A quick introduction to 10 basic graph algorithms with examples and visualisations. Graphs have become a powerful means of modelling and capturing data in real-world scenarios such as social media networks, web pages and links, and locations and routes in GPS. If you have a set of objects that are related to each other, then you can represent them using a graph. In this article, I will be briefly explaining 10 basic graph algorithms that become very useful for analysis and their applications.
A graph consists of a finite set of vertices or nodes and a set of edges connecting these vertices. Two vertices are said to be adjacent if they are connected to each other by the same edge.
Some basic definitions related to graphs are given below. You can refer to Figure 1 for examples.
Fig 1. Visualization of Terminology of Graphs (Image by Author)
Fig 2. Animation of BFS traversal of a graph (Image by Author)
Traversing or searching is one of the fundamental operations which can be performed on graphs. In breadth-first search (BFS), we start at a particular vertex and explore all of its neighbours at the present depth before moving on to the vertices in the next level. Unlike trees, graphs can contain cycles (a path where the first and last vertices are the same). Hence, we have to keep track of the visited vertices. When implementing BFS, we use a queue data structure.
Figure 2 denotes the animation of a BFS traversal of an example graph. Note how vertices are discovered (yellow) and get visited (red).
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The agenda of the talk included an introduction to 3D data, its applications and case studies, 3D data alignment and more.
I won’t go into detail about graph data structure, but I will summarise must-to-know graph algorithms to solve coding interview questions.
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