Graphmonster will take an edgelist, create a graph, and remove noise from it. It then uses Node2Vec to find node embeddings. It uses umap or t-sne for reducing dimensionality. In the resulting visualised graph, nodes are coloured by community (Infomap algorithm) and sized by degree.
python train.py <parameters>
Input
-f, --file, name of edgelist file with one space-separated edge per line (e.g. 0 2 or pig owl), default = edgelist.txt
Clustering
-k, --keep, max number of communities to keep, default = 16.
Node2Vec
-l, --length, walk length, default = 16.
-n, --num, number of walks per node, default = 10.
-w, --win, window for model, default = 10.
-p, --pparam, p-parameter (return), default = 1.
-q, --qparam, q-parameter (inout), default = 1.
python reduce.py <parameters>
umap (default)
-n, --nneigh, similar to perplexity, recommended 5-50 (must be >1), default = 4.
-m, --mind, minimum distance, emphasise local structure (low) or even distribution (high), recommended 0.001-0.5, default = 0.5.
--mtrc, metric (default = euclidean)
t-sne
--tsne, set this flag to use t-sne instead of umap.
-x, --perp, perplexity for t-sne, recommended 5-50, default = 5.
-i, --iters, number of iterations, default = 300 (min 250).
Start with low x and i and increase gradually to find sweet spot.
python tweak.py <parameters>
When running train.py, the files commlabels.txt and gm.csv will be created. The community info in the csv can be used, depending on the character of your data, to look up more info about the nodes as input to setting the labels in the txt. Running tweak.py will use the labels.
-s, --size, nodesize will be (log degree * size), default = 60.
-a, --alpha, alpha opacity of nodes, default = 0.6.
-d, --dark, darkmode graph
--svg, set this flag to also create an svg file
Files for the visualisation(gm.png/gm.svg) will be created.
Run the following command to install package dependencies:
pip install -r requirements.txt
Newer versions might work, but here is a working setup as of 2024:
- networkx 2.7.1
- node2vec 0.4.6
- scikit-learn 1.0.2
- umap-learn 0.5.5
- seaborn 0.13.2
- infomap 2.7.1