R: Community detection algorithm based on interacting fluids

cluster_fluid_communities {igraph} | R Documentation

## Community detection algorithm based on interacting fluids

### Description

The algorithm detects communities based on the simple idea of several fluids interacting in a non-homogeneous environment (the graph topology), expanding and contracting based on their interaction and density.

### Usage

    cluster_fluid_communities(graph, no.of.communities)

### Arguments

`graph` | The input graph. The graph must be simple and connected. Empty graphs are not supported as well as single vertex graphs. Edge directions are ignored. Weights are not considered.
`no.of.communities` | The number of communities to be found. Must be greater than 0 and fewer than number of vertices in the graph.

### Value

`cluster_fluid_communities` returns a `communities` object, please see the `communities` manual page for details.

### Author(s)

Ferran Parés

### References

Parés F, Gasulla DG, et. al. (2018) Fluid Communities: A Competitive, Scalable and Diverse Community Detection Algorithm. In: Complex Networks &amp; Their Applications VI: Proceedings of Complex Networks 2017 (The Sixth International Conference on Complex Networks and Their Applications), Springer, vol 689, p 229, doi: 10.1007/978-3-319-72150-7_19

### See Also

See `communities` for extracting the membership, modularity scores, etc. from the results.

Other community detection algorithms: `cluster_walktrap`, `cluster_spinglass`, `cluster_leading_eigen`, `cluster_edge_betweenness`, `cluster_fast_greedy`, `cluster_label_prop` `cluster_louvain`, `cluster_leiden`

### Examples

    g <- graph.famous("Zachary")
    comms <- cluster_fluid_communities(g, 2)

[Package igraph version 1.3.2 Index]