A Graph Convolutional Credit Fraud Detection Model with Spatial-Neighborhood Adaptability
[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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