A graph, also known as a network in some contexts, is a mathematical concept used to represent a set of nodes or vertices connected by edges or links. In data science, a graph can represent a dataset and its relationships between variables, where the nodes are the rows in the dataset and the edges are the relationships between the variables.
Three key highlights of graphs in data science are:
- Graphs can be used to visualize complex relationships between variables in a dataset.
- Clustering algorithms can be applied to graphs to identify groups of related nodes or vertices.
- Graphs can be used as a basis for network analysis to understand patterns of interaction between entities.
If you want to learn more about graphs, check out the following references:
In plain English, applying the concept of graphs to business means representing complex relationships between different variables in a dataset to gain insights into patterns or clusters of data. By using clustering algorithms, businesses can identify groups of related variables or patterns of interaction between entities. This kind of analysis can help businesses make better decisions by identifying areas of strength and weakness and providing insights into customer behavior, market trends, and other important factors.
