CN107679201B - Hide people's method for digging, device and electronic equipment - Google Patents
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Abstract
The present invention provides a kind of concealment people method for digging, device and electronic equipment, this method to include：According to the weights on the company frontier juncture sides Xi Helian between each node of every straton network in relational network, the standard compactness concentration degree of each node corresponding with the sub-network is determined；Node includes people to be predicted and locked people；For each of relational network people to be predicted, according to the standard compactness concentration degree of each node in every straton network, the degree of association of people to be predicted and each locked people in sub-network are determined respectively；According to people to be predicted in each straton network and the degree of association of each locked people and the importance of each straton network, the suspicion index of people to be predicted is determined；According to the suspicion index of each people to be predicted, the concealment people of target case is determined.Concealment people is excavated by calculating the suspicion index of people to be predicted in this way, is not influenced by subjective factor, the reliability and accuracy of Result can be improved.
Description
Technical field
The present invention relates to data mining technology fields, are set more particularly, to a kind of concealment people method for digging, device and electronics
It is standby.
Background technology
With being constantly progressive for human society, the development that science and technology are advanced by leaps and bounds especially is modernized, criminal offence
Intelligence, high-technicalization, mobilism, the systematism of criminal and professional trend are also more and more obvious, and have characteristics of the times
New crime form and new crime means continuously emerge, modern times break laws and commit crime is in a case high-incidence season and speed-raising
Phase.
In all kinds of crime cases, suspect or offender usually hide in complicated relationship.Usually
The social groups or chat record for searching known suspect, the society of suspect is hidden according to the information architecture found
Network is handed over, the prediction of concealment suspect is then carried out to the member in social network diagram, to excavate concealment suspect (letter
Referred to as hide people).
However, carrying out the prediction of concealment suspect using the above method, subjective, obtained Result can
It is low by property, accuracy is poor.
Invention content
In view of this, the purpose of the present invention is to provide a kind of concealment people method for digging, device and electronic equipment, to improve
The reliability and accuracy of Result.
In a first aspect, an embodiment of the present invention provides a kind of concealment people's method for digging, including：
According to the weights on the company frontier juncture sides Xi Helian between each node per straton network in relational network, determine with it is described
The standard compactness concentration degree of the corresponding each node of sub-network；Wherein, the relational network includes and target case pair
The importance of sub-network described in the multilayer answered and each layer sub-network；The node includes people to be predicted and locked people；
For each of the relational network people to be predicted, according to each node in sub-network described in every layer
Standard compactness concentration degree, determine being associated with for people to be predicted and each locked people described in the sub-network respectively
Degree；
According to the degree of association and each layer institute of people to be predicted described in each layer sub-network and each locked people
The importance for stating sub-network determines the suspicion index of the people to be predicted；
According to the suspicion index of each people to be predicted, the concealment people of the target case is determined.
With reference to first aspect, an embodiment of the present invention provides the first possible embodiments of first aspect, wherein institute
The weights according to the company frontier juncture sides Xi Helian between each node of every straton network in relational network are stated, are determined and the sub-network
The standard compactness concentration degree of corresponding each node, including：
According to the weights on the company frontier juncture sides Xi Helian between each node of every straton network in relational network, the son is calculated
Shortest path distance in network between each node；
According to the shortest path distance between each node in sub-network described in every layer, calculate corresponding with the sub-network
Each of the node standard compactness concentration degree.
The possible embodiment of with reference to first aspect the first, an embodiment of the present invention provides second of first aspect
Possible embodiment, wherein the standard for being calculated by the following formula each node corresponding with the sub-network is close
Property concentration degree：
Wherein, Cx(i) the compactness concentration degree of node i corresponding with sub-network x, C ' are indicatedx(i) it indicates and x pairs of sub-network
The standard compactness concentration degree for the node i answered, NxIndicate the total number of sub-network x interior joints, dx(i, j) indicates to save in sub-network x
Shortest path distance between point i and node j.
The possible embodiment of with reference to first aspect the first, an embodiment of the present invention provides the third of first aspect
Possible embodiment, wherein it is described for each of the relational network people to be predicted, according to subnet described in every layer
The standard compactness concentration degree of each node in network, respectively determine the sub-network described in people to be predicted with it is each described
The degree of association of locked people, including：
For each of the relational network people to be predicted, each layer that the people to be predicted is related to is determined
Network；
In every layer of sub-network, each locked people being connected to the people to be predicted is determined；
For the locked people each of is connected in sub-network described in every layer with the people to be predicted, according to it is described wait for it is pre-
Survey people standard compactness concentration degree, the standard compactness concentration degree of the locked people and the people to be predicted with it is described
The shortest path distance for locking people calculates the degree of association of people and the locked people to be predicted described in the sub-network.
