Predictive random graph ranking on the web

Haixuari Yang; Irwin King; Michael R. Lyu

Predictive random graph ranking on the web

Haixuari Yang
, Irwin King
, Michael R. Lyu

Research output: Chapter in Book or Conference Publication/Proceeding › Conference Publication › peer-review

4 Citations (Scopus)

Abstract

The incomplete information about the Web structure causes inaccurate results of various ranking algorithms. In this paper, we propose a solution to this problem by formulating a new framework called, Predictive Random Graph Ranking, in which we generate a random graph based on the known information about the Web structure. The random graph can be considered as the predicted Web structure, on which ranking algorithm are expected to be improved in accuracy. For this purpose, we extend some current ranking algorithms from a static graph to a random graph. Experimental results show that the Predictive Random Graph Ranking framework can improve the accuracy of the ranking algorithms such as PageRank, Common Neighbor, and Jaccard's Coefficient.

Original language	English
Title of host publication	International Joint Conference on Neural Networks 2006, IJCNN '06
Pages	1825-1832
Number of pages	8
Publication status	Published - 2006
Externally published	Yes
Event	International Joint Conference on Neural Networks 2006, IJCNN '06 - Vancouver, BC, Canada Duration: 16 Jul 2006 → 21 Jul 2006

Publication series

Name	IEEE International Conference on Neural Networks - Conference Proceedings
ISSN (Print)	1098-7576

Conference

Conference	International Joint Conference on Neural Networks 2006, IJCNN '06
Country/Territory	Canada
City	Vancouver, BC
Period	16/07/06 → 21/07/06

Cite this

@inproceedings{c9cd591b010347a8b74d0f88bef5a2e9,

title = "Predictive random graph ranking on the web",

abstract = "The incomplete information about the Web structure causes inaccurate results of various ranking algorithms. In this paper, we propose a solution to this problem by formulating a new framework called, Predictive Random Graph Ranking, in which we generate a random graph based on the known information about the Web structure. The random graph can be considered as the predicted Web structure, on which ranking algorithm are expected to be improved in accuracy. For this purpose, we extend some current ranking algorithms from a static graph to a random graph. Experimental results show that the Predictive Random Graph Ranking framework can improve the accuracy of the ranking algorithms such as PageRank, Common Neighbor, and Jaccard's Coefficient.",

author = "Haixuari Yang and Irwin King and Lyu, \{Michael R.\}",

year = "2006",

language = "English",

isbn = "0780394909",

series = "IEEE International Conference on Neural Networks - Conference Proceedings",

pages = "1825--1832",

booktitle = "International Joint Conference on Neural Networks 2006, IJCNN '06",

note = "International Joint Conference on Neural Networks 2006, IJCNN '06 ; Conference date: 16-07-2006 Through 21-07-2006",

}

Predictive random graph ranking on the web. / Yang, Haixuari; King, Irwin; Lyu, Michael R.
International Joint Conference on Neural Networks 2006, IJCNN '06. 2006. p. 1825-1832 1716331 (IEEE International Conference on Neural Networks - Conference Proceedings).

Research output: Chapter in Book or Conference Publication/Proceeding › Conference Publication › peer-review

TY - GEN

T1 - Predictive random graph ranking on the web

AU - Yang, Haixuari

AU - King, Irwin

AU - Lyu, Michael R.

PY - 2006

Y1 - 2006

N2 - The incomplete information about the Web structure causes inaccurate results of various ranking algorithms. In this paper, we propose a solution to this problem by formulating a new framework called, Predictive Random Graph Ranking, in which we generate a random graph based on the known information about the Web structure. The random graph can be considered as the predicted Web structure, on which ranking algorithm are expected to be improved in accuracy. For this purpose, we extend some current ranking algorithms from a static graph to a random graph. Experimental results show that the Predictive Random Graph Ranking framework can improve the accuracy of the ranking algorithms such as PageRank, Common Neighbor, and Jaccard's Coefficient.

AB - The incomplete information about the Web structure causes inaccurate results of various ranking algorithms. In this paper, we propose a solution to this problem by formulating a new framework called, Predictive Random Graph Ranking, in which we generate a random graph based on the known information about the Web structure. The random graph can be considered as the predicted Web structure, on which ranking algorithm are expected to be improved in accuracy. For this purpose, we extend some current ranking algorithms from a static graph to a random graph. Experimental results show that the Predictive Random Graph Ranking framework can improve the accuracy of the ranking algorithms such as PageRank, Common Neighbor, and Jaccard's Coefficient.

UR - https://www.scopus.com/pages/publications/40649086709

M3 - Conference Publication

AN - SCOPUS:40649086709

SN - 0780394909

SN - 9780780394902

T3 - IEEE International Conference on Neural Networks - Conference Proceedings

SP - 1825

EP - 1832

BT - International Joint Conference on Neural Networks 2006, IJCNN '06

T2 - International Joint Conference on Neural Networks 2006, IJCNN '06

Y2 - 16 July 2006 through 21 July 2006

ER -

Predictive random graph ranking on the web

Abstract

Publication series

Conference

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