# US 2014/0195984 A1 · Analytic frameworks for persons of interest > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2014/0195984 A1 - Document ID: US20140195984A1 - Status: Published application - Kind: A1 - Application number: 14/149,289 - Filed: 2014-01-07 - Published: 2014-07-10 - Earliest claimed: 2013-01-07 - Assignees: Northeastern University - Applicants: Northeastern University - Inventors: Javed ASLAM; Matthew EKSTRAND-ABUEG; Nikos PASSAS; Ravi SUNDARAM; Saber Shokat FADAEE - Web page: https://patentgrep.com/patent/US20140195984A1 ## CPC - G06Q 50/265: {Personal security, identity or safety} - G06Q 10/48: using social graphs - G06Q: INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR ## Abstract The present systems and methods relate to frameworks for identifying persons of interest identified through datasets collected from law-enforcement agencies, financial institutions, or other public resources. The present systems represent network data regarding individuals and explicit connections between them as a network graph. The present systems determine a statistical model representing the network graph, the statistical model generating hidden parameters for decomposing and projecting the network graph onto a space of baseline communities. The present systems categorize cliques of nodes in the network graph as the space of baseline communities and infer a category for a received potential person of interest by determining a node corresponding to the received potential person of interest, and associating a clique from the categorized cliques to the node corresponding to the received potential person of interest, where the inferred category for the potential person of interest identifies the potential person of interest as suspicious. ## Claims ### Claim 1 A system for identifying persons of interest using network data, the system comprising: a memory for storing network data regarding persons of interest, wherein the network data is stored as a network graph of persons of interest, and wherein the network graph represents individual people as nodes in the network graph and connections between the people as edges in the network graph; a display for rendering a computer-implemented user interface regarding the persons of interest; and at least one processor coupled to the memory and to the display, the processor configured to: determine a statistical model representing the network graph, the statistical model generating hidden parameters modeling the network data, for decomposing and projecting the network graph onto a space of baseline communities; categorize cliques of nodes in the network graph as the space of baseline communities based at least in part on the statistical model; infer a category for a received potential person of interest by determining a node corresponding to the received potential person of interest and associating a clique from the categorized cliques of nodes to the node corresponding to the received potential person of interest, and wherein the inferred category for the potential person of interest identifies the potential person of interest as suspicious; and render, using the display, the inferred category for the received person of interest. ### Claim 2 (depends on 1) The system of claim 1, wherein the inferred categories include at least one of Suspicious Individuals, Convicted Individuals, Legal/Lawyer Professionals, Political Individuals, Politically Exposed Individuals, Terrorists, Suspected Terrorists, Bank, Blacklisted, Corporate, Country, Crime, Diplomat, Embargo, Individual, Legal, Military, Organization, Political, Port, Religion, Terrorism, Trade, and Vessel. ### Claim 3 (depends on 1) The system of claim 1, wherein the statistical model generates the hidden parameters for reconstructing the network graph. ### Claim 4 (depends on 1) The system of claim 1, wherein the categorizing the cliques of nodes further comprises (i) determining power relationships between baseline cliques, and (ii) determining commonalities between the network graph and the baseline cliques. ### Claim 5 (depends on 1) The system of claim 1, wherein the network graph is unweighted, wherein the network graph does not adhere to a power law, and wherein the network graph does not exhibit small world phenomena. ### Claim 6 (depends on 1) The system of claim 1, wherein the categorizing the cliques of nodes further comprises identifying maximal cliques in the network graph using the Bron-Kerbosch algorithm. ### Claim 7 (depends on 3) The system of claim 3, wherein the determining the parameters for reconstructing the network graph further including solving for the parameters according to a least squares solution. ### Claim 8 A computer-implemented method for identifying persons of interest using network data, the method comprising: storing, in a computer memory, network data regarding persons of interest, wherein the network data is stored as a network graph of persons of interest, and wherein the network graph represents individual people as nodes in the network graph and connections between the people as edges in the network graph; rendering, on a display, a user interface regarding the persons of interest; determining a statistical model representing the network graph, the statistical model generating hidden parameters modeling