DETECTING NETWORK PATTERNS USING RANDOM WALKS

A system may receive a dynamic network graph and select an origination node. From the origination node, the system may deploy a random walk simulation on the dynamic network graph simulating steps from the origination node to one or more other nodes, and determine a convergence node for the random walk simulation.

BACKGROUND

Aspects of the present disclosure relate to detecting network patterns using random walks.

A financial network is a concept describing any collection of financial entities (such as traders, firms, banks and financial exchanges) and the links between them, ideally through direct transfers or the ability to mediate a transfer.

Data networks refer to systems designed to transfer data between two or more access points via the use of system controls, transmission lines and data switching.

BRIEF SUMMARY

The present disclosure provides a method, computer program product, and system of detecting network patterns using random walks. In some embodiments, the method includes receiving a dynamic network graph, selecting an origination node, deploying a random walk simulation on the dynamic network graph simulating steps from the origination node to one or more other nodes, and determining, from the results of the random walk simulations, a convergence node for the random walk simulation.

Some embodiments of the present disclosure can also be illustrated by a computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a processor to cause the processors to perform a method, the method comprising receiving a dynamic network graph, selecting an origination node, deploying a random walk simulation on the dynamic network graph simulating steps from the origination node to one or more other nodes, and determining, from the results of the random walk simulations, a convergence node for the random walk simulation.

Some embodiments of the present disclosure can also be illustrated by a system comprising a processor and a memory in communication with the processor, the memory containing program instructions that, when executed by the processor, are configured to cause the processor to perform a method, the method comprising receiving a dynamic network graph, selecting an origination node, deploying a random walk simulation on the dynamic network graph simulating steps from the origination node to one or more other nodes, and determining, from the results of the random walk simulations, a convergence node for the random walk simulation.

DETAILED DESCRIPTION

Aspects of the present disclosure relate to detecting network patterns using random walks. While the present disclosure is not necessarily limited to such applications, various aspects of the disclosure may be appreciated through a discussion of various examples using this context.

Since financial system and data systems are becoming increasingly complex and are strongly interrelated, the interconnectedness among institutions can lead to a rapid propagation of transactions. In this regard, interconnectedness represents a key aspect of financial stability and data access. However, millions of interactions between nodes make tracking illicit activities (few in number) amongst an astounding amount transactions impossible to detect using traditional means. Many instances of money laundering or data hijacking (amongst other illicit activities in networks) are done with many small transfers instead of a few large transfers. For example, large transfers of money or data are often flagged for review, but it is impossible to track the multitude of small transfers that are commonplace on networks. For example, money laundering may transfer large amounts of money (e.g., $1,000,000) from a first node (e.g., a bank account, credit account, crypto currency account, etc.) to a second node with a large number of small transfers ($100-1000) by way of a number of intermediary accounts. Since the transfers are proceeding through a number of intermediary accounts, it may be difficult to determine that the bulk of the money is going from the first account to the second account. Similar transfers may happen in other types of networks, such as data networks.

Therefore, in some embodiments, a method of determining links on nodes with random walks is proposed.

FIG.1A and1Bdepict an example deployment of a random walk simulation (RWS) in a network100. In some embodiments, network100may be a financial network, a data network, or another kind of network where transfers or transfer take place. In some embodiments, the network may be deployed, in full or in part, on a cloud network, such as cloud computing network50depicted inFIG.5. In some embodiments, nodes101,102,103,104,105,106,107,108,109,110, and111may be accounts, institutions, computers, servers, etc. Network100has arrows depicting example transfers, but other transfers (not shown for simplicity purposes) may be envisioned. For example, the transfers may happen in both directions, other nodes may be present with transfers to and from the depicted nodes, and there may be other transfers between the depicted nodes. In some instances, each arrow may represent a number of transfers. For example, there may be 100 transfers between node101and102and 10,000 transfers between node101and104.

In some embodiments, an origination node may be selected for the RWS. The origination node may be selected based on the outgoing transfers from each node. For example, origination nodes may be selected based on a large (e.g., 1000 transfers a day) number of small size (e.g., less than $10,000 or 100 megabits) outgoing transfers for a certain time period (e.g., a day or week).

In some embodiments, the number of transfers between each node may affect the likelihood that the random walk takes a step between nodes. Following the example from above, if there may be 100 transfers between node101and102and 10,000 transfers between node101and104, the step from node101and node104may be 100 times more likely than a step from101and102because there are 100 times more transfers from node101to104than101and102.

