While scoring nodes in graphs to understand their importance (e.g., in terms of centrality) has been investigated for decades, comparing nodes in property graphs based on their properties has not, to our knowledge, yet been addressed. In this paper, we propose an approach to automatically extract comparison of nodes in property graphs, to support the interactive exploratory analysis of said graphs. We first present a way of devising comparison indicators using the context of nodes to be compared. Then, we formally define the problem of using these indicators to group the nodes so that the comparisons extracted are both significant and not straightforward. We propose various heuristics for solving this problem. Our tests on real property graph databases show that simple heuristics can be used to obtain insights within minutes while slower heuristics are needed to obtain insights of higher quality.

翻译：尽管通过评分节点以理解其在图中的重要性（例如，基于中心性）的研究已有数十年历史，但据我们所知，基于节点属性对属性图中的节点进行比较的问题尚未得到解决。本文提出一种自动提取属性图中节点比较的方法，以支持对该类图的交互式探索性分析。我们首先提出一种利用待比较节点的上下文设计比较指标的方式。随后，我们正式定义了利用这些指标对节点进行分组的问题，以确保提取的比较既具有显著性又非显而易见。针对该问题，我们提出了多种启发式算法。在真实属性图数据库上的测试表明，简单的启发式算法可在数分钟内获得洞察，而更耗时的启发式算法则能提供更高质量的洞察。