  Blockchain technology and, in particular, blockchain-based transaction offers
us information that has never been seen before in the financial world. In
contrast to fiat currencies, transactions through virtual currencies like
Bitcoin are completely public. And these transactions of cryptocurrencies are
permanently recorded on Blockchain and are available at any time. Therefore,
this allows us to build transaction networks (TN) to analyze illegal
phenomenons such as phishing scams in blockchain from a network perspective. In
this paper, we propose a Transaction SubGraph Network (TSGN) based
classification model to identify phishing accounts in Ethereum. Firstly we
extract transaction subgraphs for each address and then expand these subgraphs
into corresponding TSGNs based on the different mapping mechanisms. We find
that TSGNs can provide more potential information to benefit the identification
of phishing accounts. Moreover, Directed-TSGNs, by introducing direction
attributes, can retain the transaction flow information that captures the
significant topological pattern of phishing scams. By comparing with the TSGN,
Directed-TSGN indeed has much lower time complexity, benefiting the graph
representation learning. Experimental results demonstrate that, combined with
network representation algorithms, the TSGN model can capture more features to
enhance the classification algorithm and improve phishing nodes' identification
accuracy in the Ethereum networks.
