A Iterative Method to Analyze Misbehavior in Blockchain by Transaction Graph Matrix

Blockchain provides an publicly accessed and accepted ledger without centralized control among open participators. The anonymity in payments and transactions may results in possible misbehaviors, e.g., money laundry. Fortunately, the transaction data is open and can be analyzed. In this paper, we propose The major challenges are the data volume is large and the global view of transactions in terms of hops and time sequences. In this paper, we address the transactions with multiple intermediators, and graph dynamics with time-spans. The analyzing models and computing algorithms are proposed. The extensive and formal analysis justifies the soundness of our proposed methods.