The Experts below are selected from a list of 360 Experts worldwide ranked by ideXlab platform

Hongyuan Zha - One of the best experts on this subject based on the ideXlab platform.

  • link analysis hubs and authorities on the world wide web
    Siam Review, 2004
    Co-Authors: Chris Ding, Hongyuan Zha, Parry Husbands, Horst Simon
    Abstract:

    Ranking the tens of thousands of retrieved webpages for a user query on a Web search engine such that the most informative webpages are on the top is a key information retrieval technology. A popular ranking algorithm is the HITS algorithm of Kleinberg. It explores the reinforcing interplay between authority and hub webpages on a particular topic by taking into account the structure of the Web graphs formed by the Hyperlinks between the webpages. In this paper, we give a detailed analysis of the HITS algorithm through a unique combination of probabilistic analysis and matrix algebra. In particular, we show that to first-order approximation, the ranking given by the HITS algorithm is the same as the ranking by counting inbound and outbound Hyperlinks. Using Web graphs of different sizes, we also provide experimental results to illustrate the analysis.

  • web document clustering using hyperlink structures
    Computational Statistics & Data Analysis, 2002
    Co-Authors: Hongyuan Zha, Chris Ding, Horst D Simon
    Abstract:

    With the exponential growth of information on the World Wide Web, there is great demand for developing efficient methods for effectively organizing the large amount of retrieved information. Document clustering plays an important role in information retrieval and taxonomy management for the Web. In this paper we examine three clustering methods: K-means, multi-level METIS, and the recently developed normalized-cut method using a new approach of combining textual information, hyperlink structure and co-citation relations into a single similarity metric. We found the normalized-cut method with the new similarity metric is particularly effective, as demonstrated on three datasets of web query results. We also explore some theoretical connections between the normalized-cut method and the K-means method.

  • web document clustering using hyperlink structures
    Lawrence Berkeley National Laboratory, 2001
    Co-Authors: Hongyuan Zha, Chris Ding, Horst D Simon
    Abstract:

    WEB DOCUMENT CLUSTERING USING HYPERLINK STRUCTURES XIAOFENG HE y , HONGYUAN ZHA , CHRIS H.Q. DING y AND HORST D. SIMON y Abstract. With the exponential growth of information on the World Wide Web, there is great demand for developing e cient and e ective methods for organizing and retrieving the information available. Document clustering plays an important role in information retrieval and taxonomy man- agement for the World Wide Web and remains an interesting and challenging problem in the eld of web computing. In this paper we consider document clustering methods exploring textual infor- mation, hyperlink structure and co-citation relations. In particular, we apply the normalized-cut clustering method developed in computer vision to the task of hyperdocument clustering. We also explore some theoretical connections of the normalized-cut method to K-means method. We then experiment with normalized-cut method in the context of clustering query result sets for web search engines. Keywords. World Wide Web, graph partitioning, cheeger constant, clustering method, K-means method, normalized cut method, eigenvalue decomposition, power method. 1. Introduction. Currently the World Wide Web contains billions of documents and it is still growing rapidly. Finding the relevant documents to satisfy a user's infor- mation need is a very important and challenging task. Many commercial search en- gines have been developed and used by millions of people all over the world. However, the relevancy of documents returned in search engine result sets is still lacking, and further research and development is needed to really make search engines a ubiquitous information-seeking tool. The World Wide Web has a rich structure: it contains both textual web documents and the Hyperlinks that connect them. The web documents and Hyperlinks between them form a directed graph in which the web documents can be viewed as vertices and the Hyperlinks as directed edges. Algorithms have been de- veloped utilizing this directed graph to extract information contained in a collection of hyperlinked web documents. Kleinberg proposed HITS algorithm based purely on hyperlink information to retrieve the most relevant information: authority and hub documents for a user query 20]. However, if the hypertext collection consists of sev- eral topics, authority and hub documents may only cover the most popular topics and leave out the less popular ones. One way to remedy this situation is to rst partition the hypertext collection into topical groups, and present the search results as a list of topics to the user. This leads to the need to cluster web documents based on both the textual and hyperlink information. There exists a large literature on clustering methods and algorithms 13, 19]. Gen- erally speaking, the purpose of cluster analysis is to organize the data into meaningful groups: the data objects in the same group are highly similar and those in di erent groups are dissimilar. Judging the e ectiveness of a clustering algorithm is di cult and usually application-dependent. In this paper, we apply a similarity-based cluster- ing method to the problem of clustering web documents. It utilizes a graph-theoretic criterion called normalized cut which has its root in the study of graph isoperimetric Department of Computer Science and Engineering, The Pennsylvania State University, Uni- versity Park, PA 16802, f xhe,zha g @cse.psu.edu . This work was supported in part by NSF grant CCR-9901986. y NERSC Division, Lawrence Berkeley National Laboratory, University of California, Berkeley, CA 94720, f xfhe,chqding,hdsimon g @lbl.gov . Supported by Department of Energy through an LBL LDRD fund.

