  With the popularity of blockchain technology, the financial security issues
of blockchain transaction networks have become increasingly serious. Phishing
scam detection methods will protect possible victims and build a healthier
blockchain ecosystem. Usually, the existing works define phishing scam
detection as a node classification task by learning the potential features of
users through graph embedding methods such as random walk or graph neural
network (GNN). However, these detection methods are suffered from high
complexity due to the large scale of the blockchain transaction network,
ignoring temporal information of the transaction. Addressing this problem, we
defined the transaction pattern graphs for users and transformed the phishing
scam detection into a graph classification task. To extract richer information
from the input graph, we proposed a multi-channel graph classification model
(MCGC) with multiple feature extraction channels for GNN. The transaction
pattern graphs and MCGC are more able to detect potential phishing scammers by
extracting the transaction pattern features of the target users. Extensive
experiments on seven benchmark and Ethereum datasets demonstrate that the
proposed MCGC can not only achieve state-of-the-art performance in the graph
classification task but also achieve effective phishing scam detection based on
the target users' transaction pattern graphs.
