  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).
