The mirand algorithm learns continuous representations for nodes in any (un)directed, (un)weighted graph.

Basic Usage

Input

• Look at the sample dataset cora. If you want to experiment on different datasets, create a folder with name of your dataset.
• Two files are required to run and generate the embedding - edgelist file for structure graph and edgelist file for content graph
• Naming convention for link structure layer: <dataset_name>_struc.edgelist
• Naming convention for content/attribute layer: <dataset_name>_attr.edgelist

Example

To run mirand on Zachary's karate club network, execute the following command from the project home directory:
python main.py --input-struc ../data/cora/cora_struc.edgelist --input-attr ../data/cora/cora_attr.edgelist --output ../data/cora/cora.embed --dataset=cora --dimensions=128

Options

You can check out the other options available to use with mirand using:
python src/main.py --help

Input

The supported input format is an edgelist:

node1_id_int node2_id_int <weight_float, optional>

The graph is assumed to be undirected and unweighted by default. These options can be changed by setting the appropriate flags.

Output

The output file has n+1 lines for a graph with n vertices. The first line has the following format:

num_of_nodes dim_of_representation

The next n lines are as follows:

node_id dim1 dim2 ... dimd

where dim1, ... , dimd is the d-dimensional representation learned by mirand.