Temporal high-order proximity aware behavior analysis on Ethereum

Ethereum, the most popular public blockchain with the capability of smart contracts and the cryptocurrency Ether, is escalating in the number of account addresses and transactions since its birth. Due to the decentralisation of the Ethereum blockchain and the anonymity of its users, Ethereum serves as a noteworthy environment for malicious activities that are difficult to unearth. As a result, understanding the behaviors of the account addresses on Ethereum has become an imperative problem receiving much attention very recently. Existing works for such task mainly rely on extracting statistical features of account addresses and applying machine learning techniques to group or identify them. However, seldom prevailing approaches take temporal information and high-order interactions among the account addresses into consideration. To this end, we propose a novel approach coined THCD (T emporal H igh-order proximity aware C ommunity D etection) for behavior analysis on Ethereum from the perspective of graph mining. First, frequent temporal motifs are mined over a transaction graph constructed by the Ethereum block transactions. Next, we define the high-order proximity between two accounts based on these temporal motif occurrences. Finally, a novel temporal motif-aware community detection method is devised to find account communities over the defined high-order proximity. Experiments on four real datasets constructed from Ethereum blocks demonstrate the effectiveness of our approach. Some discovered suspicious accounts are confirmed by real-world reports. Meanwhile, THCD is scalable to large-scale transaction datasets.