus without the kind of logical organization that can be built into more tradit,ionally-created hypermedia. To extract, meaningful structure under such circumstances, we develop a notion of hyperlinked communities on the www t,hrough an analysis of the link topology. By invoking a simple, mathematically clean method for defining and exposing the structure of these communities, we are able to derive a number of themes: The communities can be viewed as containing a core of central, “authoritative” pages linked togh and they exhibit a natural type of hierarchical topic generalization that can be inferred directly from the pat,t,ern of linkage. Our investigation shows that although the process by which users of the Web create pages and links is very difficult to understand at a “local” level, it results in a much greater degree of orderly high-level structure than has typically been assumed. 