connectivity-modifier 0.1.0b6

connectivity modifier

cm

Connectivity Modifier (CM) is a generic meta-method for community detection while ensuring a certain connectivity (minimum number of edges to remove to disconnect a community) on the output communities (clusters). To be more precise, suppose that you want to ensure that Leiden clusters must not be "easily-cut". For example, ensuring that none of the output clusters have connectivity below $\log_{10}(n)$, $n$ the size of any cluster, you can run CM paired with Leiden, and it will ensure that all output clusters have that minimum size cut. CM supports customizable requirements on the connectivity of the clusters. CM currently supports Leiden (CPM optimization), IKC, and Leiden (modularity optimization) out-of-the-box. After installing the necessary dependencies, users can simply run CM to obtain Leiden clusters with strong guarantees on connectivity.

Installation

Our software is provided via PyPI and supports at least Python 3.9.

pip3 install --pre connectivity-modifier # install prerelease version

Note that you must install VieCut as a dependency explicitly. More specifically, mincut the binary must be compiled and the path to mincut must be specified in the config file (explained below).

Say that you installed mincut as /foo/bar/mincut; then you want to create a config file in ~/.config/cm/config.toml and have something like this:

[tools]
ikc_path = "{project_root}/third_party/ikc.py" # {project_root} is a specific path resolving to the source code root
leiden_path = "" # this config option is currently unused
viecut_path = "/foo/bar/mincut" # viecut's path

Also feel free to use this alternative CMakeLists.txt file if having issues with compiling VieCut using their built-in CMakeLists (e.g. having issues with MPI, etc.).

If the main executable detects that cm.toml is in the current working directory, the cm.toml config file will have the highest priority instead.

After all this, try cm --help, and you should see something like this:

Usage: cm [OPTIONS]

  Connectivity-Modifier (CM). Take a network and cluster it ensuring cut
  validity

Options:
  -i, --input TEXT                [required]
[...]

Usage

Our main executable is provided as cm, and we list the options below:

-i, --input GRAPH_TSV

The input graph to be clustered, where graph.tsv is a tab-delimited edgelist, only including integer edge ids. Note that we follow the igraph convention, where we assume that the input node ids are continuous, and if not, dummy nodes are added.

-c, --clusterer [leiden|ikc|leiden_mod]

The clusterer to be paired with. If using with an existing clustering (-e), then the same clusterer must be used (see below). Otherwise, one must decide which clusterer should be used. The clusterers are:

• leiden: Leiden (leidenalg) with CPM optimization, must specify -g, --resolution later
• ikc: Iterative k-core, must specify -k later
• leiden_mod: Leiden with modularity optimization, no other parameters allowed to be specified

-e, --existing-clustering CLUSTERING_FILE

Specifies the starting clustering (in effect saving time for cm to reproduce the initial clustering) to be modified to have sufficient connectivity thresholds (c.f. -t). The file format is "native" to the clustering method. For example, for IKC, it is the default IKC csv output format. For Leiden, it is the Leiden output format (i.e., tab-delimited node_id cluster_id file).

-g, --resolution FLOAT, -k, --k INTEGER

The respective parameters for either Leiden(CPM) (-c leiden) or IKC (-c ikc). Only at most one should be specified, and for modularity optimization neither should be specified.

-o, --output OUTPUT_PREFIX

The output prefix. Two files will be produced, first the OUTPUT_PREFIX will have a file denoting the last cluster a node has been in, and {OUTPUT_PREFIX}.tree.json is a serialized tree denoting the history of the execution of the algorithm. See also converting the output to more parsable formats.

-t, --threshold TEXT

Threshold expression. cm guarantees that the output clustering all have clusters that are above a specific threshold. We list some examples for -t below:

# each line denotes a valid example for "-t"
2 # connectivity must > 2
0.1mcd # connectivity must > 0.1 MCD, MCD the minimum intra-cluster degree
0.1mcd+42 # linear combinations are allowed to some extent
1log10 # > log10 of size of cluster
99log10+0.0002mcd+1 # combinations like this are allowed

-d, --working-dir TEXT

Optional for throw-away runs; specifies where cm should store its temporary files. Prudently one should always specify different working directories for different runs under the same directory.

