Graph Anomaly Detection (GAD) has recently become a hot research spot due to
its practicability and theoretical value. Since GAD emphasizes the application
and the rarity of anomalous samples, enriching the varieties of its datasets is
a fundamental work. Thus, this paper present DGraph, a real-world dynamic graph
in the finance domain. DGraph overcomes many limitations of current GAD
datasets. It contains about 3M nodes, 4M dynamic edges, and 1M ground-truth
nodes. We provide a comprehensive observation of DGraph, revealing that
anomalous nodes and normal nodes generally have different structures, neighbor
distribution, and temporal dynamics. Moreover, it suggests that those unlabeled
nodes are also essential for detecting fraudsters. Furthermore, we conduct
extensive experiments on DGraph. Observation and experiments demonstrate that
DGraph is propulsive to advance GAD research and enable in-depth exploration of
anomalous nodes.

本研究介绍了一种基于金融领域的动态图表 DGraph，其中包含约 3M 个节点和 4M 个动态边缘，通过对 DGraph 的全面观察和实验证明，DGraph 对于推进图形异 常检测研究并深入探索异常节点非常有用，同时还表明未标记的节点对于检测欺诈者也是至关重要的。

DGraph: 大规模金融图谱异常检测数据集

DGraph: A Large-Scale Financial Dataset for Graph Anomaly Detection

Many online platforms have deployed anti-fraud systems to detect and prevent
fraudulent activities. However, there is usually a gap between the time that a
user commits a fraudulent action and the time that the user is suspended by the
platform. How to detect fraudsters in time is a challenging problem. Most of
the existing approaches adopt classifiers to predict fraudsters given their
activity sequences along time. The main drawback of classification models is
that the prediction results between consecutive timestamps are often
inconsistent. In this paper, we propose a survival analysis based fraud early
detection model, SAFE, which maps dynamic user activities to survival
probabilities that are guaranteed to be monotonically decreasing along time.
SAFE adopts recurrent neural network (RNN) to handle user activity sequences
and directly outputs hazard values at each timestamp, and then, survival
probability derived from hazard values is deployed to achieve consistent
predictions. Because we only observe the user suspended time instead of the
fraudulent activity time in the training data, we revise the loss function of
the regular survival model to achieve fraud early detection. Experimental
results on two real world datasets demonstrate that SAFE outperforms both the
survival analysis model and recurrent neural network model alone as well as
state-of-the-art fraud early detection approaches.

本文提出了一种基于生存分析的欺诈早期检测模型 (SAFE)，该模型采用递归神经网络 (RNN) 处理用户活动序列，并直接在每个时间戳上输出危险值，然后，从危险值衍生出生存概率，以实现一致的预测。实验结果表明 SAFE 在两个真实世界数据集上优于其他技术。