1Department of Information and Network Security, People’s Public Security University of China, Beijing 100038, China 2Beijing Chaoyang District People’s Court, Beijing 100021, China
[Objective] This paper provides a graph convolutional neural network model with spatial and neighborhood adaptability for credit fraud detection. [Methods] We proposed a Hyperbolic Jumping Connection Graph Convolutional Neural Networks. Regarding spatial adaptability, we represented the node attributes as a trainable curvature in hyperbolic space and completed the low-distortion embedding representation of the fraudulent network. In terms of neighborhood adaptability, we defined a Hyperbolic Jumping Knowledge Networks framework and fused the neighborhood representation results through the hyperbolic inter-layer aggregation mechanism. As a result, we provided the relational network with a graph representation learning result integrating spatial and neighborhood adaptability. Finally, we completed the task of credit fraud detection. [Results] By deploying experiments in a large-scale social network that is publicly available and comes from real business scenarios, the proposed model achieved an AUC of 0.835 5, which was 0.059 4 higher than the baseline model represented by GraphSAGE(NS). [Limitations] The advantages of shallow social networks on neighborhood adaptability are slightly limited, and the advantages of our model are more evident in large-scale complex deep network structures. [Conclusions] Spatial adaptation provides a more accurate description of node attribute correlations, and neighborhood adaptation selects the optimal neighborhood aggregation range for graph representation learning. The proposed model has a better identification effect in large-scale fraud relationship networks.
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