Financial frauds cause billions of losses annually and yet it lacks efficient
approaches in detecting frauds considering user profile and their behaviors
simultaneously in social network . A social network forms a graph structure
whilst Graph neural networks (GNN), a promising research domain in Deep
Learning, can seamlessly process non-Euclidean graph data . In financial fraud
detection, the modus operandi of criminals can be identified by analyzing user
profile and their behaviors such as transaction, loaning etc. as well as their
social connectivity. Currently, most GNNs are incapable of selecting important
neighbors since the neighbors' edge attributes (i.e., behaviors) are ignored.
In this paper, we propose a novel behavior information aggregation network
(BIAN) to combine the user behaviors with other user features. Different from
its close "relatives" such as Graph Attention Networks (GAT) and Graph
Transformer Networks (GTN), it aggregates neighbors based on neighboring edge
attribute distribution, namely, user behaviors in financial social network. The
experimental results on a real-world large-scale financial social network
dataset, DGraph, show that BIAN obtains the 10.2% gain in AUROC comparing with
the State-Of-The-Art models.

本文提出了一种新的行为信息聚合网络（BIAN），它结合了用户的行为和其他用户特征，通过相邻边缘属性分布，即金融社交网络的用户行为，来聚合邻居，从而改善了现有图神经网络在金融欺诈检测方面的不足，实验结果表明，与现有的最先进模型相比，BIAN 在真实的大规模金融社交网络数据集 DGraph 上获得了 10.2% 的 AUROC 增益

基于行为信息聚合网络 (BIAN) 的大规模金融社交网络欺诈用户检测

Fraudulent User Detection Via Behavior Information Aggregation Network (BIAN) On Large-Scale Financial Social Network

Social connectivity is the key process that characterizes the structural
properties of social networks and in turn processes such as navigation,
influence or information diffusion. Since time, attention and cognition are
inelastic resources, humans should have a predefined strategy to manage their
social interactions over time. However, the limited observational length of
existing human interaction datasets, together with the bursty nature of dyadic
communications have hampered the observation of tie dynamics in social
networks. Here we develop a method for the detection of tie
activation/deactivation, and apply it to a large longitudinal, cross-sectional
communication dataset ($\approx$ 19 months, $\approx$ 20 million people).
Contrary to the perception of ever-growing connectivity, we observe that
individuals exhibit a finite communication capacity, which limits the number of
ties they can maintain active. In particular we find that men have an overall
higher communication capacity than women and that this capacity decreases
gradually for both sexes over the lifespan of individuals (16-70 years). We are
then able to separate communication capacity from communication activity,
revealing a diverse range of tie activation patterns, from stable to
exploratory. We find that, in simulation, individuals exhibiting exploratory
strategies display longer time to receive information spreading in the network
those individuals with stable strategies. Our principled method to determine
the communication capacity of an individual allows us to quantify how
strategies for human interaction shape the dynamical evolution of social
networks.

通过开发一种检测关系激活 / 失活的方法并将其应用于一个大型的纵向交流数据集，研究表明相比于个体不断增长的互联程度的看法，人们的交流能力是有限的，尤其是随着时间的流逝。男性的通信能力总体上高于女性，且男女通信能力都会随着年龄的增长而逐渐降低，同时该方法也揭示了从稳定到探索性的各种关系激活模式以及这些模式对社交网络的动态演化造成的影响。