fluid_communities

Asynchronous Fluid Communities detection algorithm.

This algorithm is based on the simple idea of fluids interacting and expanding in a graph environment. It is unique in that it allows specifying exactly the number of communities k to find.

The algorithm starts with k randomly placed fluids (seeds). Each fluid has a density that decreases as the community grows. Nodes iteratively update their community to match the fluid with the highest density in their neighborhood. The process completes when no node changes its community.

Example

import yog/community/fluid_communities

// Find exactly 4 communities
let options = fluid_communities.FluidOptions(
  target_communities: 4,
  max_iterations: 100,
  seed: Some(42)
)
let communities = fluid_communities.detect_with_options(graph, options)

References

Parés, F., et al. (2017). Fluid Communities: A Competitive, Scalable and Diverse Community Detection Algorithm.

Types

FluidOptions

</>

Options for the Fluid Communities algorithm.

pub type FluidOptions {
  FluidOptions(
    target_communities: Int,
    max_iterations: Int,
    seed: option.Option(Int),
  )
}

Constructors

```
FluidOptions(
  target_communities: Int,
  max_iterations: Int,
  seed: option.Option(Int),
)
```
Arguments

target_communities

Exact number of communities to partition the graph into.

max_iterations

Maximum number of propagation iterations.

seed

Seed for the random number generator (vital for consistent results due to random processing order and seed selection).

Values

default_options

</>

pub fn default_options() -> FluidOptions

Default options: target 2 communities, max 100 iterations, random seed.

detect

</>

pub fn detect(graph: model.Graph(n, e)) -> community.Communities

Detects k communities on an unweighted graph. Default k = 2.

detect_with_options

</>

pub fn detect_with_options(
  graph: model.Graph(n, e),
  options: FluidOptions,
) -> community.Communities

Detects communities using specific options.

detect_with_weights

</>

pub fn detect_with_weights(
  graph: model.Graph(n, e),
  options: FluidOptions,
  weight_fn: fn(e) -> Float,
) -> community.Communities

Detects communities using specific options and a given weight function.

Example

References

Constructors

Arguments