Mining temporally changing web usage graphs

Prasanna Desikan, Jaideep Srivastava

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

6 Citations (Scopus)

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
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pages1-17
Number of pages17
Volume3932 LNAI
Publication statusPublished - 2006
Externally publishedYes
Event6th International Workshop on Knowledge Discovery on the Web, WebKDD 2004 - Seattle, WA
Duration: 22 Aug 200425 Aug 2004

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3932 LNAI
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other6th International Workshop on Knowledge Discovery on the Web, WebKDD 2004
CitySeattle, WA
Period22/8/0425/8/04

Fingerprint

Web Mining
Mining
Intrusion detection
Graph in graph theory
Web Graph
Network Intrusion Detection
Research
Social Support
Web Search
Personalization
Experiments
Social Networks
Subgraph
Verify
Vertex of a graph
Experiment
Direction compound

ASJC Scopus subject areas

  • Computer Science(all)
  • Biochemistry, Genetics and Molecular Biology(all)
  • Theoretical Computer Science

Cite this

Desikan, P., & Srivastava, J. (2006). Mining temporally changing web usage graphs. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 3932 LNAI, pp. 1-17). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3932 LNAI).

Mining temporally changing web usage graphs. / Desikan, Prasanna; Srivastava, Jaideep.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 3932 LNAI 2006. p. 1-17 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3932 LNAI).

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

Desikan, P & Srivastava, J 2006, Mining temporally changing web usage graphs. in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). vol. 3932 LNAI, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 3932 LNAI, pp. 1-17, 6th International Workshop on Knowledge Discovery on the Web, WebKDD 2004, Seattle, WA, 22/8/04.
Desikan P, Srivastava J. Mining temporally changing web usage graphs. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 3932 LNAI. 2006. p. 1-17. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
Desikan, Prasanna ; Srivastava, Jaideep. / Mining temporally changing web usage graphs. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 3932 LNAI 2006. pp. 1-17 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
@inproceedings{b2c86ebae74343f182ee06dd91e7c70c,
title = "Mining temporally changing web usage graphs",
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.",
author = "Prasanna Desikan and Jaideep Srivastava",
year = "2006",
language = "English",
isbn = "3540471278",
volume = "3932 LNAI",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
pages = "1--17",
booktitle = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

}

TY - GEN

T1 - Mining temporally changing web usage graphs

AU - Desikan, Prasanna

AU - Srivastava, Jaideep

PY - 2006

Y1 - 2006

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=33845193995&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=33845193995&partnerID=8YFLogxK

M3 - Conference contribution

SN - 3540471278

SN - 9783540471271

VL - 3932 LNAI

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 1

EP - 17

BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

ER -