Transaction community identification in bitcoin

The emergency of anonymous encrypted digital currency based on blockchain brings the rapid growth of financial crimes simultaneously. However, under the condition of Know Your Customer rules, the traditional rule-based filtering and supervised pattern recognition methods are mainly built, which does not apply to the scenario of anonymous encrypted digital currency. In this paper, we attempt to tackle this problem by constructing user graph from transactions and dividing the whole user graph into tightly connected communities and clustering similar communities into groups. Experimental results on bitcoin transaction datasets show that the proposed approach has higher than 92% precision and higher than 73% recall for identifying gambling and mining pool communities.