Algorithms overview

Graph analytics, also called network analysis, is the use of a graph-based approach to analyze highly connected data. It is a set of tools that helps us understand relationships between nodes and identify values or uncover insights within the data. Graph algorithms are a subset of those tools and they are used to solve problems related to graph theory in economics, aeronautics, physics, biology, mathematics, computer science etc. There is a vast amount of algorithms, and let’s learn more about the most important ones:

Most popular group of algorithms are graph traversals, centrality and shortest path algorithms. Through this section you are going to learn more about the most popular examples of such algorithms and how to use them in NetworkX. Here is an overview:

• Graph traversals

  • Breadth-first search
  • Depth-first search

• Centrality algorithms

  • PageRank
  • Betweenness centrality
  • Degree centrality
  • Closeness centrality
  • Katz centrality

• Shortest path algorithms

  • Dijkstra's algorithm
  • A* algorithm
  • Floyd-Warshall algorithm

• Community detection

  • Girvan-Newman algorithm

• Components

  • Weakly conected components algorithm (Union find)

Not fast enough? Find 100x faster algorithms here.

Learn more

There are many useful resources for NetworkX developers created by the Memgraph team. Besides that, Memgraph has its own graph algorithms library called MAGE with highly optimized algorithm implementations in C++.

Memgraph for NetworkX developers