The third possible embodiment with reference to first aspect, an embodiment of the present invention provides the 4th kind of first aspect
Possible embodiment, wherein be calculated by the following formula the degree of association γ of people i and locked people j to be predicted in sub-network xx
(i,j)：
Wherein, C 'x(i) the standard compactness concentration degree of to be predicted people i corresponding with sub-network x, C ' are indicatedx(j) indicate with
The standard compactness concentration degree of the corresponding locked people j of sub-network x, dx(i, j) indicates in sub-network x people i to be predicted and has locked
Determine the shortest path distance between people j.
With reference to first aspect, an embodiment of the present invention provides the 5th kind of possible embodiments of first aspect, wherein logical
Cross the suspicion index that following formula calculates the people to be predicted：
Wherein, PiIndicate that the suspicion index of people i to be predicted, N indicate total number of plies of the sub-network of the relational network, WxTable
Show the importance of sub-network x, γx(i, j) indicates the degree of association of people i to be predicted and locked people j in sub-network x, described in J expressions
The set of locked people.
With reference to first aspect, an embodiment of the present invention provides the 6th kind of possible embodiments of first aspect, wherein institute
The suspicion index according to each people to be predicted is stated, determines the concealment people of the target case, including：
Each people to be predicted is sorted according to suspicion exponential size；
According to putting in order for each people to be predicted, the concealment people of the target case is determined.
Second aspect, the embodiment of the present invention also provide a kind of concealment people's excavating gear, including：
Concentration degree determining module, for according to the frontier juncture system of company and company between each node of every straton network in relational network
The weights on side determine the standard compactness concentration degree of each node corresponding with the sub-network；Wherein, the network of personal connections
Network includes the importance of sub-network and each layer sub-network described in multilayer corresponding with target case；The node is pre- including waiting for
Survey people and locked people；
Degree of association determining module is used for for each of the relational network people to be predicted, according to described in every layer
The standard compactness concentration degree of each node in sub-network, respectively determine the sub-network described in people to be predicted with it is each
The degree of association of the locked people；
Suspicion index determining module, for according to people to be predicted described in each layer sub-network with it is each described locked
The importance of the degree of association of people and each layer sub-network determines the suspicion index of the people to be predicted；
People's determining module is hidden, for the suspicion index according to each people to be predicted, determines the target case
Hide people.
The third aspect, the embodiment of the present invention also provide a kind of electronic equipment, including memory, processor, the memory
In be stored with the computer program that can be run on the processor, the processor is realized when executing the computer program
State the method described in first aspect.
Fourth aspect, the embodiment of the present invention also provide a kind of meter for the non-volatile program code that can perform with processor
Calculation machine readable medium, said program code make the processor execute above-mentioned first aspect the method.
The embodiment of the present invention brings following advantageous effect：
In the embodiment of the present invention, according to the company frontier juncture sides Xi Helian between each node of every straton network in relational network
Weights determine the standard compactness concentration degree of each node corresponding with the sub-network；Wherein, relational network includes and goal-trail
The importance of part corresponding multilayer sub-network and each straton network；Node includes people to be predicted and locked people；For network of personal connections
Each of network people to be predicted determines sub-network respectively according to the standard compactness concentration degree of each node in every straton network
In people to be predicted and each locked people the degree of association；According to being associated with for people to be predicted in each straton network and each locked people
The importance of degree and each straton network, determines the suspicion index of people to be predicted；According to the suspicion index of each people to be predicted, really
The concealment people for the case that sets the goal.In this way, excavate concealment people by calculating the suspicion index of each people to be predicted, not by it is subjective because
The influence of element, can improve the reliability and accuracy of Result, while providing investigation foundation for relevant department, convenient for having
Pass department carries out further concealment people and investigates work.
Other features and advantages of the present invention will illustrate in the following description, also, partly become from specification
It obtains it is clear that understand through the implementation of the invention.The purpose of the present invention and other advantages are in specification, claims
And specifically noted structure is realized and is obtained in attached drawing.
