CN110033277A - Risk trade identification device, method and medium - Google Patents
Risk trade identification device, method and medium Download PDFInfo
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Abstract
The present invention provides a kind of recognition methods for risk trade and device, and this method includes choosing multiple trade orders；Establish primitive network relational graph, wherein the primitive network relational graph includes and represents more than first a service provider's nodes of the business service quotient and represent more than second a user nodes of the user, and every line between service provider's node and user node represents an order in the multiple trade order；Twoth k value of the setting for the first k value of service provider's node and for the user node；Using the first k value and the 2nd k value respectively in the primitive network relational graph service provider's node and the user node iterated application K-core algorithm to generate first network relational graph；Identifying the transaction corresponding to the service provider's node and user node in the first network relational graph there are still line between business service quotient and user is risk trade.It can be to avoid judging or miss risk trade by accident using the solution of the present invention.
Description
Technical field
The present invention relates to risk controls, are especially accused of the identification of the risk trade of fraud.
Background technique
In the prior art, usually certain third party transaction platforms are in order to promote the purpose of product to user, and on platform
Carry out promotion subsidy in pin product, and promote and cling use of the users group to platform, ' product ' here with this
The tangible products that can be produced, the valuable service that can also be provided with business service quotient.By taking film industry as an example, viewing booking mesh
Before be a market with keen competition, the naughty ticket ticket, other bean cotyledon films, opal film etc. as third party transaction platform are all
Competitor.In such keen competition environment, the subsidy of third party transaction platform money obtains consumer loyalty degree and can not almost keep away
Exempt from.In order to open up markets, the booking class App that third-party platform is released obtains traveller by way of subsidizing film fare, and
This behavior is easy to combine set with part spectators (i.e. so-called ' ox ') by movie theatre (i.e. business service quotient or product vendor)
Subsidy interests are taken, not only do not play the role of promoting App, obtain and stablize traveller, also have lost a large amount of marketing money.
In the prior art, it for the subsidy of third party's booking platform, markets to solve ox ticket scalper and movie theatre arbitrage, usually
By the way of blacklist, including black user, black identity, black phone, black equipment, black bank card etc., it hits in actual use
Either of which identification is to do ox buying behavior.But the accuracy of blacklist is not 100 percent reliable, such as one
Cell-phone number may be recycled by operator, then a handy family can not just enjoy benefit when buying film ticket with this new cell-phone number
Patch, affects platform public praise, causes the distrust to platform.Another problem of such scheme is to identify not entirely.Cinema
As long as fixed ticket-holder's " cooperation " with a part can extract interests, and these " partners " can not be complete by blacklist
Covering.As long as cinema finds user of a batch not in blacklist, resource can be largely extracted without being penetrated, causes resource
Waste.
Summary of the invention
The present invention provides a kind of method for identifying exception or risk trade, is that there may be risk trades by mark
The mode of both sides' main body determines risk trade.The present invention is not limited to film booking this be trading activity fraud prevention,
And it is suitable for other industries by both parties' common implementing risk trade,.
According to one aspect of the present invention, a kind of recognition methods for risk trade is provided, including chooses multiple transaction
Order, wherein each order represents the transaction more than first between one of one of a business service quotient and more than second a users；It establishes
Primitive network relational graph, wherein the figure includes to represent described in more than first a service provider's nodes and the representative of the business service quotient
A user node more than the second of user, and every line between service provider's node and user node represents the multiple transaction
An order in order；Twoth k value of the setting for the first k value of service provider's node and for user node；Using institute
State the first k value and the 2nd k value respectively in the cyberrelationship figure service provider's node and the user node iterated application
K-core algorithm is to generate first network relational graph；Identify the service provider in the first network relational graph there are still line
Transaction between business service quotient and user corresponding to node and user node is risk trade.
In one embodiment of the invention, the business service quotient is cinema, user's film ticket-holder.
Other side according to the invention provides a kind of identification device of risk trade, comprising: obtains module, configuration
To choose multiple trade orders, wherein each order represent one of one of a business service quotient and more than second a users more than first it
Between transaction；Composition module is configured to establish primitive network relational graph, and wherein the figure includes represent the business service quotient the
A service provider's node more than one and more than the second a user nodes for representing the user, and service provider's node and user node it
Between every line represent an order in the multiple trade order；Computing module is configured that setting is saved for service provider
The first k value put and the 2nd k value for user node；Using the first k value and the 2nd k value respectively to the network
Service provider's node and the user node iterated application K-core algorithm in relational graph is to generate first network relational graph；And
Mark module, is configured to identify in the first network relational graph that there are still service provider's nodes of line and user node institute
Transaction between corresponding business service quotient and user is risk trade.
According to another aspect of the present invention, a kind of machine readable media with instruction is provided, described instruction is in machine
When execution, the machine is made to execute method of the invention.
