| Bipartite network|
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Bipartite network is included as keyword or extra keyword in 0 datasets, 0 tools and 5 publications.
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|Title||Author(s)||Published in||Language||DateThis property is a special property in this wiki.||Abstract||R||C|
|La connaissance est un réseau: Perspective sur l’organisation archivistique et encyclopédique||Martin Grandjean||Les Cahiers du Numérique||French||2014||Network analysis is not revolutionizing our objects of study, it revolutionizes the perspective of the researcher on the latter. Organized as a network, information becomes relational. It makes potentially possible the creation of new information, as with an encyclopedia which links between records weave a web which can be analyzed in terms of structural characteristics or with an archive directory which sees its hierarchy fundamentally altered by an index recomposing the information exchange network within a group of people. On the basis of two examples of management, conservation and knowledge enhancement tools, the online encyclopedia Wikipedia and the archives of the Intellectual Cooperation of the League of Nations, this paper discusses the relationship between the researcher and its object understood as a whole.
Abstract (french)L’analyse de réseau ne transforme pas nos objets d’étude, elle transforme le regard que le chercheur porte sur ceux-ci. Organisée en réseau, l’information devient relationnelle. Elle rend possible en puissance la création d’une nouvelle connaissance, à l’image d’une encyclopédie dont les liens entre les notices tissent une toile dont on peut analyser les caractéristiques structurelles ou d’un répertoire d’archives qui voit sa hiérarchie bouleversée par un index qui recompose le réseau d’échange d’information à l’intérieur d’un groupe de personnes. Sur la base de deux exemples d’outils de gestion, conservation et valorisation de la connaissance, l’encyclopédie en ligne Wikipédia et les archives de la coopération intellectuelle de la Société des Nations, cet article questionne le rapport entre le chercheur et son objet compris dans sa globalité. [Version preprint disponible].
|Link prediction in a bipartite network using Wikipedia revision information||Chang Y.-J.
|Proceedings - 2012 Conference on Technologies and Applications of Artificial Intelligence, TAAI 2012||English||2012||We consider the problem of link prediction in the bipartite network of Wikipedia. Bipartite stands for an important class in social networks, and many unipartite networks can be reinterpreted as bipartite networks when edges are modeled as vertices, such as co-authorship networks. While bipartite is the special case of general graphs, common link prediction function cannot predict the edge occurrence in bipartite graph without any specialization. In this paper, we formulate an undirected bipartite graph using the history revision information in Wikipedia. We adapt the topological features to the bipartite of Wikipedia, and apply a supervised learning approach to our link prediction formulation of the problem. We also compare the performance of link prediction model with different features.||0||0|
|Temporal motifs reveal the dynamics of editor interactions in Wikipedia||Jurgens D.
|ICWSM 2012 - Proceedings of the 6th International AAAI Conference on Weblogs and Social Media||English||2012||Wikipedia is a collaborative setting with both combative and cooperative editing. We propose a new method for investigating the types of editor interactions using a novel representation of Wikipedia's revision history as a temporal, bipartite network with multiple node and edge types for users and revisions. From this representation we identify significant author interactions as network motifs and show how the motif types capture important, diverse editing behaviors. Two experiments demonstrate the further benefit of motifs. First, we demonstrate significant performance improvement over a purely revision-based analysis in classifying pages as combative or cooperative page by using motifs; and second we use motifs as a basis for analyzing trends in the dynamics of editor behavior to explain Wikipedia's content growth. Copyright © 2012, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.||0||0|
|Hot off the Wiki: Dynamics, Practices, and Structures in Wikipedia’s Coverage of the Tōhoku Catastrophes||Brian Keegan
|WikiSym||English||2011||Wikipedia editors are uniquely motivated to collaborate around current and breaking news events. However, the speed, urgency, and intensity with which these collaborations unfold also impose a substantial burden on editors’ abilities to effectively coordinate tasks and process information. We analyze the patterns of activity on Wikipedia following the 2011 Tōhoku earthquake and tsunami to understand the dynamics of editor attention and participation, novel practices employed to collaborate on these articles, and the resulting coauthorship structures which emerge between editors and articles. Our findings have implications for supporting future coverage of breaking news articles, theorizing about motivations to participate in online community, and illuminating Wikipedia’s potential role in storing cultural memories of catastrophe.||0||0|
|Bipartite networks of Wikipedia's articles and authors: A meso-level approach||Rut Jesus
|WikiSym||English||2009||This exploratory study investigates the bipartite network of articles linked by common editors in Wikipedia, 'The Free Encyclopedia that Anyone Can Edit'. We use the articles in the categories (to depth three) of Physics and Philosophy and extract and focus on significant editors (at least 7 or 10 edits per each article). We construct a bipartite network, and from it, overlapping cliques of densely connected articles and editors. We cluster these densely connected cliques into larger modules to study examples of larger groups that display how volunteer editors flock around articles driven by interest, real-world controversies, or the result of coordination in WikiProjects. Our results confirm that topics aggregate editors; and show that highly coordinated efforts result in dense clusters. Copyright 2009 ACM.||0||1|