Graph ML — From 0 to Hero
Graphs are important buildings that mannequin connections between entities in various fields, together with on-line social platforms, transport networks, and organic programs. By extracting significant options from these graphs, we achieve helpful insights and enhance the efficiency of machine studying algorithms.
In a earlier article, we confirmed characterize graphs utilizing NumPy and NetworkX. On this article, we are going to discover use NetworkX to extract important graph options at completely different ranges (nodes, edges, and the graph itself).
We’ll use Zachary’s Karate Club Network, accessible with NetworkX, as our instance graph (see details on the license). This well-known dataset represents the social community of a college karate membership and serves as a wonderful start line for understanding graph-based function extraction.
Earlier than diving into the small print, let’s outline some code that helps us show graphs. First, we introduce some helper capabilities to specify default visualization choices. Within the following code, you see two capabilities…