To enable the above objects, features and advantages of the present invention to be clearer and more comprehensible, preferred embodiment cited below particularly, and coordinate
Appended attached drawing, is described in detail below.
Description of the drawings
It, below will be to specific in order to illustrate more clearly of the specific embodiment of the invention or technical solution in the prior art
Embodiment or attached drawing needed to be used in the description of the prior art are briefly described, it should be apparent that, in being described below
Attached drawing is some embodiments of the present invention, for those of ordinary skill in the art, before not making the creative labor
It puts, other drawings may also be obtained based on these drawings.
Fig. 1 is the first flow diagram of concealment people's method for digging provided in an embodiment of the present invention；
Fig. 2 is the part-structure of a straton network of relational network in concealment people's method for digging provided in an embodiment of the present invention
Schematic diagram；
Fig. 3 is the pass that people and each locked people to be predicted are determined in concealment people's method for digging provided in an embodiment of the present invention
The flow diagram of connection degree；
Fig. 4 is the module composition schematic diagram of concealment people's excavating gear provided in an embodiment of the present invention；
Fig. 5 is the structural schematic diagram of electronic equipment provided in an embodiment of the present invention.
Specific implementation mode
In order to make the object, technical scheme and advantages of the embodiment of the invention clearer, below in conjunction with attached drawing to the present invention
Technical solution be clearly and completely described, it is clear that described embodiments are some of the embodiments of the present invention, rather than
Whole embodiments.Based on the embodiments of the present invention, those of ordinary skill in the art are not making creative work premise
Lower obtained every other embodiment, shall fall within the protection scope of the present invention.
The method for digging of concealment suspect at present, subjective, obtained Result reliability is low, accuracy is poor.
Based on this, a kind of concealment people method for digging, device and electronic equipment provided in an embodiment of the present invention can improve Result
Reliability and accuracy.
For ease of understanding the present embodiment, first to a kind of concealment people method for digging disclosed in the embodiment of the present invention
It describes in detail.
Embodiment one：
Fig. 1 is the first flow diagram of concealment people's method for digging provided in an embodiment of the present invention, as shown in Figure 1, should
Method includes：
Step S101, according to the weights on the company frontier juncture sides Xi Helian between each node per straton network in relational network,
Determine the standard compactness concentration degree of each node corresponding with the sub-network.
Specifically, relational network is to be built by the data in a variety of sources, such as exchange text data, character relation number
According to, consumer record data etc., thus the coverage area of the relational network is more comprehensive, and reliability is high, and practicability is high.The network of personal connections
Network includes the importance of multilayer sub-network and each straton network, corresponds to a kind of specified dimension per straton network, each specified dimension
For characterizing the relationship type between a kind of personnel.Specified dimension can be chosen according to actual demand, such as larceny case, refer to
Determining dimension can choose and exchange the related geography information dimension of text data (close to theft place, the residence etc. of locked people
Place), case content dimension (stolen trade name, stolen object etc.), high word frequency information dimension is (in locked human world exchange of information
The high word frequency information of appearance), theft relevant dimension (the theft degrees of correlation of each personnel) etc., can also choose and character relation number
(room note is opened according to related kinship dimension (lineal relative's information etc.), and room dimension of opening related with consumer record data
Information is recorded, such as opens room frequency), specified dimension is not construed as limiting here.The importance of above-mentioned sub-network is for characterizing the subnet
The correlation degree of network corresponding relationship type and target case.Further, which includes multiple nodes, node packet
People to be predicted and locked people are included, passes through even side connection between each node.There may be corresponding multilayer sub-networks between two nodes
Multiple even sides, it is also possible to there is only a company side (corresponding a straton network), (connected by other nodes even without even side
It is logical).Even sideband has weights, which is used to characterize this and connects correlation degree between corresponding two nodes in side.
According to the weights on the company frontier juncture sides Xi Helian between above-mentioned each node per straton network, determine and the sub-network pair
The standard compactness concentration degree for each node answered, can specifically be divided into following two steps：
(1) according to, per the weights on the company frontier juncture sides Xi Helian between each node of straton network, calculating should in relational network
Shortest path distance in sub-network between each node.
