Welcome to the latest post in the Graphs for Data Science substack. In this post, we explore the structure of networks using k-core decomposition, a unique way to probe the connectivity patterns of complex networks.
Real-world networks are typically large and hard to visualize, leading to the development of sophisticated methods and algorithms, such as degree correlations, network structure, and modularity, to explore their structure systematically. In this post, we continue this exploration by learning about k-core decomposition.
The k-core of a graph is the largest subgraph where every node has degree of at least k. The k-core can easily computed by recursively removing every node of degree less than k. In Python, this can done with:
