## 1 Introduction  
In the current information age, things are moving towards cyberspace. The Internet has become a significant part of modern society. Moreover, fraud activities have subsequently expended from offline to online. Fraudsters post fake content anonymously, which disrupts the normalcy of cyberspace, and benign users may suffer losses as a result. It is inefficient and costly to manually censor tens of millions or even hundreds of millions of complicated online content to make sure whether the information is fraud or not. Performing fraud detection efficiently has important research significance. Considering a social networking service or a shopping site, we abstract its users, reviews, or any information into nodes, and regard the relationship between nodes as edges. Then, we can build a graph, and GNNs are applied to detect the suspicious characteristics of nodes so as to find out the fraudsters. In fact, graph neural networks (GNNs) extend application field of classic deep learning to tasks with non-Euclidean data [1]. By dealing with relation camouflages and feature camouflages, CAmouflage-REsistant GNN (CARE-GNN) is proposed and achieves state-of-the-art results recently [2]. One of primary reasons for the success of neural network models in the area of computer vision is the ability to train deep network architectures (e.g., 19 layers for VGG [3], 22 layers for GoogLeNet [4], and 152 layers for ResNet [5]). However, GNNs suffer from a intrinsic limit which makes it difficult for GNNs to be trained with deep architectures. As a consequence, most state-of-the-art GNNs are restricted to have no more than four layers [6]. Experimental results of CARE-GNN show that it performs the best with single-layerarchitecture. Moreover, its performance drops rapidly as the number of layers increases. In this case, the single-layer CARE-GNN aggregates neighboring information for every node only once. The model has no chance to fix its potential mistakes and to revise its inferences. We call the problem above as single-layer learning. Briefly, single-layer learning has two meanings: (1) intrinsic shallow limit of GNNs and (2) no chance to correct mistakes made by previous layer and to revise inferences. Based on the CARE-GNN algorithm, inspired by the work in [7], we first use a different method to stack multiple layers. The classical architecture of multi-layer for GNNs is defined by hop. We take the calculation process of a node \(v\) as an example. If we first take \(v\)'s neighbors as central nodes and aggregate information from the neighbors' neighbors for each one, and then aggregate the neighbors' information into node \(v\), we define that the model has two hops, which also means that the model has two layers. Thus, as we increase the number of layers, more and further information can be aggregated. However, the most valuable information always exists in neighbors that are directly connected to central nodes. The further the distance (i.e., the hop) is, the less relevant the information will be. Therefore, we consider how to make the most effective use of the information of directly connected nodes. Based on the above considerations, we propose a new improved algorithm named Residual Layered CARE-GNN (RLC-GNN). We use a different method to define the concept of multi-layer. We utilize a layered structure to collect the most valuable information from neighbors. The architecture and working mechanism of RLC-GNN is similar to the models for classic deep learning tasks (e.g., image classification). In an iteration, every layer processes a same batch of nodes. Moreover, we do not update original dataset with each layer's output features. Furthermore, we introduce the residual structure into our algorithm to compensate the losses of information during propagation. Thus, RLC-GNN has the ability to correct its own mistakes and \"think\" more deeply and comprehensively. The two structures can form a complementary relationship and make the most effective use of neighboring information.