Shortest path distance is related with the even weights on side, has the weights on the company side between the even node of frontier juncture system smaller, connection
Two nodes between path it is shorter.Company side of the shortest path distance not necessarily between two nodes between two nodes
Weights.With reference to figure 2, in certain straton network, there is mulitpath from node a to node d, be respectively：
Path one：Node d is directly reached since node a, corresponding even side right value is 3, then the distance in this path is 3；
Path two：Since node a, by node b, node d is reached, wherein the weights on the company side being related to are respectively：
1,2, then the distance in this path is 3；
Path three：Since node a, by node c, node d is reached, wherein the weights being related to connect the weights point on side
It is not：2,2, then the distance in this path is 4；
Path four：Since node a, by node b, node c, node d is reached, wherein the weights on the company side being related to
Respectively：1,1,2, then the distance in this path is 4；
Path five：Since node a, by node c, node b, node d is reached, wherein the weights on the company side being related to
Respectively：2,1,2, then the distance in this path is 5.
It is possible to egress a to node d shortest path respectively path one and path two, shortest path away from
From being 3.
Calculate shortest path apart from when can be, but not limited to use Dijkstra algorithm (Dijkstra's
Algorithm) or A* algorithms, excessive elaboration is not done here.
(2) it according to the shortest path distance between each node in every straton network, calculates corresponding with the sub-network each
The standard compactness concentration degree of node.
Specifically, the standard compactness concentration degree C ' of node i corresponding with sub-network x can be calculated by the following formulax
(i)：
Wherein, Cx(i) the compactness concentration degree of node i corresponding with sub-network x, N are indicatedxIndicate sub-network x interior joints
Total number, dx(i, j) indicates the shortest path distance between sub-network x interior joints i and node j.
The standard compactness concentration degree to each node in every straton network that can be calculated by above-mentioned formula.
Step S102, for each of above-mentioned relation network people to be predicted, according to each node in every straton network
Standard compactness concentration degree determines the degree of association of people to be predicted and each locked people in the sub-network respectively.
Fig. 3 is the pass that people and each locked people to be predicted are determined in concealment people's method for digging provided in an embodiment of the present invention
The flow diagram of connection degree, as shown in figure 3, in an alternative embodiment, above-mentioned steps S102 is specifically by following step
It is rapid to execute：
Step S301 determines each straton network that the people to be predicted is related to for each of relational network people to be predicted.
Step S302 determines each locked people being connected to above-mentioned people to be predicted in the above-mentioned network per straton.
Step S303, the standard according to the standard compactness concentration degree of above-mentioned people to be predicted and above-mentioned each locked people are tight
Shortest path distance between the two in close property concentration degree and the sub-network, calculate separately in every straton network it is above-mentioned wait for it is pre-
Survey the degree of association of people and above-mentioned each locked people.
Specifically, first true according to the frontier juncture system of company of the people to be predicted for each of above-mentioned relation network people to be predicted
Each straton network that the fixed people to be predicted is related to, then determine per each locked people communicated therewith in straton network, it is then right
In above-mentioned per the locked people of each of straton network, according to the standard compactness concentration degree of the people to be predicted, the locked people
Standard compactness concentration degree and the sub-network in the people to be predicted and the locked people shortest path distance, calculate the son
The degree of association of this in network people to be predicted and the locked people.For example, people A to be predicted be related to sub-network be B and C, in B with A
The locked people of connection has D and E, and the locked people being connected to A in C has F, then calculates separately the degree of association of A and D, A and E in B
The degree of association and C in A and F the degree of association.To calculate in B for the degree of association of A and D：According to the standard compactness collection of A in B
The shortest path distance of moderate, the standard compactness concentration degree of D and A and D calculates the degree of association of A and D in B.
It is possible to further be calculated by the following formula the degree of association γ of people i and locked people j to be predicted in sub-network xx
(i,j)：
Wherein, C 'x(i) the standard compactness concentration degree of to be predicted people i corresponding with sub-network x, C ' are indicatedx(j) indicate with
The standard compactness concentration degree of the corresponding locked people j of sub-network x, dx(i, j) indicates in sub-network x people i to be predicted and has locked
Determine the shortest path distance between people j.
Step S103, according to the degree of association and each straton network of people to be predicted in each straton network and each locked people
Importance, determine the suspicion index of the people to be predicted.
Specifically, suspicion index is used to characterize the correlation degree of the people to be predicted and target case, and suspicion index is bigger, closes
Connection degree is bigger.
In an alternative embodiment, the suspicion index of people to be predicted can be calculated by the following formula：
Wherein, PiIndicate that the suspicion index of people i to be predicted, N indicate total number of plies of the sub-network of relational network, WxIndicate son
The importance of network x, γx(i, j) indicates that the degree of association of people i to be predicted and locked people j in sub-network x, J indicate locked people
Set.
