---
title: "Graph Scoring Functions"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Graph Scoring Functions}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  warning = FALSE,
  comment = "#>"
)
```

```{r setup}
library(clustAnalytics)
```





The package includes efficient implementations of the clustering coefficient and transitivity for weighted networks introduced by \cite{clustcoeficient}. As they can be applied to weighted graphs in general and not only to their partition into communities, they are simply called with the graph as the only argument:
```{r}
data(karate, package="igraphdata")
weighted_clustering_coefficient(karate)
```

To be able to obtain the result for every community in the graph, we provide the function *apply_subgraphs*; which given a graph, a membership vector and a scalar function, applies the function to every community and returns the vector of results. In this case it works as follows:
```{r}
apply_subgraphs(karate, V(karate)$Faction, weighted_clustering_coefficient)
```


