Default resolution parameter.
Default randomness parameter.
Resolution parameter.
Randomness parameter.
Random number generator.
Initializes a CPM clustering algorithm with a specified resolution parameter.
Resolution parameter
Returns the resolution parameter.
Resolution parameter
Sets the resolution parameter.
Resolution parameter
Calculates the quality of a clustering using the CPM quality function.
The CPM quality function is given by
1 / (2 * m) * sum(d(c[i], c[j]) * (a[i][j] - resolution * n[i] *
n[j])),
where a[i][j] is the weight of the edge between nodes i and j,
n[i] is the weight of node i, m is the total edge weight, and
resolution is the resolutionparameter. The function d(c[i], c[j])
equals 1 if nodes i and j belong to the same cluster and 0 otherwise.
The sum is taken over all pairs of nodes i and j.
Modularity can be expressed in terms of CPM by setting n[i] equal to
the total weight of the edges between node i and its neighbors and by
rescaling the resolution parameter by 2 * m.
Network
Clustering
Quality of the clustering
Removes a cluster from a clustering by merging the cluster with another cluster. If a cluster has no connections with other clusters, it cannot be removed.
Network
Clustering
Cluster to be removed
Cluster with which the cluster to be removed has been merged, or -1 if the cluster could not be removed
Removes small clusters from a clustering. Clusters are merged until each cluster contains at least a certain minimum number of nodes.
Network
Clustering
Minimum number of nodes per cluster
Boolean indicating whether any clusters have been removed
Removes small clusters from a clustering. Clusters are merged until each cluster has at least a certain minimum total node weight.
The total node weight of a cluster equals the sum of the weights of the nodes belonging to the cluster.
Network
Clustering
Minimum total node weight of a cluster
Boolean indicating whether any clusters have been removed
Initializes a local merging algorithm.
Random number generator
Initializes a local merging algorithm for a specified resolution parameter and randomness parameter.
Resolution parameter
Randomness parameter
Random number generator
Clones the algorithm.
Cloned algorithm
Returns the randomness parameter.
Randomness
Sets the randomness parameter.
Randomness
Finds a clustering of the nodes in a network using the local merging algorithm.
The local merging algorithm starts from a singleton partition. It performs a single iteration over the nodes in a network. Each node belonging to a singleton cluster is considered for merging with another cluster. This cluster is chosen randomly from all clusters that do not result in a decrease in the quality function. The larger the increase in the quality function, the more likely a cluster is to be chosen. The strength of this effect is determined by the randomness parameter. The higher the value of the randomness parameter, the stronger the randomness in the choice of a cluster. The lower the value of the randomness parameter, the more likely the cluster resulting in the largest increase in the quality function is to be chosen. A node is merged with a cluster only if both are sufficiently well connected to the rest of the network.
Network
Clustering
Constructs a local merging algorithm.
Generated using TypeDoc
Local merging algorithm.
The local merging algorithm starts from a singleton partition. It performs a single iteration over the nodes in a network. Each node belonging to a singleton cluster is considered for merging with another cluster. This cluster is chosen randomly from all clusters that do not result in a decrease in the quality function. The larger the increase in the quality function, the more likely a cluster is to be chosen. The strength of this effect is determined by the randomness parameter. The higher the value of the randomness parameter, the stronger the randomness in the choice of a cluster. The lower the value of the randomness parameter, the more likely the cluster resulting in the largest increase in the quality function is to be chosen. A node is merged with a cluster only if both are sufficiently well connected to the rest of the network.
The local merging algorithm is used in the cluster refinement phase of the LeidenAlgorithm.