Coding of Graphs with Application to Graph Anomaly Detection

04/06/2018
by   Anders Host-Madsen, et al.
0

This paper has dual aims. First is to develop practical universal coding methods for unlabeled graphs. Second is to use these for graph anomaly detection. The paper develops two coding methods for unlabeled graphs: one based on the degree distribution, the second based on the triangle distribution. It is shown that these are efficient for different types of random graphs, and on real-world graphs. These coding methods is then used for detecting anomalous graphs, based on structure alone. It is shown that anomalous graphs can be detected with high probability.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
10/07/2020

Anomaly Detection in Large Labeled Multi-Graph Databases

Within a large database G containing graphs with labeled nodes and direc...
research
02/13/2023

Deep Graph-Level Orthogonal Hypersphere Compression for Anomaly Detection

Graph-level anomaly detection aims to identify anomalous graphs from a c...
research
08/03/2023

Discriminative Graph-level Anomaly Detection via Dual-students-teacher Model

Different from the current node-level anomaly detection task, the goal o...
research
12/08/2021

On anti-stochastic properties of unlabeled graphs

We study vulnerability of a uniformly distributed random graph to an att...
research
04/16/2021

Holmes: An Efficient and Lightweight Semantic Based Anomalous Email Detector

Email threat is a serious issue for enterprise security, which consists ...
research
01/24/2018

A Theoretical Investigation of Graph Degree as an Unsupervised Normality Measure

For a graph representation of a dataset, a straightforward normality mea...
research
06/19/2023

Pattern Mining for Anomaly Detection in Graphs: Application to Fraud in Public Procurement

In the context of public procurement, several indicators called red flag...

Please sign up or login with your details

Forgot password? Click here to reset