Frequent subgraph mining based collaboration pattern analysis for wikipedia

Abstract

Online knowledge collaborations, where distributed members without hierarchies self-organize themselves to create valuable contents, are prevalent in many open production systems such as Wikipedia, GitHub and social networks. While many existing studies from network science have been brought to analyze the general interactive behavior patterns embedded in these systems, how the collaborations influence the achievement outcomes has not been thoroughly investigated. In this paper, we mine the collaboration patterns from a micro perspective to deeply understand the relationships between the collaboration among participants and the qualities of the Wikipedia articles. In particular, the subgraphs contained in the collaboration networks derived from the Wikipedia revision histories are taken as the fundamental units to analyze the collaboration diversities from the subgraph properties such as size and topology. In contrast to the predefined static motifs adopted by the previous works, the collaboration subgraphs are directly found from Wikipedia dataset by a frequent subgraph mining algorithm GRAMI, which is able to capture the real dynamic collaboration patterns. Moreover, the relationships between the co-authors in the subgraphs are also discriminated to further explore the collaboration patterns. The experiments exhibit the statistical properties of the collaboration subgraphs and the efficiency of them as the metrics for the article quality assessments. We conclude that a small group of editors with relative frequent fixed collaboration patterns contribute more to the excellent article quality than the professional extents of arbitrary individuals in the collaboration group. This discovery confirms the common insight about collaboration that many heads are always better than one and concretely suggests a potential explanation for the increasing prevalence and success of the online knowledge collaborations.