Graphs have been widely adopted to accomplish fraud detection tasks because of their inherently favorable structure to capture the intricate features in many complicated scenarios, especially in some modern e-commerce situations that have various relation attributes like transactions. These works tend to utilize the direct aggregate information about neighbor nodes of the target node or the aggregation of the neighbor information after the conditional filter and mostly use local information but ignore global information. However, in some cases, local abnormal points are detected in the global view, so global information is very important for fraud detection. In this article, we propose a local and global aware memory-based graph neural network for fraud detection (LGM-GNN). It first obtains the preliminary node embedding through relation-aware embedding and then interactively aggregates the local and global memory network to fuse and utilize the local and global information. Finally, the node embeddings of different levels are aggregated through the hierarchical information aggregator. Extensive experiments show our proposed LGM-GNN outperforms other SOAT methods on two real-world fraud detection datasets.

As a type of evolving-fuzzy system, the evolving-fuzzy-neuro (EFN) system uses the structure inspired by neural networks to determine its parameters (fuzzy sets and fuzzy rules), so EFN system can inherit the advantages of neural networks. However, for streaming data regression, EFN systems still have several drawbacks: 1) determining fuzzy sets is not robust to data sequence; 2) determining fuzzy rules is complex due to subspaces that can approximate to a Takagi-Sugeno-Kang (TSK) rule need to be obtained, and many parameters need to be optimized; 3) it is difficult to detect and adapt to changes in the data distribution, i.e., concept drift, if the output is a continuous variable. Hence, in this paper, a novel evolving-fuzzy-neuro system, called the topology learning-based fuzzy random neural network (TLFRNN), is proposed. In TLFRNN, an online topology learning algorithm is designed to self-organize each layer of TLFRNN. Different from current EFN systems, TLFRNN learns multiple fuzzy sets to reduce the impact of noises on each fuzzy set, and a randomness layer is designed, which assigning the probability of each fuzzy set. Also, TLFRNN does not utilize TSK rules instead uses a simple inference which considering fuzzy and random information of data simultaneously. More importantly, in TLFRNN, concept drift can be detected and adapted easily and rapidly. The experiments demonstrate that TLFRNN achieves superior performance compared to other EFSs.