  With the ever-increasing boom of Cryptocurrency, detecting fraudulent
behaviors and associated malicious addresses draws significant research effort.
However, most existing studies still rely on the full history features or
full-fledged address transaction networks, thus cannot meet the requirements of
early malicious address detection, which is urgent but seldom discussed by
existing studies. To detect fraud behaviors of malicious addresses in the early
stage, we present Evolve Path Tracer, which consists of Evolve Path Encoder
LSTM, Evolve Path Graph GCN, and Hierarchical Survival Predictor. Specifically,
in addition to the general address features, we propose asset transfer paths
and corresponding path graphs to characterize early transaction patterns.
Further, since the transaction patterns are changing rapidly during the early
stage, we propose Evolve Path Encoder LSTM and Evolve Path Graph GCN to encode
asset transfer path and path graph under an evolving structure setting.
Hierarchical Survival Predictor then predicts addresses' labels with nice
scalability and faster prediction speed. We investigate the effectiveness and
versatility of Evolve Path Tracer on three real-world illicit bitcoin datasets.
Our experimental results demonstrate that Evolve Path Tracer outperforms the
state-of-the-art methods. Extensive scalability experiments demonstrate the
model's adaptivity under a dynamic prediction setting.
