System and method for discovery and attribution of critical ecosystem multiple counterparty behaviors to enable discovery

1 . A system that characterizes and detects specific relationship patterns and scans large relationship graphs seeking similar patterns of behaviors, said system comprises:

a computer processor;

a memory for storing a set of instructions for the computer processor;

a plurality of databases, accessible by said processor, including at least a database of behavior patterns, a database of candidate entities, a database of target space entities describing a target space ecosystem of participants, a database of target space traversal modalities selectors, and a database of dyadic relationships,

wherein the set of instructions in the memory cause the computer processor to perform steps of:

a. collecting a subset list of candidate entities from said database of candidate entities, wherein said database of candidate entities are known to exhibit a pattern of behavior;

b. constructing a digital fingerprint grammar of a candidate pattern of behavior from said subset list of candidate entities using canonical dyadic relationships for establishing a candidate relationship digital fingerprint;

c. defoliating said candidate relationship digital fingerprint to generate a defoliated candidate relationship digital fingerprint;

d. collecting a subset list of target space entities describing a target space ecosystem from said database of target space entities describing a target space ecosystem of participants, wherein said database of target space entities are known to exhibit said pattern of behavior;

e. constructing a target space grammar using said canonical dyadic relationships;

f. constructing a target space from said target space grammar for a period of time; and

g. utilizing said defoliated candidate relationship digital fingerprint within said target space during said period of time, to discover entities exhibiting similar patterns of behaviors from navigating said target space.

2 . The system of claim 1 , wherein said system further comprises the steps of:

i. selecting a target space traversal modality selector from said database of target space traversal modalities selectors to utilize said defoliated candidate relationship digital fingerprint to navigate said target space for a known pattern of behavior;

ii. traversing said target space by using said target space traversal modality selector for said period of time to navigate said target space for said known pattern of behavior;

iii. recursively processing said step (ii) until exit condition(s) are met, thereby identifying a set of subgraph components in said target space which most closely resemble key aspects of said defoliated candidate relationship digital fingerprint; and

iv. producing an output data of entities exhibiting similar patterns of behaviors from navigating said target space, which most closely resemble important attributes of said defoliated candidate relationship digital fingerprint from step (iii).

3 . The system of claim 2 , wherein said producing said output data of entities exhibiting similar patterns of behaviors comprises at least one item selected from the group consisting of:

i. description of said candidate pattern of behavior,

ii. candidate pattern of defoliation step(s),

iii. target space aggregation specifics,

iv. similarity-ranked subgraph patterns discovered with associated subgraph digital fingerprints, and

v. analytic support to justify similarity of said similarity-ranked subgraph patterns discovered with said associated subgraph digital fingerprints and data relating to statistical discovery through any method.

4 . The system of claim 2 , wherein said producing said output data of entities exhibiting similar patterns of behaviors occurs before, preparation of a known behavior and said entities exhibiting similar patterns of behaviors become aware of discovery.

5 . The system of claim 1 , further comprising:

presenting digital fingerprint patterns of said canonical dyadic relationships that have been discovered along with supporting statistical data to prosecute said discovered digital fingerprint patterns.

6 . The system of claim 5 , wherein said discovering of said digital fingerprint patterns of said canonical dyadic relationships comprises target space modalities selectors as defined by graph walks.

7 . The system of claim 6 , wherein said graph walks are at least one selected from the group consisting of: RandomWalks, DirectedWalks, and GuidedWalks.

8 . The system of claim 5 , wherein said discovering of said digital fingerprint patterns of said canonical dyadic relationships continues until exit conditions are met, either by establishing a sufficient list of target space graph subcomponents with sufficient similarity to said defoliated candidate relationship digital fingerprint or pre-established computational thresholds strongly suggest an absence of said digital fingerprint patterns.

9 . The method of claim 5 , wherein said presenting of said digital fingerprint patterns of said dyadic relationships discovers business patterns indicative of adverse consequences including at least one selected from the group consisting of money laundering, fraud, and malfeasance.

10 . The system of claim 1 , wherein the processor generates a multi-dimensional vector space embedding of numerous graph measures to characterize a local subgraph and to provide a “distance measure” of local graph similarity, used to scan a wider network for closest approximations to said discovered digital fingerprint patterns of said canonical dyadic relationships.

11 . The system of claim 1 , further comprising filtering bias resulting from at least one selected from the group consisting of random discovery, anecdotal similarity, and computational intractability.

12 . The method of claim 1 , further comprising curating the target space of an original pattern of a first environment, which is indicative of adverse consequences and is utilized in discovering a fingerprint pattern, which is indicative of adverse consequences of a second environment.

