G2A2: An Automated Graph Generator with Attributes and Anomalies

TL;DR

G2A2 is an automated graph generator that creates realistic dynamic attributed graphs with anomalies, aiding research by providing synthetic datasets with ground truth for privacy-sensitive applications.

Contribution

It introduces a novel probabilistic and deep generative framework for producing dynamic attributed graphs with realistic anomalies, addressing data scarcity and privacy issues.

Findings

G2A2 outperforms Kronecker graph generation by reducing MMD distance by up to 6x.

The generator produces graphs with realistic attributes and anomalies.

Evaluation shows high similarity to real-world graphs.

Abstract

Many data-mining applications use dynamic attributed graphs to represent relational information; but due to security and privacy concerns, there is a dearth of available datasets that can be represented as dynamic attributed graphs. Even when such datasets are available, they do not have ground truth that can be used to train deep-learning models. Thus, we present G2A2, an automated graph generator with attributes and anomalies, which encompasses (1) probabilistic models to generate a dynamic bipartite graph, representing time-evolving connections between two independent sets of entities, (2) realistic injection of anomalies using a novel algorithm that captures the general properties of graph anomalies across domains, and (3) a deep generative model to produce realistic attributes, learned from an existing real-world dataset. Using the maximum mean discrepancy (MMD) metric to evaluate the realism of a G2A2-generated graph against three real-world graphs, G2A2 outperforms Kronecker graph generation by reducing the MMD distance by up to six-fold (6x).