In some embodiments, the random walk simulation randomly (based on the number of transfers from one node to another) steps between nodes. In some embodiments, the random walk simulation creates multiple instances of possible transfer paths between nodes.FIG.1A and1Beach depict a single instance, but the simulation may create multiple instances (e.g., hundreds or thousands). The results of the multiple instances may be aggregated to determine a convergence node as described herein. In some embodiments, the number of steps may be set for the simulation. For example, a RWS may be set to take a maximum of 10 steps from one node to another.FIG.1Adepicts a first instance of a RWS starting at origination node101to103, then to106, and then to109.FIG.1Bdepicts a second instance of the RWS starting at origination node101to104, then to107, then to110, then to111, and then to109. In some embodiments, the other instances may be performed.

In some instances, the random walk simulation takes steps based on previous transfers between nodes. For example, in each instance of the RWS, the system may randomly select a transfer from the origination node thereby leading the RWS to another node (e.g., a second node). The RWS may then repeat the process by randomly selecting a transfer out of the second node to a third node, and so on. In some instances, randomly selecting may include using a random number generator to select a transfer to follow. For example, if a node has 100 transfers leaving it, the system may generate a random number of 62. Thus, the system may follow the 62ndtransaction. In some embodiments, each subsequent transfer for an instance of the RWS occurs after the previous transfer. For example, a money transfer from node101to node104may not take place after a money transfer from node104to node107because the simulation may then produce results that could not happen in reality since the money would not have reached node104.

In some embodiments, node109is a common convergence node for the instances of the RWS simulation. In some embodiments, convergence node109is a common step for a significant percentage of the instances. For example, the significant percentage may be set by a user for an RWS. In a further example, if money laundering generally sends about 75% of the final money to a single destination, the significant percentage may be set at 75%. In some embodiments, a convergence node may have other transfers going out to other nodes. For example, 90% of the transfers from an origination node have simulations that stop by a convergence node but continue on to other nodes. All simulations do not need to stop at the convergence node to be considered significant. For example, all instances of the simulation may continue on to other nodes.

FIG.2depicts an example flow graph200for performing random walk simulations in parallel. In some embodiments, flow graph200begins at step210by analyzing a network and the network data to convert the network data into a dynamic graph in step220. In some embodiments, a system performing the simulation may receive a dynamic network graph. In some instances, a dynamic network graph can be represented as an ordered list or an asynchronous stream of timed events, such as additions or deletions of nodes and edges. The network may be a social media network, a financial network, a data network, a cloud network, or any other network were data, messages, information, currency, crypto currency, non-fungible tokens, or anything of value is transferred.

In some embodiments, an origination node is selected. In some embodiments, step230may determine if the node is diverse (e.g., referred to as an origination node herein). If the node is not diverse (“NO” at step230), the system may move on to another node and repeat step230. In some embodiments, to determine if a node is diverse, the system may evaluate one or more nodes to evaluate if the node meets a number of factors such as the number of outgoing transfers and the size of the outgoing transfers. For example, candidate diverse nodes may have greater than 1000 transfers that are all less than $1000 indicating a possibility of one or more illicit transfer (e.g., an attempt to send a significant amount of money via transactions that are all smaller than some “flaggable” amount), though in other examples other factors may be set by the system to identify potentially illicit transfers according to profiles of illicit transfers (e.g., what characteristics illicit transfers may exhibit). In some embodiments, an origination node is a starting point for a random walk simulation. In some embodiments, the selecting may be performed by identifying nodes with more than a threshold number of transfers and with a percentage of the transfers are under a transfer limit. The threshold number of transfers is a minimum number of transfers that would warrant a simulation. For example, illicit activity for accounts may involve more than 100 transfers per day, thus the threshold number may be above 100. In some instances, the users of the simulation may determine the threshold for the minimum number of transfers. In some instances, the system may determine the threshold based on the number of candidate nodes that qualify. For example, the system may have allotted resources that would only allow it to run simulations on 1000 nodes, thus the system would choose the 1000 nodes with the most transfers under the transfer limit. In some embodiments, the transfer limit is the highest amount that may be considered for a transfer. For example, transfers over $10,000 may already be monitored by a government agency and are therefore unlikely to be involved in illicit activity. In some embodiments, running the simulations in parallel may include running the simulations at the same time, in batches, in a sequence, or other method as system resources dictate.

