BibSonomy :: publication ::

tag user group author concept BibTeX key search:all search:mstrohm

The blue social bookmark and publication sharing system.

(0)

This publication has not been reviewed yet.

rating distribution

average user rating

The average rating is computed over all reviews. However, some of them may be invisible to you due to the visibility setting chosen by the reviewers.

(0.0 of 5.0 based on 0 reviews)

Hyperincident Connected Components of Tagging Networks

by: Nicolas Neubauer, and Klaus Obermayer

In: HT '09: Proceedings of the Twentieth ACM Conference on Hypertext and Hypermedia New York, NY, USA: ACM (July 2009) .

Abstract

Data created by social bookmarking systems can be described as 3-partite 3-uniform hypergraphs connecting documents, users, and tags tagging networks, such that the toolbox of complex network analysis can be applied to examine their properties. One of the most basic tools, the analysis of connected components, however cannot be applied meaningfully: Tagging networks tend to be almost entirely connected. We therefore propose a generalization of connected components, m-hyperincident connected components. We show that decomposing tagging networks into 2-hyperincident connected components yields a characteristic component distribution with a salient giant component that can be found across various datasets. This pattern changes if the underlying formation process changes, for example, if the hypergraph is constructed from search logs, or if the tagging data is contaminated by spam: It turns out that the second- to 129th largest components of the spam-labeled Bibsonomy dataset are inhabited exclusively by spam users. Based on these findings, we propose and unsupervised method for spam detection.

Description

Hypertext 09 best paper

BibTeX record

@inproceedings{neubauer2009,
  abstract = {Data created by social bookmarking systems can be described as
3-partite 3-uniform hypergraphs connecting documents, users, and
tags (tagging networks),  
such that the toolbox of complex network analysis can be applied to 
examine their properties. One of the most basic tools, the
analysis of connected components, however cannot be applied
meaningfully: Tagging networks 
tend to be almost entirely connected. We therefore propose a
generalization of connected components, m-hyperincident
connected components. 
We show that decomposing tagging networks into 2-hyperincident
connected components yields a characteristic component
distribution with a salient giant component that can be found
across various datasets.  
This pattern changes if the underlying formation process
changes, for example, if the hypergraph is constructed from
search logs, or if the tagging data is contaminated by spam: It
turns out that the second- to 129th largest components of the
spam-labeled Bibsonomy dataset are inhabited exclusively by spam
users. Based on these findings, we propose and  unsupervised
method for spam detection. },
  added-at = {2009-07-01T09:24:01.000+0200},
  address = {New York, NY, USA},
  author = {Neubauer, Nicolas and Obermayer, Klaus},
  biburl = {http://www.bibsonomy.org/bibtex/2f696989e22dd4c77c8a6352526e13efe/mstrohm},
  booktitle = {HT '09: Proceedings of the Twentieth ACM Conference on Hypertext and Hypermedia},
  description = {Hypertext 09 best paper},
  interhash = {2686bad7ff1f4d07c0b3302dff08368a},
  intrahash = {f696989e22dd4c77c8a6352526e13efe},
  keywords = {TOREAD networks tagging},
  month = {July},
  paperid = {fp105},
  publisher = {ACM},
  session = {Full Paper},
  timestamp = {2009-07-01T09:24:01.000+0200},
  title = {Hyperincident Connected Components of Tagging Networks},
  year = 2009
}

Endnote record

%0 Conference Paper
%1 neubauer2009
%A Neubauer, Nicolas
%A Obermayer, Klaus
%B HT '09: Proceedings of the Twentieth ACM Conference on Hypertext and Hypermedia
%C New York, NY, USA
%D 2009
%I ACM
%K 
%T Hyperincident Connected Components of Tagging Networks
%X Data created by social bookmarking systems can be described as
3-partite 3-uniform hypergraphs connecting documents, users, and
tags (tagging networks),  
such that the toolbox of complex network analysis can be applied to 
examine their properties. One of the most basic tools, the
analysis of connected components, however cannot be applied
meaningfully: Tagging networks 
tend to be almost entirely connected. We therefore propose a
generalization of connected components, m-hyperincident
connected components. 
We show that decomposing tagging networks into 2-hyperincident
connected components yields a characteristic component
distribution with a salient giant component that can be found
across various datasets.  
This pattern changes if the underlying formation process
changes, for example, if the hypergraph is constructed from
search logs, or if the tagging data is contaminated by spam: It
turns out that the second- to 129th largest components of the
spam-labeled Bibsonomy dataset are inhabited exclusively by spam
users. Based on these findings, we propose and  unsupervised
method for spam detection.