# Centrality algorithms overview

Result of the centrality algorithm gives answer to the question "What characterizes an important node?". The result of the centrality algorithm provides ranking which identifies important nodes. Common applications are identifying most influential user in social networks, key infrastructure nodes in urban networks, super-spreaders of disease, etc.

Most popular centrality algorithms are Degree centrality, Closeness centrality, Betweenness centrality, Katz centrality and PageRank centrality.

In this section you are going to learn about the most popular centrality algorithms:

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There are many graph algorithms libraries out there, with their own implementations of centrality algorithms. NetworkX's algorithms are written in Python, and there are many other libraries that offer faster C++ implementations, such as **MAGE**, a graph algorithms library developed by Memgraph team.