Difference between revisions of "Zhang et all, WWW 2007"
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== Citation == | == Citation == | ||
| − | + | Jun Zhang, Mark S. Ackerman, and Lada Adamic. 2007. Expertise networks in online communities: structure and algorithms. In Proceedings of the 16th international conference on World Wide Web (WWW '07). ACM, New York, NY, USA, 221-230. | |
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== Online version == | == Online version == | ||
| − | [http:// | + | [http://portal.acm.org/citation.cfm?id=1242603 ACM] |
== Summary == | == Summary == | ||
The aim of this [[Category::paper]] is to identify users with high expertise within online expertise-sharing communities. This [[AddressesProblem::expertise finding]] system uses graph-based algorithms on social networks within the community. | The aim of this [[Category::paper]] is to identify users with high expertise within online expertise-sharing communities. This [[AddressesProblem::expertise finding]] system uses graph-based algorithms on social networks within the community. | ||
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They created a post-reply network in which each user is represented as a node and a directed edge is created from each user who started the post to other users who replied to it. The prestige measure of this network is highly correlated with a user's expertise due to the way the network is constructed. Therefore this network is called ''community expertise network (CEN)''. | They created a post-reply network in which each user is represented as a node and a directed edge is created from each user who started the post to other users who replied to it. The prestige measure of this network is highly correlated with a user's expertise due to the way the network is constructed. Therefore this network is called ''community expertise network (CEN)''. | ||
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In experiments the authors used Spearman's Rho and Kendall's Tau measures to understand the correlations between these ranking algorithms and the human-assigned ratings. It has been observed that they are highly correlated which means that structural information can be used to identify experts in online communities. | In experiments the authors used Spearman's Rho and Kendall's Tau measures to understand the correlations between these ranking algorithms and the human-assigned ratings. It has been observed that they are highly correlated which means that structural information can be used to identify experts in online communities. | ||
| − | It has been also observed that algorithms like PageRank and HITS which works really well in WWW, does not outperform simpler algorithms used in this online community. | + | It has been also observed that algorithms like PageRank and HITS which works really well in WWW, does not outperform simpler algorithms used in this online community which confirms that structural differences may be the reason why complex algorithms may not work well in other network structures. |
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| + | A simulation network model was created in which users make the best of their time by being more selective in choosing questions that are challenging to them yet they are still capable of answering. Analysis on this network showed that ExpertiseRank and Z score outperforms others especially HITS. This shows that performance of the expertise ranking algorithms depends highly on the dynamics of the communities. | ||
| + | Expertise ranking algorithms may perform different in different structured networks therefore understanding the structural characteristics of network makes significant differences in the performance of these algorithms. | ||
| − | [[RelatedPaper:: | + | A similar work is [[RelatedPaper::Littlepage et al]] and another one that works on emails is [[RelatedPaper::Dom et al, DMKD 2003]]. |
Latest revision as of 21:30, 1 April 2011
Citation
Jun Zhang, Mark S. Ackerman, and Lada Adamic. 2007. Expertise networks in online communities: structure and algorithms. In Proceedings of the 16th international conference on World Wide Web (WWW '07). ACM, New York, NY, USA, 221-230.
Online version
Summary
The aim of this paper is to identify users with high expertise within online expertise-sharing communities. This expertise finding system uses graph-based algorithms on social networks within the community.
They created a post-reply network in which each user is represented as a node and a directed edge is created from each user who started the post to other users who replied to it. The prestige measure of this network is highly correlated with a user's expertise due to the way the network is constructed. Therefore this network is called community expertise network (CEN).
Network Characteristics
The authors experimented on the Java Forum which is a large online help-seeking community. Before testing the algorithms they did several analysis to characterize the network. Below are the performed analysis and their results
- The Bow tie structure analysis : More than half of the users only asks questions. 13% only answers and 12% both answers and asks.
- Degree distribution analysis : The majority of users answers only a few questions but few active users answers a lot of questions.
- Degree correlation analysis : Top repliers answer questions for everyone but less expert users do not reply to high expert users.
It is important to note that these characteristics are different from WWW graphs.
Expertise Ranking Algorithms
- Simple statistical measures : Just counting the number of replies or counting the number of users helped to calculate the score of expertise of a user.
- Z-score : A measure that combines one's asking and replying patterns.
- Expertise Rank Algorithm : PageRank like algorithm which uses not only count of users helped but also whom one helped.
- HITS Authority : Similar to HITS algorithm where good hub is a user who is helped by many experts and good authority is a user who helps many good hubs.
In experiments the authors used Spearman's Rho and Kendall's Tau measures to understand the correlations between these ranking algorithms and the human-assigned ratings. It has been observed that they are highly correlated which means that structural information can be used to identify experts in online communities.
It has been also observed that algorithms like PageRank and HITS which works really well in WWW, does not outperform simpler algorithms used in this online community which confirms that structural differences may be the reason why complex algorithms may not work well in other network structures.
A simulation network model was created in which users make the best of their time by being more selective in choosing questions that are challenging to them yet they are still capable of answering. Analysis on this network showed that ExpertiseRank and Z score outperforms others especially HITS. This shows that performance of the expertise ranking algorithms depends highly on the dynamics of the communities.
Expertise ranking algorithms may perform different in different structured networks therefore understanding the structural characteristics of network makes significant differences in the performance of these algorithms.
A similar work is Littlepage et al and another one that works on emails is Dom et al, DMKD 2003.
