**mcl**

This clustering method accepts a single parameter controlling the
granularity of the resulting clustering called *inflation*. Low inflation
leads to coarser clusterings, high inflation leads to fine-grained
clusterings. It is suggested to use a few values, for example 1.4, 2, and 6.

**mcxload**

The program mcxload constructs networks from various types of label input. The first is label or column format, where each line in a file contains two white-space separated labels, and optionally a third column containing an edge weight. The second is cluster or fingerprint format, where each line contains a number of labels, representing the set of neighbours or characters for a given entity.

**mcxarray**

The program mcxarray constructs networks from tabular data such as
provided by gene expression arrays. Either Pearson or Spearman correlation
can be used. The program can handle missing data in the form of empty
columns, *NA* values (not available/applicable) or *NaN* (not a
number). It is efficient, parallelized and can handle large data sets.

**mcx**

*query*

The **mcx** program in mode query. One main use is to vary
a cutoff below which edges are removed, emitting statistics on the resulting
thresholded graphs such as the number of components, the number of
singletons, the average and median node degrees, and the average and median
edge weights.
This program can be used for example to find a good correlation cutoff
for networks created using **mcxarray**.
It is similarly possible to gauge the same statistics when varying the
parameter *k* in the *k-NN* transform.
In this transform an edge is kept if it occurs in the *k* edges of highest weight
for both of its incident nodes.

**mcx**

*ctty*

The **mcx** program in mode ctty. It computes betweenness centrality
for all nodes in a network, a very compute-intensive task. The program uses
the efficient update algorithm by Ulrik Brandes, a clever node-wise
parallelizable algorithm. This mode can run on multiple machines, each
machine running multiple threads, and hence can make effective use of
available resources.

**mcx**

*diameter*

The **mcx** program in mode diameter. It computes the diameter of a
graph as well as the eccentricity of each node. This is also a
computationally intensive task, and this mode can also run on multiple
machines, each machine running multiple threads.

**mcx**

*clcf*

The **mcx** program in mode clcf. It computes the clustering
coefficient for each node in a network. This is not a computationally
intensive operation, and hence parallelism is not required.

**mcx**

*erdos*

The **mcx** program in mode erdos. It computes ensembles
of shortest simple (unweighted) paths between two nodes. It was written with
a focus on speed.

**clm**

*order*

The **clm** program in mode order. Given a set of input clusterings,
this program creates a reconciled fully nested set of output clusterings.
Additionally, clusters are reordered at all levels such that larger clusters
precede smaller clusters. It can output a tree structure that can
be converted to Newick format with mcxdump.

**clm**

*dist*

The **clm** program in mode dist. It computes distances
between clusterings, according to one of the *split/join*,
*variance of information*, or *Mirkin* metrics.

**clm**

*info*

The **clm** program in mode info. It outputs a simple numerical performance
criterion for a clustering. It rewards clusterings both for being granular
and for capturing many edges in the input graph. Its criterion lies in the
range `[0-1]` and achieves `1` only for the canonical clustering of a graph
that consists of pairwise disjoint internally completely connected subparts.
In addition, it is affected by differentiation among the edge weight.
It is not intended as an optimization criterion, but can be used
to detect trends and optionally to spot bad clusterings.

**mcxrand**

This program can generate random graphs using a uniform edge generation model. It can also shuffle an existing graph while preserving the node degree distribution.