Graph-based fraud detection has heretofore received considerable attention.
Owning to the great success of Graph Neural Networks (GNNs), many approaches
adopting GNNs for fraud detection has been gaining momentum. However, most
existing methods are based on the strong inductive bias of homophily, which
indicates that the context neighbors tend to have same labels or similar
features. In real scenarios, fraudsters often engage in camouflage behaviors in
order to avoid detection system. Therefore, the homophilic assumption no longer
holds, which is known as the inconsistency problem. In this paper, we argue
that the performance degradation is mainly attributed to the inconsistency
between topology and attribute. To address this problem, we propose to
disentangle the fraud network into two views, each corresponding to topology
and attribute respectively. Then we propose a simple and effective method that
uses the attention mechanism to adaptively fuse two views which captures
data-specific preference. In addition, we further improve it by introducing
mutual information constraints for topology and attribute. To this end, we
propose a Disentangled Information Graph Neural Network (DIGNN) model, which
utilizes variational bounds to find an approximate solution to our proposed
optimization objective function. Extensive experiments demonstrate that our
model can significantly outperform stateof-the-art baselines on real-world
fraud detection datasets.

本文提出了一个基于 Graph Neural Networks 的欺诈检测方法，通过解耦拓扑和属性视图，并采用注意力机制自适应融合两个视图的方法，以解决现实场景中欺诈者采用伪装行为的问题，并在真实世界欺诈检测数据集上实现了显著的结果优于现有基线。

冲突和魔鬼：解开信息图神经网络在欺诈检测中的应用

The Devil is in the Conflict: Disentangled Information Graph Neural Networks for Fraud Detection

In this paper, we present a novel implicit glyph shape representation, which
models glyphs as shape primitives enclosed by quadratic curves, and naturally
enables generating glyph images at arbitrary high resolutions. Experiments on
font reconstruction and interpolation tasks verified that this structured
implicit representation is suitable for describing both structure and style
features of glyphs. Furthermore, based on the proposed representation, we
design a simple yet effective disentangled network for the challenging one-shot
font style transfer problem, and achieve the best results comparing to
state-of-the-art alternatives in both quantitative and qualitative comparisons.
Benefit from this representation, our generated glyphs have the potential to be
converted to vector fonts through post-processing, reducing the gap between
rasterized images and vector graphics. We hope this work can provide a powerful
tool for 2D shape analysis and synthesis, and inspire further exploitation in
implicit representations for 2D shape modeling.

该论文提出了一种新的隐式字形形状表示方法，基于此方法设计了一种简单而有效的解耦网络，用于挑战性的一次性字体样式转移问题，并取得了优于现有替代方案的结果，该表示方法具有将生成的字形转换为矢量字体的潜力，并能够为 2D 形状分析和合成提供强大的工具，鼓励进一步开发 2D 形状建模的隐式表示方法。