Mark Levene

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Mark Levene is an author.

Publications

Only those publications related to wikis are shown here.
Title Keyword(s) Published in Language DateThis property is a special property in this wiki. Abstract R C
Analysis of cluster structure in large-scale English Wikipedia category networks Connected component
Graph structure analysis
Large-scale social network analysis
Wikipedia category network
Lecture Notes in Computer Science English 2013 In this paper we propose a framework for analysing the structure of a large-scale social media network, a topic of significant recent interest. Our study is focused on the Wikipedia category network, where nodes correspond to Wikipedia categories and edges connect two nodes if the nodes share at least one common page within the Wikipedia network. Moreover, each edge is given a weight that corresponds to the number of pages shared between the two categories that it connects. We study the structure of category clusters within the three complete English Wikipedia category networks from 2010 to 2012. We observe that category clusters appear in the form of well-connected components that are naturally clustered together. For each dataset we obtain a graph, which we call the t-filtered category graph, by retaining just a single edge linking each pair of categories for which the weight of the edge exceeds some specified threshold t. Our framework exploits this graph structure and identifies connected components within the t-filtered category graph. We studied the large-scale structural properties of the three Wikipedia category networks using the proposed approach. We found that the number of categories, the number of clusters of size two, and the size of the largest cluster within the graph all appear to follow power laws in the threshold t. Furthermore, for each network we found the value of the threshold t for which increasing the threshold to t + 1 caused the "giant" largest cluster to diffuse into two or more smaller clusters of significant size and studied the semantics behind this diffusion. 0 0
How Long Do Wikipedia Editors Keep Active? Social Media
User Modelling
Behaviour Mining
Survival Analysis
WikiSym English August 2012 In this paper, we use the technique of survival analysis to investigate how long Wikipedia editors remain active in editing. Our results show that although the survival function of occasional editors roughly follows a lognormal distribution, the survival function of customary editors can be better described by a Weibull distribution (with the median lifetime of about 53 days). Furthermore, for customary editors, there are two critical phases (0-2 weeks and 8-20 weeks) when the hazard rate of becoming inactive increases. Finally, customary editors who are more active in editing are likely to keep active in editing for longer time. 0 0
How long do Wikipedia editors keep active? Behaviour mining
Social media
Survival analysis
User modelling
WikiSym 2012 English 2012 In this paper, we use the technique of survival analysis to investigate how long Wikipedia editors remain active in editing. Our results show that although the survival function of occasional editors roughly follows a lognormal distribution, the survival function of customary editors can be better described by a Weibull distribution (with the median lifetime of about 53 days). Furthermore, for customary editors, there are two critical phases (0-2 weeks and 8-20 weeks) when the hazard rate of becoming inactive increases. Finally, customary editors who are more active in editing are likely to keep active in editing for longer time. 0 0
Leave or stay: The departure dynamics of wikipedia editors Lecture Notes in Computer Science English 2012 In this paper, we investigate how Wikipedia editors leave the community, i.e., become inactive, from the following three aspects: (1) how long Wikipedia editors will stay active in editing; (2) which Wikipedia editors are likely to leave; and (3) what reasons would make Wikipedia editors leave. The statistical models built on Wikipedia edit log datasets provide insights about the sustainable growth of Wikipedia. 0 0