It is related to sub-network with people A to be predicted for B and C, the locked people being connected to A in B has D and E, is connected to A in C
Locked people have F for, the suspicion indices P of AAFor：
PA=WBγB(A,D)+WBγB(A,E)+WCγC(A, F),
Wherein, WBIndicate the importance of sub-network B, WCIndicate the importance of sub-network C, γB(A, D) is indicated in sub-network B
The degree of association of A and D, γB(A, E) indicates the degree of association of A and E in sub-network B, γC(A, F) indicates being associated with for A and F in sub-network C
Degree.
Step S104 determines the concealment people of target case according to the suspicion index of each people to be predicted.
Specifically, first each people to be predicted can be sorted according to suspicion exponential size, then according to each people to be predicted
Put in order, determine the concealment people of target case.
In an alternative embodiment, since the maximum people to be predicted of suspicion index, waiting for for selection setting number is pre-
People is surveyed as concealment people, wherein setting number can be arranged according to actual conditions.In another optional embodiment, it will dislike
The person to be predicted that doubtful index is greater than the set value is taken as concealment people, wherein setting value can be arranged according to actual conditions.
The ranking of concealment people can be objectively provided by the above method, obtained result is more true and reliable, also to have
Pass department provides investigation foundation, carries out further concealment people convenient for relevant department and investigates work.
In the embodiment of the present invention, according to the company frontier juncture sides Xi Helian between each node of every straton network in relational network
Weights determine the standard compactness concentration degree of each node corresponding with the sub-network；Wherein, relational network includes and goal-trail
The importance of part corresponding multilayer sub-network and each straton network；Node includes people to be predicted and locked people；For network of personal connections
Each of network people to be predicted determines sub-network respectively according to the standard compactness concentration degree of each node in every straton network
In people to be predicted and each locked people the degree of association；According to being associated with for people to be predicted in each straton network and each locked people
The importance of degree and each straton network, determines the suspicion index of people to be predicted；According to the suspicion index of each people to be predicted, really
The concealment people for the case that sets the goal.In this way, excavate concealment people by calculating the suspicion index of each people to be predicted, not by it is subjective because
The influence of element, can improve the reliability and accuracy of Result, while providing investigation foundation for relevant department, convenient for having
Pass department carries out further concealment people and investigates work.
Embodiment two：
Fig. 4 is the module composition schematic diagram of concealment people's excavating gear provided in an embodiment of the present invention, as shown in figure 4, the dress
Set including：
Concentration degree determining module 41, for according between each node per straton network in relational network frontier juncture system of company and
The even weights on side determine the standard compactness concentration degree of each node corresponding with the sub-network；Wherein, relational network include with
The importance of target case corresponding multilayer sub-network and each straton network；Node includes people to be predicted and locked people；
Degree of association determining module 42 is used for for each of above-mentioned relation network people to be predicted, according to every straton network
In each node standard compactness concentration degree, determine being associated with for people to be predicted and each locked people in the sub-network respectively
Degree；
Suspicion index determining module 43, for the degree of association according to people to be predicted and each locked people in each straton network
And the importance of each straton network, determine the suspicion index of the people to be predicted；
People's determining module 44 is hidden, for the suspicion index according to each people to be predicted, determines the concealment people of target case.
In the embodiment of the present invention, concentration degree determining module 41 is according between each node of every straton network in relational network
The even weights on the frontier juncture sides Xi Helian determine the standard compactness concentration degree of each node corresponding with the sub-network；Wherein, relationship
Network includes the importance of multilayer sub-network corresponding with target case and each straton network；Node includes people to be predicted and has locked
Determine people；For each of relational network people to be predicted, degree of association determining module 42 is according to each node in every straton network
Standard compactness concentration degree determines the degree of association of people to be predicted and each locked people in sub-network respectively；Suspicion index determines
Module 43 is according to people to be predicted in each straton network and the degree of association of each locked people and the importance of each straton network, really
The suspicion index of fixed people to be predicted；People's determining module 44 is hidden according to the suspicion index of each people to be predicted, determines target case
Concealment people.In this way, excavating concealment people by calculating the suspicion index of each people to be predicted, do not influenced by subjective factor,
The reliability and accuracy of Result can be improved, while investigation foundation is provided for relevant department, is opened convenient for relevant department
The further concealment people of exhibition investigates work.
