abojchevski / paican

Implementation of "Bayesian Robust Attributed Graph Clustering: Joint Learning of Partial Anomalies and Group Structure".

Home Page:https://www.kdd.in.tum.de/paican

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Paican

Tensorflow implementation of the method proposed in the paper: "Bayesian Robust Attributed Graph Clustering: Joint Learning of Partial Anomalies and Group Structure", Aleksandar Bojchevski and Stephan G√ľnnemann, AAAI 2018.

Installation

python setup.py install

Requirements

  • tensorflow (>=1.4, <=2.0)
  • sklearn (only for evaluation)

Note: If you are using tensorflow >=2.0 you can stull run the above code by replacing the tensorflow import with

import tensorflow.compat.v1 as tf
tf.disable_v2_behavior()

and changing tf.contrib.distributions to tf.distributions.

Data

Each of the dataset folders consists of the following files:

  • A.mtx - the adjacency matrix in scipy's sparse csr_matrix format
  • X.mtx - the attribute matrix in scipy's sparse csr_matrix format
  • feature_to_index.npy - a dictionary mapping a feature label to index (e.g. 'neurology' -> 5)
  • node_to_index.npy - a dictionary mapping a node label (e.g name of a person or paper ID) to index
  • z.npy - ground truth clusters if available
  • label_to_cluster.npy - a dictionary mapping a label (e.g. journal, party) to cluster index

Demo

  • See the notebook example.ipynb for a simple demo.
  • Visit our website for an interactive plot that shows the inferred clustering on a subset of the Amazon dataset.

Cite

Please cite our paper if you use this code in your own work.

About

Implementation of "Bayesian Robust Attributed Graph Clustering: Joint Learning of Partial Anomalies and Group Structure".

https://www.kdd.in.tum.de/paican

License:MIT License


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