Mining temporally changing web usage graphs

Prasanna Desikan, Jaideep Srivastava

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Scopus citations

Abstract

Web mining has been explored to a vast degree and different techniques have been proposed for a variety of applications that include Web Search, Web Classification, Web Personalization etc. Most research on Web mining has been from a 'data-centric' point of view. The focus has been primarily on developing measures and applications based on data collected from content, structure and usage of Web until a particular time instance. In this project we examine another dimension of Web Mining, namely temporal dimension. Web data has been evolving over time, reflecting the ongoing trends. These changes in data in the temporal dimension reveal new kind of information. This information has not captured the attention of the Web mining research community to a large extent. In this paper, we highlight the significance of studying the evolving nature of the Web graphs. We have classified the approach to such problems at three levels of analysis: single node, sub-graphs and whole graphs. We provide a framework to approach problems in this kind of analysis and identify interesting problems at each level. Our experiments verify the significance of such an analysis and also point to future directions in this area. The approach we take is generic and can be applied to other domains, where data can be modeled as a graph, such as network intrusion detection or social networks.

Original languageEnglish (US)
Title of host publicationAdvances in Web Mining and Web Usage Analysis - 6th International Workshop on Knowledge Discovery on the Web, WebKDD 2004, Revised Selected Papers
PublisherSpringer Verlag
Pages1-17
Number of pages17
ISBN (Print)3540471278, 9783540471271
DOIs
StatePublished - 2006
Event6th International Workshop on Knowledge Discovery on the Web, WebKDD 2004 - Seattle, WA, United States
Duration: Aug 22 2004Aug 25 2004

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3932 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other6th International Workshop on Knowledge Discovery on the Web, WebKDD 2004
CountryUnited States
CitySeattle, WA
Period8/22/048/25/04

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