Embodiment three：
Referring to Fig. 5, the embodiment of the present invention also provides a kind of electronic equipment 100, including：Processor 50, memory 51, bus
52 and communication interface 53, the processor 50, communication interface 53 and memory 51 connected by bus 52；Processor 50 is for holding
The executable module stored in line storage 51, such as computer program.
Wherein, memory 51 may include high-speed random access memory (RAM, Random Access Memory),
May further include non-labile memory (non-volatile memory), for example, at least a magnetic disk storage.By extremely
A few communication interface 53 (can be wired or wireless) is realized logical between the system network element and at least one other network element
Letter connection can use internet, wide area network, local network, Metropolitan Area Network (MAN) etc..
Bus 52 can be isa bus, pci bus or eisa bus etc..The bus can be divided into address bus, data
Bus, controlling bus etc..Only indicated with a four-headed arrow for ease of indicating, in Fig. 5, it is not intended that an only bus or
A type of bus.
Wherein, memory 51 is for storing program, and the processor 50 executes the journey after receiving and executing instruction
Sequence, the method performed by device that the stream process that aforementioned any embodiment of the embodiment of the present invention discloses defines can be applied to handle
In device 50, or realized by processor 50.
Processor 50 may be a kind of IC chip, the processing capacity with signal.During realization, above-mentioned side
Each step of method can be completed by the integrated logic circuit of the hardware in processor 50 or the instruction of software form.Above-mentioned
Processor 50 can be general processor, including central processing unit (Central Processing Unit, abbreviation CPU), network
Processor (Network Processor, abbreviation NP) etc.；It can also be digital signal processor (Digital Signal
Processing, abbreviation DSP), application-specific integrated circuit (Application Specific Integrated Circuit, referred to as
ASIC), ready-made programmable gate array (Field-Programmable Gate Array, abbreviation FPGA) or other are programmable
Logical device, discrete gate or transistor logic, discrete hardware components.It may be implemented or execute in the embodiment of the present invention
Disclosed each method, step and logic diagram.General processor can be microprocessor or the processor can also be to appoint
What conventional processor etc..The step of method in conjunction with disclosed in the embodiment of the present invention, can be embodied directly in hardware decoding processing
Device executes completion, or in decoding processor hardware and software module combination execute completion.Software module can be located at
Machine memory, flash memory, read-only memory, programmable read only memory or electrically erasable programmable memory, register etc. are originally
In the storage medium of field maturation.The storage medium is located at memory 51, and processor 50 reads the information in memory 51, in conjunction with
Its hardware completes the step of above method.
It is apparent to those skilled in the art that for convenience and simplicity of description, the concealment of foregoing description
The specific work process of people's excavating gear and electronic equipment, can refer to corresponding processes in the foregoing method embodiment, herein not
It repeats again.
Concealment people excavating gear and electronic equipment provided in an embodiment of the present invention are dug with the concealment people that above-described embodiment provides
Pick method technical characteristic having the same reaches identical technique effect so can also solve identical technical problem.
Flow chart and block diagram in attached drawing show the system, method and computer journey of multiple embodiments according to the present invention
The architecture, function and operation in the cards of sequence product.In this regard, each box in flowchart or block diagram can generation
A part for a part for one module, section or code of table, the module, section or code includes one or more uses
The executable instruction of the logic function as defined in realization.It should also be noted that in some implementations as replacements, being marked in box
The function of note can also occur in a different order than that indicated in the drawings.For example, two continuous boxes can essentially base
Originally it is performed in parallel, they can also be executed in the opposite order sometimes, this is depended on the functions involved.It is also noted that
It is the combination of each box in block diagram and or flow chart and the box in block diagram and or flow chart, can uses and execute rule
The dedicated hardware based system of fixed function or action is realized, or can use the group of specialized hardware and computer instruction
It closes to realize.
What the embodiment of the present invention was provided carries out the computer program product of concealment people's method for digging, including stores processing
The computer readable storage medium of the executable non-volatile program code of device, the instruction that said program code includes can be used for holding
Method described in row previous methods embodiment, specific implementation can be found in embodiment of the method, and details are not described herein.