In some embodiments, the system may deploy a random walk simulation on the dynamic network graph simulating steps from the origination node to one or more other nodes. In some embodiments, the dynamic network graph may have a multitude of nodes and there may be links or edges between the nodes. In some embodiments, the links or edges are transfers from one node to another. For example, a first node may send a second node an amount of money, cryptocurrency, data, or a message. In some embodiments, the system may identify messages that are under a certain size. For example, the system may filter out any messages that are over 100 MB.

In some embodiments, if the node is detected as diverse (“YES” at step230), the random walk simulation performs a plethora of parallel instances steps250,251,252,253(only four instances are shown many instances may be run in parallel) by randomly selecting a transfer from a current node for each instance, where the current node is the origination node for each instance. In some embodiments, the transfer leads the simulation to a recipient node that received the transfer. In some embodiments, the recipient node is the current node. The system then repeats the randomly selecting and identifying steps up to a set number of times. For example, each node may be a step in an instance of the random walk simulation. The number of steps (length of each random walk) in a simulation may be set at a maximum of simulations (e.g., a threshold number of steps). For example, the threshold number of steps may be set at T=10, thus each instance may have up to 10 steps. Likewise, a number of instances for each simulation may also be set. For example, 1000 instances of the random walk simulation may be held. In some cases, the number of instances may also be a percentage of the total number of transfers (or total number of transfers meeting a criteria) from the origination node.

In some embodiments, the system may determine, from the results of the random walk simulations, a convergence node for the random walk simulation. For example, the convergence node may be a node that at least 50% of the instances of a simulation have as a step. In some embodiments, the system may identify and use candidate transfers that fall within a time period and under a certain transfer limit. For example, the system may investigate transfers that happen within a one-week period.

In some embodiments, the convergence node may be flagged as a suspicious node. The convergence node may be a node that received enough transfers that may have originated at the origination node that a further investigation may be warranted by authorities. For example, authorities may deem it appropriate to investigate transfers where the threshold number of simulations is more than 50% of the simulations. Different types of possible illicit activity may have different thresholds.

FIG.3depicts an example method300for detecting network patterns using random walks. Operations of method300may be enacted by one or more computer systems such as the system described inFIG.4below.

Method300begins with operation305of receiving a dynamic network graph. In some embodiments, the dynamic network graph may be a network map, a table of data, a table of transfers between nodes, a matrix of network connections with transfer metadata, or other means of conveying how and when a transfer is performed between nodes.

Method300continues with operation310of selecting an origination node. In some embodiments, the origination node may be selected based on a candidate node having more than a threshold number of transfers where a certain percentage of the transfers are below a transfer limit. In some embodiments, the threshold number may be a percentage of transfers or a certain number of transfers. In some embodiments, the transfer limit may be set by a system operator. For example, the system operator may determine that normal systems flag transfers over $10,000, so the transfer limit may be set to less than $10,000. In some embodiments, the simulation may be performed multiple times with varying transfer limits to optimize the set transfer limit. For example, simulations with transfers under 10,000 may have too many legitimate transfers creating noise making it difficult to find illegitimate transfers.

Method300continues with operation315of deploying a random walk simulation. In some embodiments, a random walk simulation creates multiple instances evaluating how transfers happen from one node to another. In some embodiments, in operation320below, the results of the simulation may be used to find a convergence node for the instances.

In some embodiments, transfers between accounts may be possible steps for the random walk simulation. For example, there may be 4 linked nodes (nodes A, B, C, and D) that have received transfers from a first node. The first node may have made a transferred money to node A 100 times, node B 200 times, node C 300 times, and node D 400 times. Each transfer is a possible step for the random walk. Thus, a step from the first node to node D may be 4 times more likely than a step from the first node to node A (since there were 400 transfers from the first node to node D and 100 transfers to node A). Subsequent steps from nodes A, B, C, and D to other nodes may also be simulated and so on from those other nodes to the next level of nodes. In some embodiments, a maximum number of steps may be set for the steps (e.g., 5 steps between nodes).

Method300continues with operation320of determining a common convergence node for the random walk simulation. In some embodiments, the random step simulation may be performed a set number of times, and the simulation results may be analyzed.

In an exemplary embodiment, the system includes computer system01as shown inFIG.4and computer system01may perform one or more of the functions/processes described above. Computer system01is only one example of a computer system and is not intended to suggest any limitation as to the scope of use or functionality of embodiments of the present invention. Regardless, computer system01is capable of being implemented to perform and/or performing any of the functionality/operations of the present invention.

Cloud Computing

Characteristics are as follows:

Service Models are as follows:

Deployment Models are as follows: