leiden

leiden

Detect communities in network data using the Leiden clustering algorithm. Adjust resolution, weights, and iterations for precise clustering. Supports directed/undirected graphs and customizable parameters for accurate analysis.

Instructions

Leiden clustering algorithm for community detection

Input Schema

Name	Required	Description	Default
`clustering_args`	No	Any further arguments to pass to the clustering algorithm.
`directed`	No	Whether to treat the graph as directed or undirected.
`flavor`	No	Which package's implementation to use.	igraph
`key_added`	No	`adata.obs` key under which to add the cluster labels.	leiden
`n_iterations`	No	How many iterations of the Leiden clustering algorithm to perform. -1 runs until optimal clustering.
`neighbors_key`	No	Use neighbors connectivities as adjacency. If specified, leiden looks .obsp[.uns[neighbors_key]['connectivities_key']] for connectivities.
`obsp`	No	Use .obsp[obsp] as adjacency. You can't specify both `obsp` and `neighbors_key` at the same time.
`random_state`	No	Change the initialization of the optimization.
`resolution`	No	A parameter value controlling the coarseness of the clustering. Higher values lead to more clusters.
`use_weights`	No	If `True`, edge weights from the graph are used in the computation (placing more emphasis on stronger edges).

Input Schema (JSON Schema)

{
  "description": "Input schema for the Leiden clustering algorithm.",
  "properties": {
    "clustering_args": {
      "anyOf": [
        {
          "additionalProperties": true,
          "type": "object"
        },
        {
          "type": "null"
        }
      ],
      "default": null,
      "description": "Any further arguments to pass to the clustering algorithm.",
      "title": "Clustering Args"
    },
    "directed": {
      "anyOf": [
        {
          "type": "boolean"
        },
        {
          "type": "null"
        }
      ],
      "default": null,
      "description": "Whether to treat the graph as directed or undirected.",
      "title": "Directed"
    },
    "flavor": {
      "default": "igraph",
      "description": "Which package's implementation to use.",
      "enum": [
        "leidenalg",
        "igraph"
      ],
      "title": "Flavor",
      "type": "string"
    },
    "key_added": {
      "default": "leiden",
      "description": "`adata.obs` key under which to add the cluster labels.",
      "title": "Key Added",
      "type": "string"
    },
    "n_iterations": {
      "default": -1,
      "description": "How many iterations of the Leiden clustering algorithm to perform. -1 runs until optimal clustering.",
      "title": "N Iterations",
      "type": "integer"
    },
    "neighbors_key": {
      "anyOf": [
        {
          "type": "string"
        },
        {
          "type": "null"
        }
      ],
      "default": null,
      "description": "Use neighbors connectivities as adjacency. If specified, leiden looks .obsp[.uns[neighbors_key]['connectivities_key']] for connectivities.",
      "title": "Neighbors Key"
    },
    "obsp": {
      "anyOf": [
        {
          "type": "string"
        },
        {
          "type": "null"
        }
      ],
      "default": null,
      "description": "Use .obsp[obsp] as adjacency. You can't specify both `obsp` and `neighbors_key` at the same time.",
      "title": "Obsp"
    },
    "random_state": {
      "default": 0,
      "description": "Change the initialization of the optimization.",
      "title": "Random State",
      "type": "integer"
    },
    "resolution": {
      "default": 1,
      "description": "A parameter value controlling the coarseness of the clustering. Higher values lead to more clusters.",
      "title": "Resolution",
      "type": "number"
    },
    "use_weights": {
      "default": true,
      "description": "If `True`, edge weights from the graph are used in the computation (placing more emphasis on stronger edges).",
      "title": "Use Weights",
      "type": "boolean"
    }
  },
  "title": "LeidenModel",
  "type": "object"
}

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Instructions

Input Schema

Input Schema (JSON Schema)

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