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https://github.com/vahancho/graphene

An abstract graph C++ library
https://github.com/vahancho/graphene

algorithm cpp cpp11 cpp17 dejkstra geodesy graph graphen hamburg map navigation road-network roadmap shortest-path shortest-paths templates

Last synced: 23 days ago
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An abstract graph C++ library

Awesome Lists containing this project

README 

        
- [The Graphene](#the-graphene)

  - [Prerequisites](#prerequisites)

  - [Shortest paths](#shortest-paths)

  - [API and usage](#api-and-usage)

  - [Build and test](#build-and-test)

  - [Examples](#examples)

    - [Hamburger road network](#hamburger-road-network)

      - [The Hamburger road network](#the-hamburger-road-network)

      - [The shortest path between two streets](#the-shortest-path-between-two-streets)

    - [California road network](#california-road-network)

- [See also](#see-also)



# The Graphene



`Graphene` is a header-only library that implements an abstract graph. The class template allows

creation of graphs of any types of nodes both directed and undirected with

the custom weight function.



[![Latest release](https://img.shields.io/github/v/release/vahancho/graphene?include_prereleases)](https://github.com/vahancho/graphene/releases)

[![Build and test (CMake)](https://github.com/vahancho/graphene/actions/workflows/cmake.yml/badge.svg)](https://github.com/vahancho/graphene/actions/workflows/cmake.yml)

[![Code coverage](https://codecov.io/gh/vahancho/graphene/graph/badge.svg?token=YLMFIEZBGG)](https://codecov.io/gh/vahancho/graphene)



## Prerequisites



No special requirements except *C++17* compliant compiler. The class is tested with

*gcc* and *MSVC* compilers. In order to build and run unit tests

for this project you are required to have Google Test library installed on the system.

For more details see the CI badges or [GitHub Actions](https://github.com/vahancho/graphene/actions).



## Shortest paths



The library calculates shortest path between two nodes using the Dijkstra algorithm.

However the term "shortest" is an abstraction, because the distance between

to nodes is just a particular notion of the weight of the graph edge. `Graphene`

allows to define custom weight functions, so that the algorithm will calculate

not only the shortest path, but the path that corresponds to the minimal overall

"weight".



## API and usage



```cpp

#include "graphene.h"



// Declare a graph with nodes as integer values

Graphene graph;



graph.addNode(0);

graph.addNode(42);



graph.addEdge(0, 42);

graph.addEdge(1, 2);



```



Calculate the shortest path between two nodes



```cpp

#include "graphene.h"



// 1--2--5--8

//  \     \/

//   10---6---7

//

Graphene graph;



auto weightFunction = [](int x, int y) -> int {

    return std::abs(x - y);

};



graph.addEdge(1, 2);

graph.addEdge(2, 5);

graph.addEdge(5, 6);

graph.addEdge(5, 8);

graph.addEdge(8, 6);

graph.addEdge(1, 10);

graph.addEdge(10, 6);

graph.addEdge(6, 7);



path = graph.shortestPath(1, 6, weightFunction);

// path = {1, 2, 5, 6}

```



## Build and test



In order to build the project please use the following commands:



```bash

cmake -B build -DCMAKE_BUILD_TYPE=Release -DENABLE_TESTING=True

cmake --build build --config Release

```



To run the unit tests



```bash

ctest -C Release --verbose

```



## Examples



The `ca_roadmap` and `hh_roadmap` examples demonstrate how to use `Graphene` library

to create road maps and find shortest paths between two nodes.



### Hamburger road network



The Hamburger road network is an interactive sample application which uses the large dataset

[Road network Hamburg (INSPIRE)](https://data.europa.eu/data/datasets/19a39b3a-2d9e-4805-a5e6-56a5ca3ec8cb?locale=en)

provided by Behörde für Verkehr und Mobilitätswende (BVM)

[Data license Germany – attribution – Version 2.0](http://www.govdata.de/dl-de/by-2-0).

The data set is represented by a number of CSV files which we parse and extract

the streets network (geodetic coordinates and street names) of the city.



The `hh_roadmap` application's usage example is:



```bash

./hh_roadmap

```



The application prompts for the start and end street names and calculates the

shortest path between them as well as the distance. The resulting `KML` file

saves into the `/data/hh_roadmap_output.kml` file.



#### The Hamburger road network



Hamburg Road Network



#### The shortest path between two streets



The shortest path between two streets



### California road network



As an input we used the [California Road Network](https://users.cs.utah.edu/~lifeifei/SpatialDataset.htm)

which represented by two files: `/examples/data/nodes_lon_lat.txt` with the nodes'

geodetic coordinates and `/examples/data/edges.txt` file with nodes indexes and

distance between them.

Using this data we created an undirected graph and use `Graphene::shortestPaths()`

function to find all paths that connect a node with all others. The tool exports

the results as a [KML](https://en.wikipedia.org/wiki/Keyhole_Markup_Language) file

which can be loaded, for example, into Google Earth application for visualization.



The `ca_roadmap` application's usage example is:



```bash

./ca_roadmap 10000

```

where `10000` is the max. number of paths to export.



California Road Network



# See also



[California Road Network](https://users.cs.utah.edu/~lifeifei/SpatialDataset.htm)