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SClump implemented in Python.

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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.


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.


PySClump is available on PyPI! Install with:

pip install pysclump


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.


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 =

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.


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).

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