Code and Software

Graph Random Neural Features

Graph Random Neural Features (GRNF) is an embedding method from graph-structured data to real vectors based on a family of graph neural networks. GRNF can be used within traditional processing methods or as a training-free input layer of a graph neural network. The theoretical guarantees that accompany GRNF ensure that the considered graph distance is metric, hence allowing to distinguish any pair of non-isomorphic graphs, and that GRNF approximately preserves its metric structure.

Please, refer to these papers for more details: zambon2020graph, zambon2019graph.

The GitHub repository provides a Spektral (TensorFlow) implementation:

from tensorflow.keras.layers import Input, Dense
from tensorflow.keras.models import Model
from grnf.tf import GraphRandomNeuralFeatures
X_in = Input(shape=(N, F))
A_in = Input(shape=(N, N))
psi = GraphRandomNeuralFeatures(64, activation="relu")([X_in, A_in])
output = Dense(1)(psi)
model = Model(inputs=[X_in, A_in], outputs=output)

and a PyTorch Geometric one:

from grnf.torch import GraphRandomNeuralFeatures
grnf = GraphRandomNeuralFeatures(64)
z = grnf(data)

CDG: Change Detection in a sequence of Graphs.

This is the reference code for most of my publications. It is an integrated collection of tools for performing change detection. For further details please visit the GitHub repository.

Package

The code is written in python3. In the package you will find following folders

  • cdg/graph interface for datasets of graphs, distances and kernels.
  • cdg/embedding several types of vector and manifold representations of graphs, such as, dissimilarity representation and manifold embeddings, like [zambon2018anomaly].
  • cdg/changedetection tests for change detection, like [zambon2018concept, zambon2019change].
  • cdg/utils utilities for the module.
  • cdg/simulation utilities for running repeated experiments.

Tutorial

I have set up a notebook on GitHub for you: tutorial.ipynb. Here a snippet of code to perform a change-detection test on a sequence x:

from cdg.changedetection import GaussianCusum
cdt = GaussianCusum()
cdt.fit(x[:N_train])
y = cdt.predict(x[N_train:])

CPM for graph sequences

Code replicating the experiments of paper zambon2019change. The code, availabel on GitHub.