Skip to main content

SClump implemented in Python.

Project description

PySClump Build Status

A Python implementation of 'Spectral Clustering in Heterogeneous Information Networks' from AAAI, 2019.
This was heavily inspired by the original implementation in MATLAB.

Figure: A similarity matrix represented as a graph. Nodes are coloured according to their assigned cluster.

References

Li, Xiang and Kao, Ben and Ren, Zhaochun and Yin, Dawei. 'Spectral Clustering in Heterogeneous Information Networks'. Proceedings of the AAAI Conference on Artificial Intelligence: 4221-4228.

Installation

PySClump is available on PyPI! Install with:

pip install pysclump

PathSim

We provide PathSim as a similarity metric between pairs of nodes. However, PySClump works with any similarity metric! See the SClump section below.

from pysclump import PathSim
import numpy as np

type_lists = {
    'A': ['Mike', 'Jim', 'Mary', 'Bob', 'Ann'],
    'C': ['SIGMOD', 'VLDB', 'ICDE', 'KDD'],
    'V': ['Pasadena', 'Guwahati', 'Bangalore']
}

incidence_matrices = { 
   'AC': np.array([[2, 1, 0, 0], [50, 20, 0, 0], [2, 0, 1, 0], [2, 1, 0, 0], [0, 0, 1, 1]]),
   'VC': np.array([[3, 1, 1, 1], [1, 0, 0, 0], [2, 1, 0, 1]])
}

# Create PathSim instance.
ps = PathSim(type_lists, incidence_matrices)

# Get the similarity between two authors (indicated by type 'A').
ps.pathsim('Mike', 'Jim', metapath='ACA')

# Get the similarity matrix M for the metapath.
ps.compute_similarity_matrix(metapath='ACVCA')

SClump

Once we have the similarity matrices (PathSim shown here), running SClump is really simple.

from pysclump import SClump

# Construct similarity matrices.
similarity_matrices = {
    'ACA': pathsim.compute_similarity_matrix(metapath='ACA'),
    'ACVCA': pathsim.compute_similarity_matrix(metapath='ACVCA'),
}

# Create SClump instance.
sclump = SClump(similarity_matrices, num_clusters=2)

# Run the algorithm!
labels, learned_similarity_matrix, metapath_weights = sclump.run()

If we have n nodes to be clustered into k clusters, labels is a n-by-1 vector, with entries from 0 to (k - 1) indicating the cluster index assigned. learned_similarity_matrix is the n-by-n matrix S referenced in the paper, indicating node-to-node similarity.

The clusters themselves are assigned by k++-means clustering either on the similarity matrix directly, or on the Laplacian of the learned similarity matrix.

Acknowledgements

This would not have been possible without the assistance of my CS529 teammates at IIT Guwahati:

Credit for visualization is completely due to Ramyani Ghosh (ramyani-ghosh).

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

pysclump-0.0.4.tar.gz (6.2 kB view hashes)

Uploaded source

Built Distribution

pysclump-0.0.4-py3-none-any.whl (12.5 kB view hashes)

Uploaded py3

Supported by

AWS AWS Cloud computing Datadog Datadog Monitoring Facebook / Instagram Facebook / Instagram PSF Sponsor Fastly Fastly CDN Google Google Object Storage and Download Analytics Huawei Huawei PSF Sponsor Microsoft Microsoft PSF Sponsor NVIDIA NVIDIA PSF Sponsor Pingdom Pingdom Monitoring Salesforce Salesforce PSF Sponsor Sentry Sentry Error logging StatusPage StatusPage Status page