13 . The method of claim 12 , wherein said first environment indicative of adverse consequences and said second environment indicative of adverse consequences comprise at least one selected from the group consisting of: a town, city, state, province and country.

14 . The method of claim 12 , further comprising:

identifying a set of subgraph components in the target space which most closely resemble key aspects of an original pattern indicative of known malfeasance.

15 . A method for characterizing and detecting specific relationship patterns and scanning large relationship graphs seeking similar patterns of behaviors, said method comprising:

a. collecting a subset list of candidate entities from a database of candidate entities, wherein said database of candidate entities are known to exhibit a pattern of behavior;

b. constructing a digital fingerprint grammar of a candidate pattern of behavior from said subset list of candidate entities using canonical dyadic relationships for establishing a candidate relationship digital fingerprint;

c. defoliating said candidate relationship digital fingerprint to generate a defoliated candidate relationship digital fingerprint;

d. collecting a subset list of target space entities describing a target space ecosystem from a database of target space entities describing a target space ecosystem of participants, wherein said database of target space entities are known to exhibit said pattern of behavior;

e. constructing a target space grammar using said canonical dyadic relationships;

f. constructing a target space from said target space grammar for a period of time;

g. utilizing said defoliated candidate relationship digital fingerprint within said target space during said period of time, wherein said method further comprises the steps of:

i. selecting a target space traversal modality selector from a database of target space traversal modalities selectors to utilize said defoliated candidate relationship digital fingerprint to navigate said target space for a known pattern of behavior;

ii. traversing said target space by using said target space traversal modality selector for said period of time to navigate said target space for said known pattern of behavior;

iii. recursively processing said step (ii) until exit condition(s) are met, thereby identifying a set of subgraph components in said target space which most closely resemble key aspects of said defoliated candidate relationship digital fingerprint; and

iv. producing an output data of entities exhibiting similar patterns of behaviors from navigating said target space, which most closely resemble important attributes of said defoliated candidate relationship digital fingerprint from step (iii).

16 . The method of claim 15 , wherein said producing said output data of entities exhibiting similar patterns of behaviors comprises at least one item selected from the group consisting of:

i. description of said candidate pattern of behavior,

ii. candidate pattern of defoliation step(s),

iii. target space aggregation specifics,

iv. similarity-ranked subgraph patterns discovered with associated subgraph digital fingerprints, and

v. analytic support to justify similarity of said similarity-ranked subgraph patterns discovered with said associated subgraph digital fingerprints and data relating to statistical discovery through any method.

17 . The method of claim 15 , further comprising:

presenting digital fingerprint patterns of said canonical dyadic relationships that have been discovered along with supporting statistical data to prosecute said discovered digital fingerprint patterns.

18 . The method of claim 17 , wherein said discovering of said digital fingerprint patterns of said canonical dyadic relationships comprises target space modalities selectors as defined by graph walks.

19 . The method of claim 18 , wherein said graph walks are at least one selected from the group consisting of: RandomWalks, DirectedWalks, and GuidedWalks.

20 . The method of claim 17 , wherein said discovering of said digital fingerprint patterns of said canonical dyadic relationships continues until exit conditions are met, either by establishing a sufficient list of target space graph subcomponents with sufficient similarity to said defoliated candidate relationship digital fingerprint or pre-established computational thresholds strongly suggest an absence of said digital fingerprint patterns.

21 . The method of claim 17 , wherein said presenting of said digital fingerprint patterns of said dyadic relationships discovers business patterns indicative of adverse consequences including at least one selected from the group consisting of money laundering, fraud, and malfeasance.

22 . The method of claim 15 , wherein the processor generates a multi-dimensional vector space embedding of numerous graph measures to characterize a local subgraph and to provide a “distance measure” of local graph similarity, used to scan a wider network for closest approximations to said discovered digital fingerprint patterns of said canonical dyadic relationships.

23 . The method of claim 15 , further comprising filtering bias resulting from at least one selected from the group consisting of random discovery, anecdotal similarity, and computational intractability.

24 . The method of claim 15 , further comprising curating the target space of an original pattern of a first environment, which is indicative of adverse consequences and is utilized in discovering a fingerprint pattern, which is indicative of adverse consequences of a second environment.

25 . The method of claim 24 , wherein said first environment indicative of adverse consequences and said second environment indicative of adverse consequences comprise at least one selected from the group consisting of: a town, city, state, province and country.

26 . The method of claim 24 , further comprising:

identifying a set of subgraph components in the target space which most closely resemble key aspects of an original pattern indicative of known malfeasance.

27 . The method of claim 15 , wherein said producing said output data of entities exhibiting similar patterns of behaviors occurs before, preparation of a known behavior and said entities exhibiting similar patterns of behaviors become aware of discovery.