What is graphs?

This repository contains custom implementations of two fundamental graph algorithms: Dijkstra's algorithm and PageRank. In plain terms, it's a toolkit for solving two common problems: finding the shortest route between two points in a network, and figuring out which nodes (or points) in that network are most important or central. Dijkstra's algorithm answers questions like "what's the fastest route from my starting point to any other point in this network?" It's useful whenever you need optimal paths, think GPS navigation, network routing, or finding the cheapest way to get from one place to another. PageRank, on the other hand, measures importance by analyzing the structure of connections. Originally used by Google to rank web pages, it works by assuming that nodes pointed to by many important nodes are themselves important, a recursive idea that surfaces the most central or influential points in a network. The project builds a custom Graph class (a data structure for representing networks) that implements both algorithms from scratch, rather than relying on an existing library. The README mentions that this custom implementation can be faster than NetworkX (a popular Python graphs library, especially for PageRank calculations. This would be useful for someone building a system that needs to analyze networks repeatedly or work with very large graphs where speed matters. The intended audience is likely students learning graph algorithms, engineers building recommendation or ranking systems, or developers who need graph analysis but want the performance benefits or simplicity of a lightweight, focused implementation rather than a heavy general-purpose library. The code is relatively simple since it's written in Python and includes working implementations of these classical algorithms.