Using complex network features for fast clustering in the web

Jintao Tang; Ting Wang; Ji Wang; Qin Lu; Wenjie Li

doi:10.1145/1963192.1963260

Using complex network features for fast clustering in the web

Jintao Tang
, Ting Wang
, Ji Wang
, Qin Lu
, Wenjie Li

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

4 Citations (Scopus)

Abstract

Applying graph clustering algorithms in real world networks needs to overcome two main challenges: the lack of prior knowledge and the scalability issue. This paper proposes a novel method based on the topological features of complex networks to optimize the clustering algorithms in real-world networks. More specifically, the features are used for parameter estimation and performance optimization. The proposed method is evaluated on real-world networks extracted from the web. Experimental results show improvement both in terms of Adjusted Rand index values as well as runtime efficiency.

Original language	English
Title of host publication	Proceedings of the 20th International Conference Companion on World Wide Web, WWW 2011
Pages	133-134
Number of pages	2
DOIs	https://doi.org/10.1145/1963192.1963260
Publication status	Published - 29 Apr 2011
Event	20th International Conference Companion on World Wide Web, WWW 2011 - Hyderabad, India Duration: 28 Mar 2011 → 1 Apr 2011

Conference

Conference	20th International Conference Companion on World Wide Web, WWW 2011
Country/Territory	India
City	Hyderabad
Period	28/03/11 → 1/04/11

Keywords

complex networks
graph clustering
parameter estimation

ASJC Scopus subject areas

Computer Networks and Communications
Information Systems

More information

10.1145/1963192.1963260

Cite this

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title = "Using complex network features for fast clustering in the web",

abstract = "Applying graph clustering algorithms in real world networks needs to overcome two main challenges: the lack of prior knowledge and the scalability issue. This paper proposes a novel method based on the topological features of complex networks to optimize the clustering algorithms in real-world networks. More specifically, the features are used for parameter estimation and performance optimization. The proposed method is evaluated on real-world networks extracted from the web. Experimental results show improvement both in terms of Adjusted Rand index values as well as runtime efficiency.",

keywords = "complex networks, graph clustering, parameter estimation",

author = "Jintao Tang and Ting Wang and Ji Wang and Qin Lu and Wenjie Li",

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AU - Tang, Jintao

AU - Wang, Ting

AU - Wang, Ji

AU - Lu, Qin

AU - Li, Wenjie

PY - 2011/4/29

Y1 - 2011/4/29

N2 - Applying graph clustering algorithms in real world networks needs to overcome two main challenges: the lack of prior knowledge and the scalability issue. This paper proposes a novel method based on the topological features of complex networks to optimize the clustering algorithms in real-world networks. More specifically, the features are used for parameter estimation and performance optimization. The proposed method is evaluated on real-world networks extracted from the web. Experimental results show improvement both in terms of Adjusted Rand index values as well as runtime efficiency.

AB - Applying graph clustering algorithms in real world networks needs to overcome two main challenges: the lack of prior knowledge and the scalability issue. This paper proposes a novel method based on the topological features of complex networks to optimize the clustering algorithms in real-world networks. More specifically, the features are used for parameter estimation and performance optimization. The proposed method is evaluated on real-world networks extracted from the web. Experimental results show improvement both in terms of Adjusted Rand index values as well as runtime efficiency.

KW - complex networks

KW - graph clustering

KW - parameter estimation

UR - https://www.scopus.com/pages/publications/79955147514

U2 - 10.1145/1963192.1963260

DO - 10.1145/1963192.1963260

M3 - Conference article published in proceeding or book

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EP - 134

BT - Proceedings of the 20th International Conference Companion on World Wide Web, WWW 2011

T2 - 20th International Conference Companion on World Wide Web, WWW 2011

Y2 - 28 March 2011 through 1 April 2011

ER -

Using complex network features for fast clustering in the web

Abstract

Conference

Keywords

ASJC Scopus subject areas

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