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Many data-mining applications use dynamic attributed graphs to representrelational information; but due to security and privacy concerns, there is adearth of available datasets that can be represented as dynamic attributedgraphs. Even when such datasets are available, they do not have ground truththat can be used to train deep-learning models. Thus, we present G2A2, anautomated graph generator with attributes and anomalies, which encompasses (1)probabilistic models to generate a dynamic bipartite graph, representingtime-evolving connections between two independent sets of entities, (2)realistic injection of anomalies using a novel algorithm that captures thegeneral properties of graph anomalies across domains, and (3) a deep generativemodel to produce realistic attributes, learned from an existing real-worlddataset. Using the maximum mean discrepancy (MMD) metric to evaluate therealism of a G2A2-generated graph against three real-world graphs, G2A2outperforms Kronecker graph generation by reducing the MMD distance by up tosix-fold (6x).

G2A2: An Automated Graph Generator with Attributes and Anomalies