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Soft and Adaptive Aggregation of Heterogeneous Graphs with Heterogeneous Attributes

机译：异构属性的异质图的软和自适应聚集

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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：在企业背景下，人们需要利用，解释和主要可视化异构对象之间的不同类型的相互作用。图形模型是表示这些交互的合适方式。节点表示个人或对象，边缘表示它们之间的关系。然而，提取的图形通常是异质的和大型的，这使得难以想象和容易分析。需要一个自适应聚合操作来具有更大可理解的图形，以便允许用户发现底层信息和对象之间的隐藏关系。现有的图形摘要方法，例如k-snap在同质图中执行，其中节点由只表示仅一个社区的属性列表。这项工作的目的是提出一种用于图形聚合的一般工具，其解决了均匀和异构图。为此，我们使用粗糙集理论（RST）的定义与正式概念分析（FCA）相结合，开发一种新的软和自适应方法来聚合异构图（即，由不同节点属性和不同的关系类型组成），井已知的k-myoids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。为了评估所获得的摘要的质量，我们提出了两个质量措施，分别根据公共邻居节点的概念分别评估了组中的相似性和可分离性。实验结果表明，我们的方法对于生产具有相关解释的高质量解决方案的能力是有效的。

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