A Data Structure graph is a measurement tool applying the concepts of graph theory to data modeling and data architecture. For those familiar with using Data Modeling tools, there is an adage associated with creating new data models. “Try not to cross the lines. If you cross the lines, your ERD will be complex.” This “rule of thumb” can be found in Graph Theory. A planar graph is a graph that can be embedded in the plane, i.e., it can be drawn on the plane in such a way that its edges intersect only at their endpoints. In other words, it can be drawn in such a way that no edges cross each other. This mathematical reason for not crossing the lines is one of the many applications of graph theory to data modeling.
Graph Theory and Network Science overview. We will talk about some of the history of graph theory, how it evolved into network science, and its applications in the “real world”. Many terms will be defined including edge, node, size, density, cliques, connectedness, walks, trails, paths, cycles, centrality, influence, and entropy. Graph Clustering and general clustering techniques will get a brief overview.
ERD Data Modeling Fundamentals. Here we will create a few entities and relationships. The most fundamental types of data modeling will be covered in this class. For more details of data modeling other sources are described and outlined.
Converting an ERD to a Data Structure Graph. Using Dbeaver and some python code that will be handed out, we will reverse engineer an existing data model and create an input file for Gephi to use in the lab. For any of you who have existing data models you wish to review, this is the portion of the class where we will convert your data model into a data structure graph that can be analyzed with Gephi, Tulip, R and Python.