# Concept Drift and Anomaly Detection in Graph Streams

## TL;DR

This paper introduces a general methodology for detecting concept drift and anomalies in graph streams by embedding graphs into a vector space and applying change detection, validated through theoretical proofs and experiments.

## Contribution

The paper proposes a novel, general approach for change detection in attributed graph streams without requiring vertex correspondence, supported by theoretical analysis and empirical validation.

## Key findings

- Effective detection of concept drift in graph streams.
- Outperforms baseline methods in biological and drawing graph datasets.
- Theoretical guarantees for the proposed change detection method.

## Abstract

Graph representations offer powerful and intuitive ways to describe data in a multitude of application domains. Here, we consider stochastic processes generating graphs and propose a methodology for detecting changes in stationarity of such processes. The methodology is general and considers a process generating attributed graphs with a variable number of vertices/edges, without the need to assume one-to-one correspondence between vertices at different time steps. The methodology acts by embedding every graph of the stream into a vector domain, where a conventional multivariate change detection procedure can be easily applied. We ground the soundness of our proposal by proving several theoretical results. In addition, we provide a specific implementation of the methodology and evaluate its effectiveness on several detection problems involving attributed graphs representing biological molecules and drawings. Experimental results are contrasted with respect to suitable baseline methods, demonstrating the effectiveness of our approach.