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首页> 外文期刊>International Journal of Geographical Information Science >Analysis of collaboration networks in OpenStreetMap through weighted social multigraph mining
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Analysis of collaboration networks in OpenStreetMap through weighted social multigraph mining

机译:通过加权社交多图挖掘分析OpenStreetMap中的协作网络

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This paper aims to qualify the behaviour of contributors to OpenStreetMap (OSM), a volunteered geographic information (VGI) project, through a multigraph approach. The main purpose is to reproduce contributor's interactions in a more comprehensive way. First, we define a multigraph that combines existing spatial collaboration networks from the literature with new graphs that illustrate collaboration based on specific aspects of the VGI modes of contribution through semantics, geometry and topology. Indeed, the ways that contributors interact with one another through editing, completion, or even consumption may provide additional information on each user's operation mode and therefore, on the quality of the contributed data. Social collaborations drawn from indirect criteria - for example, comparisons between contributors' activity areas - can also be contemplated under another network. Second, the resulting multigraph is analysed using data mining approaches to characterise individuals and identify behavioural groups. The implementation of a multiplex network based on an OSM data sample and an initial analysis make it possible to identify useful behaviours for data qualification. The initial results characterise some contributors as pioneers, moderators and truthful contributors, according to their special roles in the graphs. Mapping elements that include these contributors' participation are likely to be reliable data
机译:本文旨在通过多图方法对OpenStreetMap(OSM)(一个自愿性地理信息(VGI)项目)的贡献者的行为进行限定。主要目的是以更全面的方式重现贡献者的互动。首先,我们定义了一个多图,该多图将文献中的现有空间协作网络与新图结合起来,这些新图基于VGI贡献模式的特定方面通过语义,几何和拓扑来说明协作。实际上,贡献者通过编辑,完成甚至使用的方式彼此交互的方式可能会提供有关每个用户的操作模式以及所贡献数据质量的其他信息。从间接标准(例如,贡献者活动区域之间的比较)得出的社会合作也可以在另一个网络下进行。其次,使用数据挖掘方法对所得的多图进行分析,以表征个人并确定行为群体。基于OSM数据样本和初始分析的多路复用网络的实现使得可以识别有用的行为以进行数据鉴定。初始结果根据图表中的特殊角色将某些贡献者表征为先驱,主持人和真实的贡献者。包括这些贡献者参与的映射元素可能是可靠的数据

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