In several embodiments provided herein, it should be understood that disclosed method, apparatus and electronic equipment,
It may be implemented in other ways.The apparatus embodiments described above are merely exemplary, for example, the unit is drawn
Point, only a kind of division of logic function, formula that in actual implementation, there may be another division manner, in another example, multiple units or group
Part can be combined or can be integrated into another system, or some features can be ignored or not executed.Another point, it is shown
Or the mutual coupling, direct-coupling or communication connection discussed can be by some communication interfaces, device or unit
INDIRECT COUPLING or communication connection can be electrical, machinery or other forms.
The unit illustrated as separating component may or may not be physically separated, aobvious as unit
The component shown may or may not be physical unit, you can be located at a place, or may be distributed over multiple
In network element.Some or all of unit therein can be selected according to the actual needs to realize the mesh of this embodiment scheme
's.
In addition, each functional unit in each embodiment of the present invention can be integrated in a processing unit, it can also
It is that each unit physically exists alone, it can also be during two or more units be integrated in one unit.
It, can be with if the function is realized in the form of SFU software functional unit and when sold or used as an independent product
It is stored in the executable non-volatile computer read/write memory medium of a processor.Based on this understanding, of the invention
Technical solution substantially the part of the part that contributes to existing technology or the technical solution can be with software in other words
The form of product embodies, which is stored in a storage medium, including some instructions use so that
One computer equipment (can be personal computer, server or the network equipment etc.) executes each embodiment institute of the present invention
State all or part of step of method.And storage medium above-mentioned includes：USB flash disk, mobile hard disk, read-only memory (ROM, Read-
Only Memory), random access memory (RAM, Random Access Memory), magnetic disc or CD etc. are various can be with
Store the medium of program code.
Finally it should be noted that：Embodiment described above, only specific implementation mode of the invention, to illustrate the present invention
Technical solution, rather than its limitations, scope of protection of the present invention is not limited thereto, although with reference to the foregoing embodiments to this hair
It is bright to be described in detail, it will be understood by those of ordinary skill in the art that：Any one skilled in the art
In the technical scope disclosed by the present invention, it can still modify to the technical solution recorded in previous embodiment or can be light
It is readily conceivable that variation or equivalent replacement of some of the technical features；And these modifications, variation or replacement, do not make
The essence of corresponding technical solution is detached from the spirit and scope of technical solution of the embodiment of the present invention, should all cover the protection in the present invention
Within the scope of.Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (8)
1. a kind of concealment people's method for digging, which is characterized in that including：
According to the weights on the company frontier juncture sides Xi Helian between each node of every straton network in relational network, determine and the subnet
The standard compactness concentration degree of the corresponding each node of network；Wherein, the relational network includes corresponding with target case
The importance of sub-network described in multilayer and each layer sub-network；The node includes people to be predicted and locked people；
For each of the relational network people to be predicted, according to the mark of each node in sub-network described in every layer
Quasi- compactness concentration degree determines the degree of association of people to be predicted and each locked people described in the sub-network respectively；
According to the degree of association and each layer of people to be predicted described in each layer sub-network and each locked people
The importance of network determines the suspicion index of the people to be predicted；
According to the suspicion index of each people to be predicted, the concealment people of the target case is determined；
The weights on the company frontier juncture sides Xi Helian between each node according to per straton network in relational network, determine with it is described
The standard compactness concentration degree of the corresponding each node of sub-network, including：
According to the weights on the company frontier juncture sides Xi Helian between each node of every straton network in relational network, the sub-network is calculated
In shortest path distance between each node；
According to the shortest path distance between each node in sub-network described in every layer, calculate corresponding with the sub-network every
The standard compactness concentration degree of a node；
It is calculated by the following formula the standard compactness concentration degree of each node corresponding with the sub-network：
Wherein, Cx(i) the compactness concentration degree of node i corresponding with sub-network x, C are indicatedx' (i) indicates corresponding with sub-network x
The standard compactness concentration degree of node i, NxIndicate the total number of sub-network x interior joints, dx(i, j) indicates sub-network x interior joints i
Shortest path distance between node j.
2. according to the method described in claim 1, it is characterized in that, described pre- for being waited for described in each of described relational network
People is surveyed, according to the standard compactness concentration degree of each node in sub-network described in every layer, is determined in the sub-network respectively
The degree of association of the people to be predicted and each locked people, including：
For each of the relational network people to be predicted, each layer subnet that the people to be predicted is related to is determined
Network；
In every layer of sub-network, each locked people being connected to the people to be predicted is determined；
For each of being connected to the locked people in sub-network described in every layer with the people to be predicted, according to the people to be predicted
Standard compactness concentration degree, the standard compactness concentration degree of the locked people and the people to be predicted with it is described locked
The shortest path distance of people calculates the degree of association of people and the locked people to be predicted described in the sub-network.
