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Soft and Adaptive Aggregation of Heterogeneous Graphs with Heterogeneous Attributes

机译：具有异构属性的异构图的软自适应聚集

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摘要

In the enterprise context, people need to exploit, interpret and mainly visualize different types of interactions between heterogeneous objects. Graph model is an appropriate way to represent those interactions. Nodes represent the individuals or objects and edges represent the relationships between them. However, extracted graphs are in general heterogeneous and large sized which makes it difficult to visualize and to analyze easily. An adaptive aggregation operation is needed to have more understandable graphs in order to allow users discovering underlying information and hidden relationships between objects. Existing graph summarization approaches such as k-SNAP are carried out in homogeneous graphs where nodes are described by the same list of attributes that represent only one community. The aim of this work is to propose a general tool for graph aggregation which addresses both homogeneous and heterogeneous graphs. To do that, we develop a new soft and adaptive approach to aggregate heterogeneous graphs (i.e., composed of different node attributes and different relationship types) using the definition of Rough Set Theory (RST) combined with Formal Concept Analysis (FCA), the well known K-Medoids and the hierarchical clustering methods. Aggregated graphs are produced according to user-selected node attributes and relationships. To evaluate the quality of the obtained summaries, we propose two quality measures that evaluate respectively the similarity and the separability in groups based on the notion of common neighbor nodes. Experimental results demonstrate that our approach is effective for its ability to produce a high quality solution with relevant interpretations.
机译：在企业环境中，人们需要利用，解释并主要可视化异构对象之间的不同类型的交互。图模型是表示这些交互的一种合适方法。节点代表个体或对象，边代表它们之间的关系。但是，提取的图通常是异类的，并且尺寸很大，这使其难以可视化和分析。为了使用户发现基础信息和对象之间的隐藏关系，需要进行自适应聚合操作以具有更易理解的图形。现有的图汇总方法（例如k-SNAP）在同构图中执行，其中节点由仅代表一个社区的相同属性列表来描述。这项工作的目的是提出一种用于图聚合的通用工具，该工具可以处理同构图和异构图。为此，我们使用粗糙集理论（RST）的定义结合形式概念分析（FCA），开发了一种新的软自适应方法来聚合异构图（即，由不同的节点属性和不同的关系类型组成）已知的K-Medoids和分层聚类方法。根据用户选择的节点属性和关系生成聚合图。为了评估获得的摘要的质量，我们提出了两种质量度量，分别基于公共邻居节点的概念评估组中的相似性和可分离性。实验结果表明，我们的方法有效地产生了具有相关解释的高质量解决方案。

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