What is graph traversal in machine learning?

What is graph traversal in machine learning?

Graph traversal refers to the process of visiting nodes in a graph data structure, which is crucial in machine learning for tasks like recommendation systems and social network analysis.

Can you explain the different types of graph traversal methods?

Sure! The two main types are Depth-First Search (DFS) and Breadth-First Search (BFS). DFS explores as far as possible along a branch before backtracking, while BFS explores all neighbors at the present depth prior to moving on to nodes at the next depth level.

How are these traversal methods applied in machine learning?

In machine learning, DFS can be used for feature selection in decision trees, while BFS is often used in algorithms like PageRank for ranking web pages.

What are some real-world applications of graph traversal?

Real-world applications include social network analysis, web crawling, and recommendation systems, where understanding relationships between entities is key.