In recent years, money laundering has become much easier to be achieved but more challenging to be detected than before, which has enormous adversary effects on finance, military, and other related fields. In the real-time scenario, every money laundering case has a unique structure in terms of transactions. It is not sufficient to detect suspicious behavior by just following the probability theory, where usually the thresholds are given by experts. Since the crime of money laundering is more prevalent and sophisticated nowadays, it will increase the complexity of the detection if the accounts with the personal information are combined with the form of the transaction topology. Hence, the graph topology analysis could be used for antimoney laundering tools. This article proposes eight common topologies based on coupling and connection from simple to much more complicated structures to solve various kinds of problems concerning money laundering in the real world. Moreover, we also propose an efficient solution based on graph and subgraph isomorphism and distance measurement to detect money laundering behavior. In this way, the detection of money laundering behavior will be more efficient and effective for various situations while referencing the proposed eight topological structures.