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The *mirand* algorithm learns continuous representations for nodes in any (un)directed, (un)weighted graph. Paper accepted at ECAI-2020 (http://ecai2020.eu/papers/1648_paper.pdf)
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### Environment Set-up
**Use python version 2.7**
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- Clone the repository.
- Navigate to the base directory of mirand (the download location)
- Create a virtual environment using the following command:< br />
``virtualenv venv``< br / >
(If **virtualenv** package is not installed, please install using pip)
- Activate the environment using:< br />
``source venv/bin/activate``
- Install required python modules to run the code.< br />
``pip install -r requirements.txt``
Congratulations!! You are now setup to run the code.
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### Basic Usage
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#### Input
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- Look at the sample dataset cora (residing inside data directory). If you want to experiment on different datasets, create a folder with name of your dataset.
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- 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*
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#### Example
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To run **mirand** on *cora* network, execute the following command from **src** directory inside the project home path:< br />
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``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``
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#### Options
You can check out the other options available to use with *mirand* using:< br />
``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
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where dim1, ... , dimd is the *d* -dimensional representation learned by *mirand* .
