In graph-based applications, a common task is to pinpoint the most important or ``central'' vertex in a (directed or undirected) graph, or rank the vertices of a graph according to their importance. To this end, a plethora of so-called centrality measures have been proposed in the literature. Such measures assess which vertices in a graph are the most important ones by analyzing the structure of the underlying graph. A family of centrality measures that are suited for graph databases has been recently proposed by relying on the following simple principle: the importance of a vertex in a graph is relative to the number of ``relevant'' connected subgraphs, known as subgraph motifs, surrounding it; we refer to the members of this family as subgraph motif centrality measures. Although it has been shown that such measures enjoy several favourable properties, their absolute expressiveness remains largely unexplored. The goal of this work is to precisely characterize the absolute expressiveness of the family of subgraph motif centrality measures by considering both directed and undirected graphs. To this end, we characterize when an arbitrary centrality measure is a subgraph motif one, or a subgraph motif measure relative to the induced ranking. These characterizations provide us with technical tools that allows us to determine whether well-established centrality measures are subgraph motif. Such a classification, apart from being interesting in its own right, gives useful insights on the structural similarities and differences among existing centrality measures.
翻译:在基于图形的应用中,一个共同的任务是在(定向或非定向)图形中确定最重要的或“中央”的顶点,或按其重要性对图表的顶点进行排序。为此,文献中提出了大量所谓的中心点措施。这些措施通过分析底图的结构来评估图中哪些顶点是最重要的顶点。最近根据以下简单的原则提出了适合图表数据库的中心点措施:图表中顶点的重要性与“相关”的关联子层数量相对应,即周围的分层;为此,文献中提出了大量所谓的中心点措施。在文献中,这类措施评估了图中哪些顶点是最重要的,分析基本图的结构结构结构结构结构结构。这项工作的目的是通过考虑直接和非定向的图表来准确确定子层中心点的绝对明确性。至此,当我们把一个直线型的分层测量作为直线中心点时,我们把一个直线型的分级测量尺度与一个分级的分级点进行分级分析。我们把一个分级的分级测量结果与一个分级的分级的分级测量工具分开。