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)

### Environment Set-up

**Use python version 2.7**

- 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.
  

### Basic Usage

#### Input

- 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.
- 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 *cora* network, execute the following command from **src** directory inside the project home path:<br/>
	``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:<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

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