Horst D Simon - One of the best experts on this subject based on the ideXlab platform.

  • web document clustering using hyperlink structures
    Computational Statistics & Data Analysis, 2002
    Co-Authors: Hongyuan Zha, Chris Ding, Horst D Simon
    Abstract:

    With the exponential growth of information on the World Wide Web, there is great demand for developing efficient methods for effectively organizing the large amount of retrieved information. Document clustering plays an important role in information retrieval and taxonomy management for the Web. In this paper we examine three clustering methods: K-means, multi-level METIS, and the recently developed normalized-cut method using a new approach of combining textual information, hyperlink structure and co-citation relations into a single similarity metric. We found the normalized-cut method with the new similarity metric is particularly effective, as demonstrated on three datasets of web query results. We also explore some theoretical connections between the normalized-cut method and the K-means method.

  • web document clustering using hyperlink structures
    Lawrence Berkeley National Laboratory, 2001
    Co-Authors: Hongyuan Zha, Chris Ding, Horst D Simon
    Abstract:

    WEB DOCUMENT CLUSTERING USING HYPERLINK STRUCTURES XIAOFENG HE y , HONGYUAN ZHA , CHRIS H.Q. DING y AND HORST D. SIMON y Abstract. With the exponential growth of information on the World Wide Web, there is great demand for developing e cient and e ective methods for organizing and retrieving the information available. Document clustering plays an important role in information retrieval and taxonomy man- agement for the World Wide Web and remains an interesting and challenging problem in the eld of web computing. In this paper we consider document clustering methods exploring textual infor- mation, hyperlink structure and co-citation relations. In particular, we apply the normalized-cut clustering method developed in computer vision to the task of hyperdocument clustering. We also explore some theoretical connections of the normalized-cut method to K-means method. We then experiment with normalized-cut method in the context of clustering query result sets for web search engines. Keywords. World Wide Web, graph partitioning, cheeger constant, clustering method, K-means method, normalized cut method, eigenvalue decomposition, power method. 1. Introduction. Currently the World Wide Web contains billions of documents and it is still growing rapidly. Finding the relevant documents to satisfy a user's infor- mation need is a very important and challenging task. Many commercial search en- gines have been developed and used by millions of people all over the world. However, the relevancy of documents returned in search engine result sets is still lacking, and further research and development is needed to really make search engines a ubiquitous information-seeking tool. The World Wide Web has a rich structure: it contains both textual web documents and the Hyperlinks that connect them. The web documents and Hyperlinks between them form a directed graph in which the web documents can be viewed as vertices and the Hyperlinks as directed edges. Algorithms have been de- veloped utilizing this directed graph to extract information contained in a collection of hyperlinked web documents. Kleinberg proposed HITS algorithm based purely on hyperlink information to retrieve the most relevant information: authority and hub documents for a user query 20]. However, if the hypertext collection consists of sev- eral topics, authority and hub documents may only cover the most popular topics and leave out the less popular ones. One way to remedy this situation is to rst partition the hypertext collection into topical groups, and present the search results as a list of topics to the user. This leads to the need to cluster web documents based on both the textual and hyperlink information. There exists a large literature on clustering methods and algorithms 13, 19]. Gen- erally speaking, the purpose of cluster analysis is to organize the data into meaningful groups: the data objects in the same group are highly similar and those in di erent groups are dissimilar. Judging the e ectiveness of a clustering algorithm is di cult and usually application-dependent. In this paper, we apply a similarity-based cluster- ing method to the problem of clustering web documents. It utilizes a graph-theoretic criterion called normalized cut which has its root in the study of graph isoperimetric Department of Computer Science and Engineering, The Pennsylvania State University, Uni- versity Park, PA 16802, f xhe,zha g @cse.psu.edu . This work was supported in part by NSF grant CCR-9901986. y NERSC Division, Lawrence Berkeley National Laboratory, University of California, Berkeley, CA 94720, f xfhe,chqding,hdsimon g @lbl.gov . Supported by Department of Energy through an LBL LDRD fund.

