论文标题
多层知识图中的中心度度量
Centrality Measures in multi-layer Knowledge Graphs
论文作者
论文摘要
知识图在链接导致多层的不同数据方面起着核心作用。因此，它们被广泛用于大数据集成中，特别是用于连接来自不同域的数据。很少有研究研究了图表中的多个层如何影响为单用途网络（例如社交网络）开发的方法和算法。该手稿研究了多层对中心度度量的影响与单用光图相比。特别是（a）我们在受社交网络分析启发的随机图上开发了一个实验环境，以（b）评估两种不同的中心度度量 - 程度和中心性。提出的方法（C）表明，图结构和拓扑对存储其他数据的鲁棒性具有很大的影响。尽管对随机图的实验分析使我们能够进行一些基本的观察结果，但我们将（d）对对网络稳定性产生很大影响的特定图形结构进行其他研究提出建议。
Knowledge graphs play a central role for linking different data which leads to multiple layers. Thus, they are widely used in big data integration, especially for connecting data from different domains. Few studies have investigated the questions how multiple layers within graphs impact methods and algorithms developed for single-purpose networks, for example social networks. This manuscript investigates the impact of multiple layers on centrality measures compared to single-purpose graph. In particular, (a) we develop an experimental environment to (b) evaluate two different centrality measures - degree and betweenness centrality - on random graphs inspired by social network analysis: small-world and scale-free networks. The presented approach (c) shows that the graph structures and topology has a great impact on its robustness for additional data stored. Although the experimental analysis of random graphs allows us to make some basic observations we will (d) make suggestions for additional research on particular graph structures that have a great impact on the stability of networks.
