Analysis of cluster structure in large-scale English Wikipedia category networks
|Analysis of cluster structure in large-scale English Wikipedia category networks|
|Author(s)||Klaysri T., Fenner T., Lachish O., Levene M., Papapetrou P.|
|Published in||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Keyword(s)||connected component, graph structure analysis, large-scale social network analysis, Wikipedia category network (Extra: Cluster structure, Connected component, Edge linking, Graph structure analysis, Graph structures, Number of clusters, Social media networks, Wikipedia, Semantics, Social networking (online), Graphic methods)|
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Analysis of cluster structure in large-scale English Wikipedia category networks is a 2013 conference paper written in English by Klaysri T., Fenner T., Lachish O., Levene M., Papapetrou P. and published in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics).
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.
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