论文标题
G2A2：具有属性和异常的自动图生成器
G2A2: An Automated Graph Generator with Attributes and Anomalies
论文作者
论文摘要
许多数据挖掘应用程序使用动态归因图来表示关系信息；但是，由于安全和隐私问题，缺乏可用的数据集可以表示为动态归因图。即使有此类数据集可用，它们也没有可用于训练深度学习模型的基础真相。 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从现有的现实世界数据集中学到的现实属性。使用最大平均差异（MMD）度量标准来评估G2A2生成的图对三个现实图形的现实主义，G2A2通过将MMD距离降低到六倍（6倍）来优于Kronecker图生成Kronecker图。
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).