--ignore-trees & --ignore-smaller-than INTEGER

Allows CM to skip processing trees (those clusters with nodes equals number of edges plus one) and those clusters that are smaller than the specified number. These clusters will be skipped for processing but included in the output.

Example commands

# Leiden, CPM optimization (resolution = 0.1)
# BUT, the output clusters must satisfy global connectivity > 1 * log10(n), n the size of cluster
cm -i graph.tsv -c leiden -g 0.1 -t 1log10 -o leiden_clus.txt
cm2universal -g graph.tsv -i leiden_clus.txt -o leiden_clus.txt
# generates two files: leiden_clus.txt.before.json and leiden_clus.txt.after.json
# See also "Format Conversion"

# IKC, k = 10
# BUT, the output clusters must satisfy global connectivity > 0.1 * mcd, MCD the minimum intra-cluster degree among all nodes
# we additionally use an existing IKC clustering (ikc_output.csv) as the starting point to be modified
cm -i graph.tsv -c ikc -k 10 -t 0.1mcd -e ikc_output.csv -o ikc_clus.txt

Note that for serious runs, -d should also be specified to avoid overwriting temporary files.

Format Conversion

The default output of cm contains the entire history of the execution of the algorithm, i.e., not the true usual clustering format. This format allows preservation of much information, but often times for data analysis, only knowing the clustering before modifying the connectivity (i.e., as if just running the base method) and after modifying the connectivity is enough. These two sets of clusters can be obtained from cm using the specialized tool cm2universal (which comes included with the project):

# INPUT_GRAPH is the same INPUT_GRAPH
# CM_OUTPUT_PREFIX is the same output prefix of `cm`, i.e., `{CM_OUTPUT_PREFIX}.tree.json` and `CM_OUTPUT_PREFIX` are existing files
# CLUSTERS_OUTPUT_PREFIX is where you want the converted clusters
cm2universal -g INPUT_GRAPH -i CM_OUTPUT_PREFIX -o CLUSTERS_OUTPUT_PREFIX

Two files will be generated: {CLUSTERS_OUTPUT_PREFIX}.before.json and {CLUSTERS_OUTPUT_PREFIX}.after.json, containing the original and after clusters respectively. The json files use the so-called "universal" new-line delimited JSON format, looking like this:

{"label": "0", "nodes": [0, 3, 7, 9], "connectivity": 1}
{"label": "46", "nodes": [5765736, 4717164, 14154348, 3144303, 6290035, 3668596, 1571445, 2620022, 4717176], "connectivity": 2}

These files can be directly parsed (each line is a cluster, label the cluster name, nodes the node ids of that cluster, connectivity the edge connectivity) or can be paired with the data science tool Belinda.

Development

We use Poetry to manage our progress and follow the Poetry conventions. See below for some example commands:

poetry install # install networkit and co
poetry run pytest # run tests

Pseudocode

Assuming that we have an existing clustering method $f$ that takes in a graph and outputs a list of clusters, our algorithm roughly does the following:

1. Obtain clusters ${S_i}$ from $f$ if not provided in the input.
2. Initiate a stack $Q$ containing all above clusters (the stack meant that everything is done in a DFS fashion)
3. When $Q$ non-empty, take $S_i$ from $Q$. Do the following
   • Calculate connectivity $\lambda$ for $S_i$
     • Done via mincut -b -s -o [mincut_output_path] [input_graph_path] cactus (where mincut is provided by Viecut)
   • If $\lambda > t$ for $S_i$, where $t$ is as specified by the -t parameter, then $S_i$ is marked for output, and we never touch $S_i$ again
   • Otherwise. $S_i$ is split into $S_i^a$ and $S_i^b$ by the minimum cut, and $f(S_i^a)$ and $f(S_i^b)$ are added to $Q$.