Finding paths through networks
Day 141 of 149
👉 Full deep-dive with code examples
The City Map Analogy
Think of a city:
- Intersections are points (nodes)
- Roads connect them (edges)
- Some roads are one-way (directed)
- Some roads are longer than others (weighted)
Graph algorithms help you navigate and understand these connections!
What Problems They Solve
Networks are everywhere:
- Social networks → Who knows who?
- Maps → How do I get from A to B?
- Internet → How does data travel?
- Recommendations → What else might you like?
Graph algorithms answer questions like:
- What's the shortest path?
- Are these two things connected?
- What's the most influential node?
- How do I visit everything efficiently?
The Main Algorithms
Finding paths:
- BFS → Explore layer by layer (shortest path in simple graphs)
- DFS → Explore as deep as possible first
- Dijkstra → Shortest path when roads have different lengths
Understanding structure:
- Connected Components → Which groups are connected?
- Topological Sort → What order to do things with dependencies?
A Simple Example
Finding friends-of-friends:
You → Alice → Bob → Carol
↓
David
BFS from You:
Level 1: Alice
Level 2: Bob, David
Level 3: Carol
Where They're Used
- Google Maps (shortest route)
- Facebook (friend suggestions)
- Package delivery routing
- Network troubleshooting
- Dependency resolution (installing software)
In One Sentence
Graph algorithms help you find paths, connections, and patterns in any network of connected things.
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