#ReFeX# An implementation of Recursive Feature Extraction (ReFeX).

Described in the paper "It's Who You Know: Graph Mining using Recursive Structural Features" by Henderson <i>et al.</i>

Link to the <a href="http://dl.acm.org/citation.cfm?id=2020512">paper</a>.

<b>Disclaimer:</b> I have implemented this paper purely for research, educational experimentation and non-profit use. Hence doesn't come with any guarantees. Please contact the authors for any other purpose.

Running the Code:

Requires python 2.7.
Install package dependencies using pip install -r requirements.txt
Run the code using python refex.py
Run python refex.py -h for help and other program options.
Input graph (directed graphs are supported) is specified with comma separated edge information in each line.
- Line 1,2,3 in the input graph depicts source, destination, edge-weight.
The epsilon equitable partition (eEP) [1] based features are also supported. The -r/--rider flag enables this.
- The multiple eEPs are expected in a directory specified using -rd/--rider-dir.
- Each line of the eEP file represents a cell/block of the partition, the member nodes of the cell are separated with space.
- We compute recursive (sum and means) egonet features for eEP based primitive features.
The test/resources directory has examples of sample input graph and epsilon equitable partitions.
This code might (potentially) differ from the original paper, please refer to the inline text comments for more details.
The list of primitive features and their nomenclature is captured from the original code implementation provided by the authors. We are really grateful to them for sharing the code.

[1] Pratik V Gupte and Balaraman Ravindran. "Scalable Positional Analysis for Studying Evolution of Nodes in Networks." At SIAM Data Mining Workshop on Mining Networks and Graphs: A Big Data Analytic Challenge, Philadelphia, USA, 2014.

Refex

Install / Use

README