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106 5 Web Linkage Miningbetween users make the pages that are constrained by Q alone to be more heterogeneous incontent than the pages that are constrained by both U and Q.Based on a query log, two kinds of implicit link relations are defined according to twodifferent constraints:1. L I 1: d i and d j appear in the same entries constrained by U and Q in the query log. Thatis d i and d j are clicked by the same user issuing the same query;2. L I 2: d i and d j appear in the same entries constrained by the query Q only. That is d i andd j are clicked according to the same query, but the query may be issued by different users.It is clear that the constraint for L I 2 is not as strict as that for L I 1. Thus, more links of L I 2can be found than L I 1, however they may be more noisy.Similar to the implicit links, three kinds of explicit links are defined based on the hyperlinksamong the Web pages according to the following three different conditions:L E (i, j)={ 1 CondE0 Other(5.13)1. Cond E 1 : there exist hyperlinks from d j to d i (In-Link to d i from d j );2. Cond E 2 : there exist hyperlinks from d i to d j (Out- Link from d i to d j );3. Cond E 3 : either Cond E 1orCond E 2 holds.These three types of explicit links under the above conditions are denoted as LE1, LE2,LE3 respectively. In the above definitions, the inlink and out-link are distinguished because,given the target Web page, the in-link is the hyperlink created by other Web page editors whorefer to the target Web page. In contrast, the out-link is created by the editor of the sourceWeb page. They may be different when used to describe the target Web page. From the abovedefinitions, D.Shen et al. [80]said that the implicit links give the relationships between Webpages from the view of Web users. However, the explicit links reflect the relationships amongWeb pages from the view of Web-page editors.To utilize these link information for classification, D.Shen et al. [80] propose two approach.One is to predict the label of the target Web page by the labels of its neighborsthrough majority voting.This algorithm is similar to k-Nearst Neighbor (KNN). However, kis not a constant as in KNN and it is decided by the set of the neighbors of the target page.The other enhances classification performance through the links by constructing virtualdocuments. Given a document, the virtual document is constructed by borrowing some extratext from its neighbors. Although originally the concept of the virtual document is pioneeredby [101], the notion are extended by including different links. After constructing the virtualdocument through links, any traditional text classification algorithm could be employed toclassify the web pages. In [80], they take Naive Bayesian classifier and Support Vector Machineas the classifiers. The experimental results show that implicit links can improve theclassification performance obviously as compared to the explicit links based methods. Moredetails are in [80].SummaryIn this chapter, we have reported some interesting research topics in the context of web linkagemining. The principal technologies used in linkage analysis have been substantially addressedand the real applications have been covered as well. We start with the theory of co-citation andbibliographic couple, followed by two most famous linkage mining algorithms, i.e. PageRank
5.6 Using Link Information for Web Page Classification 107and HITS. The detailed algorithmic discussions provide readers a conceptual understanding onhow current commercial search engines work. In the next section, we will introduce one of themain extensions of web linkage mining - web community discovery. The related algorithmsand techniques have been elaborated. In this chapter, we have also talked about the web graphmodeling theory and its applications in web page classification.
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Web Mining and Social Networking
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Guandong Xu • Yanchun Zhang • L
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VIIIPrefacefollowing characteristic
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Acknowledgements: We would like to
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XIVContents3.1.2 Basic Algorithms f
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XVIContentsPart III Social Networki
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Part IFoundation
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4 1 Introduction(3). Learning usefu
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6 1 Introductioncalled computationa
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8 1 Introduction• The data on the
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10 1 Introductionin a broad range t
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2Theoretical BackgroundsAs discusse
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2.2 Textual, Linkage and Usage Expr
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2.4 Eigenvector, Principal Eigenvec
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2.5 Singular Value Decomposition (S
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2.6 Tensor Expression and Decomposi
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2.7 Information Retrieval Performan
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2.8 Basic Concepts in Social Networ
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2.8 Basic Concepts in Social Networ
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30 3 Algorithms and TechniquesTable
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36 3 Algorithms and TechniquesMetho
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46 3 Algorithms and Techniques3.2 S
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48 3 Algorithms and TechniquesConce
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Page 89 and 90: 4Web Content MiningIn recent years
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Page 93 and 94: 4.2 Web Search 75algorithm. The Web
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Page 126 and 127: 6Web Usage MiningIn previous chapte
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156 7 Extracting and Analyzing Web
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170 8 Web Mining and Recommendation
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190 9 Conclusionsries commonly used
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192 9 Conclusionsas computer scienc
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194 9 Conclusionsresearches have de
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196 References14. J. Ayres, J. Gehr
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198 References49. D. Chakrabarti, R
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200 References82. C. Dwork, R. Kuma
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202 References119. J. Hou and Y. Zh
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204 References151. A. N. Langville
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206 References186. J. K. Mui and K.
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208 References223. C. Shahabi, A. M
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210 References260. G.-R. Xue, D. Sh
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