Benefiting from the message passing mechanism, Graph Neural Networks (GNNs) have been successful on flourish tasks over graph data. However, recent studies have shown that attackers can catastrophically degrade the performance of GNNs by maliciously modifying the graph structure. A straightforward solution to remedy this issue is to model the edge weights by learning a metric function between pairwise representations of two end nodes, which attempts to assign low weights to adversarial edges. The existing methods use either raw features or representations learned by supervised GNNs to model the edge weights. However, both strategies are faced with some immediate problems: raw features cannot represent various properties of nodes (e.g., structure information), and representations learned by supervised GNN may suffer from the poor performance of the classifier on the poisoned graph. We need representations that carry both feature information and as mush correct structure information as possible and are insensitive to structural perturbations. To this end, we propose an unsupervised pipeline, named STABLE, to optimize the graph structure. Finally, we input the well-refined graph into a downstream classifier. For this part, we design an advanced GCN that significantly enhances the robustness of vanilla GCN [24] without increasing the time complexity. Extensive experiments on four real-world graph benchmarks demonstrate that STABLE outperforms the state-of-the-art methods and successfully defends against various attacks.

The outbreak of COVID-19 burgeons newborn services on online platforms and simultaneously buoys multifarious online fraud activities. Due to the rapid technological and commercial innovation that opens up an ever-expanding set of products, the insufficient labeling data renders existing supervised or semi-supervised fraud detection models ineffective in these emerging services. However, the ever accumulated user behavioral data on online platforms might be helpful in improving the performance of fraud detection on newborn services. To this end, in this paper, we propose to pre-train user behavior sequences, which consist of orderly arranged actions, from the large-scale unlabeled data sources for online fraud detection. Recent studies illustrate accurate extraction of user intentions~(formed by consecutive actions) in behavioral sequences can propel improvements in the performance of online fraud detection. By anatomizing the characteristic of online fraud activities, we devise a model named UB-PTM that learns knowledge of fraud activities by three agent tasks at different granularities, i.e., action, intention, and sequence levels, from large-scale unlabeled data. Extensive experiments on three downstream transaction and user-level online fraud detection tasks demonstrate that our UB-PTM is able to outperform the state-of-the-art designing for specific tasks.

Though Graph Neural Networks (GNNs) have been successful for fraud detection tasks, they suffer from imbalanced labels due to limited fraud compared to the overall userbase. This paper attempts to resolve this label-imbalance problem for GNNs by maximizing the AUC (Area Under ROC Curve) metric since it is unbiased with label distribution. However, maximizing AUC on GNN for fraud detection tasks is intractable due to the potential polluted topological structure caused by intentional noisy edges generated by fraudsters. To alleviate this problem, we propose to decouple the AUC maximization process on GNN into a classifier parameter searching and an edge pruning policy searching, respectively. We propose a model named AO-GNN (Short for AUC-oriented GNN), to achieve AUC maximization on GNN under the aforementioned framework. In the proposed model, an AUC-oriented stochastic gradient is applied for classifier parameter searching, and an AUC-oriented reinforcement learning module supervised by a surrogate reward of AUC is devised for edge pruning policy searching. Experiments on three real-world datasets demonstrate that the proposed AO-GNN patently outperforms state-of-the-art baselines in not only AUC but also other general metrics, e.g. F1-macro, G-means.

Online credit payment fraud detection plays a critical role in financial institutions due to the growing volume of fraudulent transactions. Recently, researchers have shown an increased interest in capturing users’ dynamic and evolving fraudulent tendencies from their behavior sequences. However, most existing methodologies for sequential modeling overlook the intrinsic structure information of web pages. In this paper, we adopt multi-scale behavior sequence generated from different granularities of web page structures and propose a model named SAH-RNN to consume the multi-scale behavior sequence for online payment fraud detection. The SAH-RNN has stacked RNN layers in which upper layers modeling for compendious behaviors are updated less frequently and receive the summarized representations from lower layers. A dual attention is devised to capture the impacts on both sequential information within the same sequence and structural information among different granularity of web pages. Experimental results on a large-scale real-world transaction dataset from Alibaba show that our proposed model outperforms state-of-the-art models. The code is available at https://github.com/WangliLin/SAH-RNN.

Fraud transactions have been the major threats to the healthy development of e-commerce platforms, which not only damage the user experience but also disrupt the orderly operation of the market. User behavioral data is widely used to detect fraud transactions, and recent works show that accurate modeling of user intentions in behavioral sequences can propel further improvements on the performances. However, most existing methods treat each transaction as an independent data instance without considering the transaction-level interactions accessed by transaction attributes, e.g., information on remark, logistics, payment, device and etc., which may fail to achieve satisfactory results in more complex scenarios. In this paper, a novel heterogeneous transaction-intention network is devised to leverage the cross-interaction information over transactions and intentions, which consists of two types of nodes, namely transaction and intention nodes, and two types of edges, i.e., transaction-intention and transaction-transaction edges. Then we propose a graph neural method coined IHGAT(Intention-aware Heterogeneous Graph ATtention networks) that not only perceives sequence-like intentions, but also encodes the relationship among transactions. Extensive experiments on a real-world dataset of Alibaba platform show that our proposed algorithm outperforms state-of-the-art methods in both offline and online modes.

Graph-based fraud detection approaches have escalated lots of attention recently due to the abundant relational information of graph-structured data, which may be beneficial for the detection of fraudsters. However, the GNN-based algorithms could fare poorly when the label distribution of nodes is heavily skewed, and it is common in sensitive areas such as financial fraud, etc. To remedy the class imbalance problem of graph-based fraud detection, we propose a Pick and Choose Graph Neural Network (PC-GNN for short) for imbalanced supervised learning on graphs. First, nodes and edges are picked with a devised label-balanced sampler to construct sub-graphs for mini-batch training. Next, for each node in the sub-graph, the neighbor candidates are chosen by a proposed neighborhood sampler. Finally, information from the selected neighbors and different relations are aggregated to obtain the final representation of a target node. Experiments on both benchmark and real-world graph-based fraud detection tasks demonstrate that PC-GNN apparently outperforms state-of-the-art baselines.