Graph Summarization for Human-Understandable Visualization towards CVE Data Analysis

Abstract

Graph is one of the data structures used to represent various real-world data effectively. It is easy to analyze complex relationships between objects through graphs, and the structure of data can be understood at a glance through visualization. As the size of the data increases, the scale of the graph representing the data also increases. However, for large-scale graphs, not only is it difficult to analyze patterns, but it is also expensive in terms of storage space and computational speed. In this case, by applying a graph summary method that can extract only the core information of the graph, the aforementioned problem can be solved. This paper studies various graph summarization techniques and applied them to the CVE data. For effective analysis, we first converted the CVE data into a graph, then summarized it with graph embedding and clustering methods. By visualizing the results, we confirmed that we could successfully derive a brief view of the data through this process.