Incorporating weight properties in detection of web spam

This paper focus on incorporating weight properties to enhance Web spam detection algorithms. Our proposed methodology adds this feature into Anti-TrustRank algorithm and call it weighted Anti-TrustRank algorithm to show the effectiveness of the weight properties using a new metric. Experiments are...

Full description

Bibliographic Details
Main Authors: Goh, Kwang Leng Alex, Ravi, Kumar, Singh, Ashutosh Kumar
Other Authors: John Wu (Contact)
Format: Conference Paper
Published: Institute of Electrical and Electronics Engineers 2012
Online Access:http://hdl.handle.net/20.500.11937/31508
_version_ 1848753399492771840
author Goh, Kwang Leng Alex
Ravi, Kumar
Singh, Ashutosh Kumar
author2 John Wu (Contact)
author_facet John Wu (Contact)
Goh, Kwang Leng Alex
Ravi, Kumar
Singh, Ashutosh Kumar
author_sort Goh, Kwang Leng Alex
building Curtin Institutional Repository
collection Online Access
description This paper focus on incorporating weight properties to enhance Web spam detection algorithms. Our proposed methodology adds this feature into Anti-TrustRank algorithm and call it weighted Anti-TrustRank algorithm to show the effectiveness of the weight properties using a new metric. Experiments are conducted on WEBSPAM-UK2006, a public Web spam dataset and have shown that weighted Anti-TrustRank significantly outperforms Anti-TrustRank algorithm up to 37.85%.
first_indexed 2025-11-14T08:23:54Z
format Conference Paper
id curtin-20.500.11937-31508
institution Curtin University Malaysia
institution_category Local University
last_indexed 2025-11-14T08:23:54Z
publishDate 2012
publisher Institute of Electrical and Electronics Engineers
recordtype eprints
repository_type Digital Repository
spelling curtin-20.500.11937-315082017-09-13T15:20:30Z Incorporating weight properties in detection of web spam Goh, Kwang Leng Alex Ravi, Kumar Singh, Ashutosh Kumar John Wu (Contact) This paper focus on incorporating weight properties to enhance Web spam detection algorithms. Our proposed methodology adds this feature into Anti-TrustRank algorithm and call it weighted Anti-TrustRank algorithm to show the effectiveness of the weight properties using a new metric. Experiments are conducted on WEBSPAM-UK2006, a public Web spam dataset and have shown that weighted Anti-TrustRank significantly outperforms Anti-TrustRank algorithm up to 37.85%. 2012 Conference Paper http://hdl.handle.net/20.500.11937/31508 10.1109/URKE.2012.6319540 Institute of Electrical and Electronics Engineers restricted
spellingShingle Goh, Kwang Leng Alex
Ravi, Kumar
Singh, Ashutosh Kumar
Incorporating weight properties in detection of web spam
title Incorporating weight properties in detection of web spam
title_full Incorporating weight properties in detection of web spam
title_fullStr Incorporating weight properties in detection of web spam
title_full_unstemmed Incorporating weight properties in detection of web spam
title_short Incorporating weight properties in detection of web spam
title_sort incorporating weight properties in detection of web spam
url http://hdl.handle.net/20.500.11937/31508