cluster_fluid_communities | R Documentation |

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.

```
cluster_fluid_communities(graph, no.of.communities)
```

`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. |

`cluster_fluid_communities()`

returns a `communities()`

object, please see the `communities()`

manual page for details.

Ferran Parés

Parés F, Gasulla DG, et. al. (2018) Fluid Communities: A Competitive, Scalable and Diverse Community Detection Algorithm. In: Complex Networks & 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 `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()`

Community detection
`as_membership()`

,
`cluster_edge_betweenness()`

,
`cluster_fast_greedy()`

,
`cluster_infomap()`

,
`cluster_label_prop()`

,
`cluster_leading_eigen()`

,
`cluster_leiden()`

,
`cluster_louvain()`

,
`cluster_optimal()`

,
`cluster_spinglass()`

,
`cluster_walktrap()`

,
`compare()`

,
`groups()`

,
`make_clusters()`

,
`membership()`

,
`modularity.igraph()`

,
`plot_dendrogram()`

,
`split_join_distance()`

```
g <- make_graph("Zachary")
comms <- cluster_fluid_communities(g, 2)
```

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