  The anonymity of blockchain has accelerated the growth of illegal activities
and criminal behaviors on cryptocurrency platforms. Although decentralization
is one of the typical characteristics of blockchain, we urgently call for
effective regulation to detect these illegal behaviors to ensure the safety and
stability of user transactions. Identity inference, which aims to make a
preliminary inference about account identity, plays a significant role in
blockchain security. As a common tool, graph mining technique can effectively
represent the interactive information between accounts and be used for identity
inference. However, existing methods cannot balance scalability and end-to-end
architecture, resulting high computational consumption and weak feature
representation. In this paper, we present a novel approach to analyze user's
behavior from the perspective of the transaction subgraph, which naturally
transforms the identity inference task into a graph classification pattern and
effectively avoids computation in large-scale graph. Furthermore, we propose a
generic end-to-end graph neural network model, named $\text{I}^2 \text{BGNN}$,
which can accept subgraph as input and learn a function mapping the transaction
subgraph pattern to account identity, achieving de-anonymization. Extensive
experiments on EOSG and ETHG datasets demonstrate that the proposed method
achieve the state-of-the-art performance in identity inference.
