## 2 Related Works  
GNNs are originally designed to deal with the complicated non-Euclidean data [8], which the classic deep learning algorithms are unable to process systematically and reliably in general. There are two main types of GNNs [6]: spectral-based GNNs and spatial-based GNNs. The first proposed spectral-based GNN algorithm implements the convolutional operation on topological graphs using spectral graph theory [9] (i.e., using the eigenvalues and eigenvectors of the Laplacian matrix of the graph to study the properties of graphs). Since then, many improved algorithms based on graph convolutional network (GCN) have appeared one after another. The first spatial-based GNN algorithm [10] was actually proposed much earlier, which iteratively aggregates neighborhood information and updates the node embeddings by global shared local transition function and local output function. As spatial-based GNNs are in possession of more flexibility in algorithm designing, most proposed GNN variants belongs to this type. Moreover, so is our proposed RLC-GNN.  
Fraud detection is essentially a semi-supervised node classification problem. There are two reasons which make the fraud detection task special: One reason is that sample distribution of datasets is extremely unbalanced, and the other reason is that fraudsters will actively conceal their features to avoid to be detected [2; 11]. These characteristics make it difficult for classic deep learning algorithms to learn implicit rules in data. In this case, even if a classification model is obtained, it always have serious bias, which results in poor generalization. Various methods have been presented recently to solve the problem. The Graph Embeddings for Malicious accounts algorithm [12] is the first heterogeneous graph neural network approach for detecting malicious accounts, establishing multiple homogeneous subgraphs based on the types of nodes, and aggregating neighborhood information under each subgraph. Moreover, the GEM algorithm uses an attention mechanism to learn the importance of each type. The GCN-based Anti-Spam algorithm [13] is the first GCN-based spam detector which applies on heterogeneous graph and directly aggregates information of three types of nodes at the same time. Then, research of the Adversary Situation Awareness algorithm [14] gave the solutions to the fact of the confrontational behaviors of fraudsters, e.g., adding special symbols into texts which makes detectors unable to recognize the original semantic information or avoiding detection by switching devices and networks.  
Current GNN algorithms are limited to shallow depth in the conventional sense which is defined by \(k\)-hop. Some approaches have been proposed to help train deeper GNNs. In the traditional CNN area, the work of ResNet [5] presents a residual learning framework to ease the training of very deep neural networks (as many as 152 layers). Moreover, the works in [15; 16] apply the residual structure to break the shallow limit of GCNs. The work in [17] proposes a Node Normalization technique to reduce feature correlation and increase the smoothness of models, successfully helping train deeper GCN. The research of H-GCN [18] proposes coarsening procedure and correspondingly makes GCN deeper to enlarge the receptive field for each node.  
However, many algorithms and methods are designed for spectral-based GNNs of which the calculation theories are not similar to those of classic deep learning models (e.g., convolutional neural network (CNN)). They cannot utilize well-developed techniques to assist training and make good use of the flexibility of structure design of spatial-based GNNs.