  A payment network contains transactions between sellers and buyers. Detecting
risky (or bad) sellers on such a payment network is crucial to payment service
providers for risk management and legal compliance. In this research, we
formulate this task as a node classification task. Specifically, we aim to
predict a label for each seller in a payment network, by analysing its
properties and/or interactions. Nodes residing in different parts of a payment
network can have similar local topology structures. Such local topology
structures reveal sellers' business roles, eg., supplier, drop-shipper, or
retailer. We note that many existing solutions for graph-based node
classification only consider node connectivity but not the similarity between
node's local topology structure. Motivated by business need, we present a
dual-path graph convolution network, named DP-GCN, for node classification.
DP-GCN considers both node connectivity and topology structure similarity. The
proposed model consists of three main modules: (i) a C-GCN module to capture
connectivity relationships between nodes, (ii) a T-GCN module to capture
topology structure similarities between nodes, and (iii) a multi-head
self-attention module to align both properties. We evaluate DP-GCN on seven
benchmark datasets against diverse baselines. We also provide a case study of
running DP-GCN on three large-scale payment networks from PayPal, one of the
leading payment service providers. Experimental results demonstrate DP-GCN's
effectiveness and practicability.
