G2A2: An Automated Graph Generator with Attributes and Anomalies
This addresses a data scarcity problem for researchers and practitioners in data-mining applications using dynamic attributed graphs, but it is incremental as it builds on existing graph generation methods.
The paper tackles the lack of available dynamic attributed graph datasets with ground truth for training deep-learning models by presenting G2A2, an automated graph generator that reduces the maximum mean discrepancy distance by up to six-fold compared to Kronecker graph generation.
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).