the network data, for decomposing and projecting the network graph onto a space of baseline communities; categorizing cliques of nodes in the network graph as the space of baseline communities based at least in part on the statistical model; inferring a category for a received potential person of interest by determining a node corresponding to the received potential person of interest and associating a clique from the categorized cliques of nodes to the node corresponding to the received potential person of interest, and wherein the inferred category for the potential person of interest identifies the potential person of interest as suspicious; and rendering, using the display, the inferred category for the received person of interest. ### Claim 9 (depends on 8) The method of claim 8, wherein the inferred categories include at least one of Suspicious Individuals, Convicted Individuals, Legal/Lawyer Professionals, Political Individuals, Politically Exposed Individuals, Terrorists, Suspected Terrorists, Bank, Blacklisted, Corporate, Country, Crime, Diplomat, Embargo, Individual, Legal, Military, Organization, Political, Port, Religion, Terrorism, Trade, and Vessel. ### Claim 10 (depends on 8) The method of claim 8, wherein the statistical model generates the hidden parameters for reconstructing the network graph. ### Claim 11 (depends on 8) The method of claim 8, wherein the categorizing the cliques of nodes further comprises (i) determining power relationships between baseline cliques, and (ii) determining commonalities between the network graph and the baseline cliques. ### Claim 12 (depends on 8) The method of claim 8, wherein the network graph is unweighted, wherein the network graph does not adhere to a power law, and wherein the network graph does not exhibit small world phenomena. ### Claim 13 (depends on 8) The method of claim 8, wherein the categorizing the cliques of nodes further comprises identifying maximal cliques in the network graph using the Bron-Kerbosch algorithm. ### Claim 14 (depends on 10) The method of claim 10, wherein the determining the parameters for reconstructing the network graph further including solving for the parameters according to a least squares solution. ### Claim 15 A non-transitory computer program product for identifying persons of interest using network data, tangibly embodied in a computer-readable medium, the computer program product including instructions operable to cause a data processing apparatus to: store, in a computer memory, network data regarding persons of interest, wherein the network data is stored as a network graph of persons of interest, and wherein the network graph represents individual people as nodes in the network graph and connections between the people as edges in the network graph; render, on a display, a user interface regarding the persons of interest; determine a statistical model representing the network graph, the statistical model generating hidden parameters modeling the network data, for decomposing and projecting the network graph onto a space of baseline communities; categorize cliques of nodes in the network graph as the space of baseline communities based at least in part on the statistical model; infer a category for a received potential person of interest by determining a node corresponding to the received potential person of interest and associating a clique from the categorized cliques of nodes to the node corresponding to the received potential person of interest, and wherein the inferred category for the potential person of interest identifies the potential person of interest as suspicious; and render, using the display, the inferred category for the received person of interest. ### Claim 16 (depends on 15) The computer program product of claim 15, wherein the inferred categories include at least one of Suspicious Individuals, Convicted Individuals, Legal/Lawyer Professionals, Political Individuals, Politically Exposed Individuals, Terrorists, Suspected Terrorists, Bank, Blacklisted, Corporate, Country, Crime, Diplomat, Embargo, Individual, Legal, Military, Organization, Political, Port, Religion, Terrorism, Trade, and Vessel. ### Claim 17 (depends on 15) The computer program product of claim 15, wherein the statistical model generates the hidden parameters for reconstructing the network graph. ### Claim 18 (depends on 15) The computer program product of claim 15, wherein the instructions operable to cause the data processing apparatus to categorize the cliques of nodes further comprise instructions operable to cause the data processing apparatus (i) to determine power relationships between baseline cliques, and (ii) to determine commonalities between the network graph and the baseline cliques. ### Claim 19 (depends on 15) The computer program product of claim 15, wherein the network graph is unweighted, wherein the network graph does not adhere to a power law, and wherein the network graph does not exhibit small world phenomena. ### Claim 20 (depends on 8) The computer program product of claim 8, wherein the instructions operable to cause the data processing apparatus to categorize the cliques of nodes further comprise instructions operable to cause the data processing apparatus to identify maximal cliques in the network graph using the Bron-Kerbosch algorithm.