Chris Ding - One of the best experts on this subject based on the ideXlab platform.

  • link analysis hubs and authorities on the world wide web
    Siam Review, 2004
    Co-Authors: Chris Ding, Hongyuan Zha, Parry Husbands, Horst Simon
    Abstract:

    Ranking the tens of thousands of retrieved webpages for a user query on a Web search engine such that the most informative webpages are on the top is a key information retrieval technology. A popular ranking algorithm is the HITS algorithm of Kleinberg. It explores the reinforcing interplay between authority and hub webpages on a particular topic by taking into account the structure of the Web graphs formed by the Hyperlinks between the webpages. In this paper, we give a detailed analysis of the HITS algorithm through a unique combination of probabilistic analysis and matrix algebra. In particular, we show that to first-order approximation, the ranking given by the HITS algorithm is the same as the ranking by counting inbound and outbound Hyperlinks. Using Web graphs of different sizes, we also provide experimental results to illustrate the analysis.

  • web document clustering using hyperlink structures
    Computational Statistics & Data Analysis, 2002
    Co-Authors: Hongyuan Zha, Chris Ding, Horst D Simon
    Abstract:

    With the exponential growth of information on the World Wide Web, there is great demand for developing efficient methods for effectively organizing the large amount of retrieved information. Document clustering plays an important role in information retrieval and taxonomy management for the Web. In this paper we examine three clustering methods: K-means, multi-level METIS, and the recently developed normalized-cut method using a new approach of combining textual information, hyperlink structure and co-citation relations into a single similarity metric. We found the normalized-cut method with the new similarity metric is particularly effective, as demonstrated on three datasets of web query results. We also explore some theoretical connections between the normalized-cut method and the K-means method.

  • web document clustering using hyperlink structures
    Lawrence Berkeley National Laboratory, 2001
    Co-Authors: Hongyuan Zha, Chris Ding, Horst D Simon
    Abstract:

    WEB DOCUMENT CLUSTERING USING HYPERLINK STRUCTURES XIAOFENG HE y , HONGYUAN ZHA , CHRIS H.Q. DING y AND HORST D. SIMON y Abstract. With the exponential growth of information on the World Wide Web, there is great demand for developing e cient and e ective methods for organizing and retrieving the information available. Document clustering plays an important role in information retrieval and taxonomy man- agement for the World Wide Web and remains an interesting and challenging problem in the eld of web computing. In this paper we consider document clustering methods exploring textual infor- mation, hyperlink structure and co-citation relations. In particular, we apply the normalized-cut clustering method developed in computer vision to the task of hyperdocument clustering. We also explore some theoretical connections of the normalized-cut method to K-means method. We then experiment with normalized-cut method in the context of clustering query result sets for web search engines. Keywords. World Wide Web, graph partitioning, cheeger constant, clustering method, K-means method, normalized cut method, eigenvalue decomposition, power method. 1. Introduction. Currently the World Wide Web contains billions of documents and it is still growing rapidly. Finding the relevant documents to satisfy a user's infor- mation need is a very important and challenging task. Many commercial search en- gines have been developed and used by millions of people all over the world. However, the relevancy of documents returned in search engine result sets is still lacking, and further research and development is needed to really make search engines a ubiquitous information-seeking tool. The World Wide Web has a rich structure: it contains both textual web documents and the Hyperlinks that connect them. The web documents and Hyperlinks between them form a directed graph in which the web documents can be viewed as vertices and the Hyperlinks as directed edges. Algorithms have been de- veloped utilizing this directed graph to extract information contained in a collection of hyperlinked web documents. Kleinberg proposed HITS algorithm based purely on hyperlink information to retrieve the most relevant information: authority and hub documents for a user query 20]. However, if the hypertext collection consists of sev- eral topics, authority and hub documents may only cover the most popular topics and leave out the less popular ones. One way to remedy this situation is to rst partition the hypertext collection into topical groups, and present the search results as a list of topics to the user. This leads to the need to cluster web documents based on both the textual and hyperlink information. There exists a large literature on clustering methods and algorithms 13, 19]. Gen- erally speaking, the purpose of cluster analysis is to organize the data into meaningful groups: the data objects in the same group are highly similar and those in di erent groups are dissimilar. Judging the e ectiveness of a clustering algorithm is di cult and usually application-dependent. In this paper, we apply a similarity-based cluster- ing method to the problem of clustering web documents. It utilizes a graph-theoretic criterion called normalized cut which has its root in the study of graph isoperimetric Department of Computer Science and Engineering, The Pennsylvania State University, Uni- versity Park, PA 16802, f xhe,zha g @cse.psu.edu . This work was supported in part by NSF grant CCR-9901986. y NERSC Division, Lawrence Berkeley National Laboratory, University of California, Berkeley, CA 94720, f xfhe,chqding,hdsimon g @lbl.gov . Supported by Department of Energy through an LBL LDRD fund.

