Many real-world datasets can be represented as attributed graphs that contain vertices, each of which is associated with a set of attribute values. Discovering clusters, or communities, which are structural patterns in these graphs, are one of the most important tasks in graph analysis. To perform the task, a number of algorithms have been proposed. Some of them detect clusters of particular topological properties, whereas some others discover them mainly based on attribute information. Also, most of the algorithms discover disjoint clusters only. As a result, they may not be able to detect more meaningful clusters hidden in the attributed graph. To do so more effectively, we propose an algorithm, called FSPGA, to discover fuzzy structural patterns for graph analytics. FSPGA performs the task of cluster discovery as a fuzzy-constrained optimization problem, which takes into consideration both the graph topology and attribute values. FSPGA has been tested with both synthetic and real-world graph datasets and is found to be efficient and effective at detecting clusters in attributed graphs. FSPGA is a promising fuzzy algorithm for structural pattern detection in attributed graphs.

中文翻译：

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发现图分析的模糊结构模式

许多现实世界的数据集可以表示为包含顶点的属性图，每个顶点都与一组属性值相关联。发现作为这些图中的结构模式的集群或社区是图分析中最重要的任务之一。为了执行任务，已经提出了许多算法。其中一些检测特定拓扑属性的集群，而另一些则主要基于属性信息来发现它们。此外，大多数算法只发现不相交的集群。结果，他们可能无法检测到隐藏在属性图中的更有意义的集群。为了更有效地做到这一点，我们提出了一种称为 FSPGA 的算法，以发现用于图形分析的模糊结构模式。FSPGA 将集群发现任务作为一个模糊约束优化问题来执行，它同时考虑了图拓扑和属性值。FSPGA 已经用合成图和真实世界的图数据集进行了测试，发现在检测属性图中的集群方面是高效的。FSPGA 是一种很有前途的模糊算法，用于属性图中的结构模式检测。