3. according to the method described in claim 2, it is characterized in that, be calculated by the following formula in sub-network x people i to be predicted with
The degree of association γ of locked people jx(i,j)：
Wherein, C 'x(i) the standard compactness concentration degree of to be predicted people i corresponding with sub-network x, C ' are indicatedx(j) expression and subnet
The standard compactness concentration degree of the corresponding locked people j of network x, dx(i, j) indicates people i to be predicted and locked people j in sub-network x
Between shortest path distance.
4. according to the method described in claim 1, it is characterized in that, the suspicion for being calculated by the following formula the people to be predicted refers to
Number：
Wherein, PiIndicate that the suspicion index of people i to be predicted, N indicate total number of plies of the sub-network of the relational network, WxIndicate son
The importance of network x, γx(i, j) indicates the degree of association of people i to be predicted and locked people j in sub-network x, has been locked described in J expressions
Determine the set of people.
5. according to the method described in claim 1, it is characterized in that, the suspicion index according to each people to be predicted,
Determine the concealment people of the target case, including：
Each people to be predicted is sorted according to suspicion exponential size；
According to putting in order for each people's suspicion index to be predicted, the concealment people of the target case is determined.
6. a kind of concealment people's excavating gear, which is characterized in that including：
Concentration degree determining module, for according to the company frontier juncture sides Xi Helian between each node of every straton network in relational network
Weights determine the standard compactness concentration degree of each node corresponding with the sub-network；Wherein, the relational network packet
Include the importance of sub-network and each layer sub-network described in multilayer corresponding with target case；The node includes people to be predicted
With locked people；
Degree of association determining module is used for for each of the relational network people to be predicted, according to subnet described in every layer
The standard compactness concentration degree of each node in network, respectively determine the sub-network described in people to be predicted with it is each described
The degree of association of locked people；
Suspicion index determining module is used for according to people to be predicted described in each layer sub-network with each locked people's
The importance of the degree of association and each layer sub-network determines the suspicion index of the people to be predicted；
People's determining module is hidden, for the suspicion index according to each people to be predicted, determines the concealment of the target case
People；
The concentration degree determining module is specifically used for：
According to the weights on the company frontier juncture sides Xi Helian between each node of every straton network in relational network, the sub-network is calculated
In shortest path distance between each node；
According to the shortest path distance between each node in sub-network described in every layer, calculate corresponding with the sub-network every
The standard compactness concentration degree of a node；
It is calculated by the following formula the standard compactness concentration degree of each node corresponding with the sub-network：
Wherein, Cx(i) the compactness concentration degree of node i corresponding with sub-network x, C ' are indicatedx(i) indicate corresponding with sub-network x
The standard compactness concentration degree of node i, NxIndicate the total number of sub-network x interior joints, dx(i, j) indicates sub-network x interior joints i
Shortest path distance between node j.
7. a kind of electronic equipment, including memory, processor, be stored in the memory to run on the processor
Computer program, which is characterized in that the processor is realized any in the claims 1-5 when executing the computer program
Method described in.
8. a kind of computer-readable medium for the non-volatile program code that can perform with processor, which is characterized in that described
Program code makes the processor execute any the method in the claims 1-5.
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| CN110188325B (en) * | 2019-06-04 | 2022-12-09 | 杭州中奥科技有限公司 | Hidden node feature extraction method and device, electronic equipment and readable storage medium |
| CN110175791B (en) * | 2019-06-04 | 2021-08-27 | 杭州中奥科技有限公司 | Stagnation point determination method and stagnation point determination device |
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| US9077744B2 (en) * | 2013-03-06 | 2015-07-07 | Facebook, Inc. | Detection of lockstep behavior |
| US9648030B2 (en) * | 2014-11-11 | 2017-05-09 | International Business Machines Corporation | Identifying an imposter account in a social network |
| CN104408149B (en) * | 2014-12-04 | 2017-12-12 | 威海北洋电气集团股份有限公司 | Suspect based on social network analysis excavates correlating method and system |
| CN105205155B (en) * | 2015-09-25 | 2019-05-03 | 珠海世纪鼎利科技股份有限公司 | A kind of screening system and method for big data crime partner |
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