Elsa Costa E Silva - One of the best experts on this subject based on the ideXlab platform.

  • beyond links understanding meaning and control in political blogs
    New Media & Society, 2016
    Co-Authors: Elsa Costa E Silva
    Abstract:

    The use of Hyperlinks in blogs produces hypertexts, which are characterized by an irregular and not sequential organization. Contrary to the narrative structures in which the author exercises his authority by setting an order of events, hypertexts compromise the integrity of authorship. Blog hyperlinking thus challenges traditional notion of authorship and control exercised over the speech produced. Political contexts, such as those inhabited by politicians and news providers, are quite sensitive to these compromises as control over meanings is seen as problematic. The use of a hyperlink can be seen as willingness to renounce to full control, but acknowledging its presence is insufficient to analyze its effects in the meaning of the posts. This article argues that a qualitative assessment of links is necessary if a full understanding of Hyperlinks implications is to be provided. It proposes a model of analysis with three levels of compromising: referential, accessory, and compulsory.

Robert Wilensky - One of the best experts on this subject based on the ideXlab platform.

  • robust Hyperlinks cheap everywhere now
    Lecture Notes in Computer Science, 2004
    Co-Authors: Thomas A Phelps, Robert Wilensky
    Abstract:

    We propose robust Hyperlinks as a solution to the problem of broken Hyperlinks. A robust hyperlink is a URL augmented with a small signature consisting of carefully chosen words taken from the referenced document. If the address-based portion of the URL fails, this content-based signature can be submitted as a query to web search engines to locate the document. It turns out that very small signatures are sufficient to readily locate individual documents out of the billion on the web, even if the document is modified. Robust Hyperlinks ex hibit a number of desirable qualities: They can be computed and exploited automatically, are small and cheap to compute (so that it is practical to make all Hyperlinks robust), do not require new server or infrastructure support, can be rolled out reasonably well in the existing URL syntax (so they can retrofit existing links to make them robust), and are easy to understand. One can start using robust Hyperlinks now, as servers and web pages are mostly compatible as is, while clients can increase their support in the future. Robust Hyperlinks is one example of using the web to bootstrap new features onto itself.

  • robust Hyperlinks cost just five words each
    2000
    Co-Authors: Thomas A Phelps, Robert Wilensky
    Abstract:

    We propose robust Hyperlinks as a solution to the problem of broken Hyperlinks. A robust hyperlink is a URL augmented with a small "signature", computed from the referenced document. The signature can be submitted as a query to web search engines to locate the document. It turns out that very small signatures are sufficient to readily locate individual documents out of the many millions on the web. Robust Hyperlinks exhibit a number of desirable qualities: They can be computed and exploited automatically, are small and cheap to compute (so that it is practical to make all Hyperlinks robust), do not require new server or infrastructure support, can be rolled out reasonably well in the existing URL syntax, can be used to automatically retrofit existing links to make them robust, and are easy to understand. In particular, one can start using robust Hyperlinks now, as servers and web pages are mostly compatible as is, while clients can increase their support in the future. Robust Hyperlinks are one example of using the web to bootstrap new features onto itself. PLEASE NOTE: a hypertext version of this paper is available at http://HTTP.CS.Berkeley.EDU/~wilensky/robust-Hyperlinks.html