outgraph 1.0.0

Outlier detection algorithm for graph datasets

outgraph

outgraph is a simple outlier detection algorithm for graph datasets. Given a list of graphs, it uses Mahalanobis distance detect which graphs are outliers based on either their topology or node attributes.

Note: outgraph only works for datasets where each graph has an equal number of nodes.

Installation

You can install outgraph with pip:

$ pip install outgraph

How it Works

Unlike most approaches to graph outlier detection, outgraph does not use machine learning. Instead, each graph is converted into a vector representation using one of three available methods:

1. Averaging the node feature/attribute vectors
2. Flattening the adjacency matrix
3. A concatenation of 1 and 2

Then, the Mahalanobis distance between each vector and the distribution of vectors is calculated. Lastly, a Chi-Squared distribution is used to model the distribution of distances and identify the distances outside a cutoff threshold (e.g. p < 0.05).

This approach is based off this article.

Usage

Each graph in your dataset needs to be an instance of outgraph.Graph. This object has two parameters, node_attrs and adjacency_matrix –– both numpy arrays where the indices correspond to nodes. Example:

import numpy as np
from outgraph import Graph

node_attrs = np.array([[-1], [0], [1]])
adj_matrix = np.array([[1, 1, 0],
                       [1, 1, 1],
                       [0, 1, 1]])
graph = Graph(node_attrs, adj_matrix)

Once you have a list of Graph objects, simply submit them into outgraph.detect_outliers:

from outgraph import Graph, detect_outliers

graphs = [Graph(), ...]
outliers, indices = detect_outliers(graphs, method=1, p_value=0.05)

Notice the method and p_value parameters. The method parameter is an integer between 1 and 3 that corresponds to one of the three graph vectorization methods described in the  section. p_value is the outlier cutoff threshold.