Examples

Real-world examples demonstrating how to use Yog.FSharp for various graph problems.

Running Examples

All examples are literate F# scripts that you can run directly:

# Run an example script
dotnet fsi docs/examples/gps-navigation.fsx

Or reference in your F# Interactive session:

#load "docs/examples/gps-navigation.fsx"

The examples use literate programming - documentation is embedded in the .fsx files using (** and *) comment blocks.

Example	Description	Demonstrates
GPS Navigation with A*	Find the fastest route between locations using heuristic-based search.	A* algorithm, heuristics, route planning
Dijkstra's Shortest Path	Find the fastest delivery route in a weighted road network.	Dijkstra's algorithm, single-source distances
Bellman-Ford with Negative Weights	Shortest paths with negative weights and negative cycle detection.	Bellman-Ford, negative cycle detection
City Distance Matrix with Floyd-Warshall	Compute shortest distances between all pairs of cities in a network.	All-pairs shortest paths, distance matrices
Cave Path Counting	Count all valid paths through a cave system with complex visit rules.	Custom DFS, backtracking, state-space search

Example	Description	Demonstrates
The Seven Bridges of Königsberg	The classic problem that founded graph theory.	Eulerian paths/circuits, degree analysis
Task Scheduling with Topological Sort	Determine the correct execution order for tasks with dependencies.	Topological sorting on DAGs, cycle detection
Deterministic Task Ordering	Alphabetically earliest execution order for tasks with multiple valid orderings.	Lexicographical topological sort, stable resolution
Social Network Analysis with SCC	Find communities in a social network using strongly connected components.	Tarjan's & Kosaraju's SCC algorithms
Network Cable Layout Optimization	Find the minimum cost to connect all buildings using Kruskal's algorithm.	Minimum Spanning Trees (MST), cost optimization
Centrality Analysis	Identify the most important nodes using degree, closeness, betweenness, PageRank, and eigenvector centrality.	All 5 centrality measures
Bridges & Articulation Points	Find critical infrastructure whose failure disconnects the network.	Tarjan's bridge-finding algorithm
Clique Finding	Find tightly-knit groups using the Bron-Kerbosch algorithm.	Maximum clique, all maximal cliques, k-cliques
DAG Algorithms	Critical path analysis and transitive closure on a task dependency DAG.	Longest path, transitive closure, topological sort

Example	Description	Demonstrates
Job Matching with Maximum Flow	Solve a bipartite matching problem using maximum flow algorithms.	Edmonds-Karp, bipartite matching, network modeling
Network Bandwidth Allocation	Maximize throughput and identify bottlenecks in a computer network.	Edmonds-Karp, Max-Flow Min-Cut Theorem
Global Minimum Cut	Find the "natural" split point of a graph that disconnects it with minimum cost.	Stoer-Wagner algorithm, network reliability
Medical Residency Matching	Solve the stable marriage problem to match residents to hospitals.	Gale-Shapley algorithm, stable matching

Example	Description	Demonstrates
Complete Graphs	Generate \(K_n\) graphs where every node connects to every other.	`Classic.complete`
Cycle Graphs	Generate \(C_n\) graphs forming a single closed loop.	`Classic.cycle`
Path Graphs	Generate \(P_n\) graphs forming a linear chain.	`Classic.path`
Star Graphs	Generate \(S_n\) graphs with a central hub node.	`Classic.star`
Wheel Graphs	Generate \(W_n\) graphs by adding a hub to a cycle.	`Classic.wheel`
2D Grid Graphs	Generate rectangular lattice meshes.	`Classic.grid2D`
Bipartite Graphs	Generate complete bipartite graphs \(K_{m,n}\).	`Classic.completeBipartite`
Empty Graphs	Generate \(n\) isolated nodes with no edges.	`Classic.emptyGraph`
Binary Trees	Generate complete binary trees of a given depth.	`Classic.binaryTree`
The Petersen Graph	Generate the famous 10-node, 15-edge cubic graph.	`Classic.petersenGraph`

Example	Description	Demonstrates
Erdős-Rényi \(G(n,p)\) Graphs	Random graphs where each edge exists with probability \(p\).	`Random.erdosRenyiGnp`
Erdős-Rényi \(G(n,m)\) Graphs	Random graphs with exactly \(m\) edges added uniformly.	`Random.erdosRenyiGnm`
Barabási-Albert Networks	Scale-free networks generated via preferential attachment.	`Random.barabasiAlbert`
Watts-Strogatz Networks	Small-world networks with high clustering and short paths.	`Random.wattsStrogatz`
Random Spanning Trees	Uniformly random tree structure on \(n\) nodes.	`Random.randomTree`

Example	Description	Demonstrates
DOT Graph Rendering	Export graphs to Graphviz DOT format for professional visualization.	Yog.IO.Dot, visual customization, Graphviz
JSON Data Export	Export graph data to JSON for web APIs and frontend visualization.	Yog.IO.Json, indented serialization, data interchange
GraphML Serialization	Export and import graphs in GraphML format for Gephi, yEd, and Cytoscape.	Yog.IO.GraphML, round-trip, custom attributes
GDF Format Export	Export graphs to GDF format for Gephi and lightweight text-based interchange.	Yog.IO.Gdf, column-based format, custom attributes

Check the GitHub repository for the latest examples!