According to another aspect of the present invention, a kind of identification device of risk trade is provided, comprising: memory is deposited thereon
Contain instruction；Processor, the processor can be configured to execute described instruction to realize according to the method for the present invention.
Detailed description of the invention
Fig. 1 shows the block diagram of risk trade identification device according to an embodiment of the invention；
Fig. 2 shows the flow charts of risk trade recognition methods according to an embodiment of the invention；
Fig. 3 shows the schematic diagram of the creation cyberrelationship figure according to one embodiment；
Fig. 4 shows the schematic diagram of the cyberrelationship figure created according to one embodiment；
Fig. 5 shows the method flow diagram of the processing cyberrelationship figure according to one embodiment；
Fig. 6 shows the schematic diagram according to one embodiment treated cyberrelationship figure；
Fig. 7 shows the method flow diagram of the processing cyberrelationship figure according to one embodiment；
Fig. 8 is the schematic diagram according to the identification device of another embodiment.
Specific embodiment
Method and apparatus provided in an embodiment of the present invention are described in detail with reference to the accompanying drawing.Although being shown in attached drawing
The preferred embodiment of the disclosure, however, it is to be appreciated that may be realized in various forms the disclosure without that should be illustrated here
Embodiment limited.On the contrary, these embodiments are provided so that this disclosure will be more thorough and complete, and can incite somebody to action
The scope of the present disclosure is fully disclosed to those skilled in the art.
It is to be herein pointed out ' risk trade ' suggested by the present invention refers to the both sides i.e. business service for implementing transaction
There may be the transaction of irregularity between quotient and certain user, the arbitrage of such as certain cinemas and ox ticket-holder market this
The fraudulent trading of sample, and these transaction can be predicted by the trade order having occurred and that.
As shown in Figure 1, its block diagram for showing risk trade identification device 100 according to the present invention.As shown, identification dress
Setting 100 includes obtaining module 101, composition module 102, computing module 103 and mark module 104.Obtaining module 101 can be from
Multiple trade orders to be identified are chosen in a large amount of trade orders occurred between multiple business service quotient and multiple users, wherein
Each order represents the transaction between a business service quotient and a user, it is assumed here that selected order occurs at M
Between business service quotient and N number of user.In the present invention, obtain module 101 choose the standard of trade order to be identified can be with
Sets itself as needed.It is to be herein pointed out may have between same business service quotient and same user multiple
Order can only select an order between the two here.
Composition module 102 establishes cyberrelationship figure using multiple trade orders to be identified that module 101 is chosen are obtained, with
Under with Graph0It indicates.The cyberrelationship figure Graph established0It include M service provider's node for representing the business service quotient
And N number of user node of the user is represented, and the line between service provider's node and user node represents therebetween
The order occurred.
The cyberrelationship figure Graph that computing module 103 establishes composition module 102 using K-core algorithm0It is handled.
For this purpose, computing module 103 is service provider's node sets k value K1, and be user node setting k value K2, then, based on described
K1And K2For as benchmark is judged, respectively to M service provider's node with N number of user node iterated application K-core algorithm to update
The cyberrelationship figure.Specifically, computing module 103 calculates separately each of M service provider's node using K-core algorithm
The degree of each user node in the degree of service provider's node and N number of user node, by deleting its degree from cyberrelationship figure
Value is less than K1Service provider's node and coupled whole lines, and delete its angle value less than K2User node and and its
Connected whole lines, so that the cyberrelationship figure of update is generated, below with Graph1It indicates.
Mark module 104 confirms that there are still lines in updated cyberrelationship figure according to the cyberrelationship figure of update
Service provider's node and user node, and by business service quotient corresponding to the service provider's node confirmed and user node with
Order note identification between user is risk trade.It should be pointed out that transaction identified herein is not limited in choosing module
The order of 101 selections, but contain the whole orders occurred between the business service quotient confirmed and user.
Here K1And K2Setting both can rule of thumb set, can also pass through setting initial value and constantly adjustment K1
And K2The mode of value finds suitable k value, wherein to K1And K2Adjustment can be based on each node that mark module 104 is identified
Reasonability carry out.In one embodiment, using the K being adjusted1And K2Value is again to initial to composition module 102
The primitive network relational graph Graph of foundation0Using k-core algorithm；Separately in one embodiment, using the K being adjusted1
And K2Value is to updated cyberrelationship figure Graph1Using k-core algorithm, to be continuously updated cyberrelationship figure.
Fig. 2 shows the method flow diagrams according to the present invention implemented by risk trade identity device.As shown, in step
Rapid 201, multiple transaction to be identified are chosen from a large amount of trade orders occurred between multiple business service quotient and multiple users
Order, as previously mentioned, can only select an order between same user and same service provider.In general, the mesh of the selection criteria
Be to delete to select unnecessary order as far as possible, such as the obvious order extremely low there is no risk trade or risk trade possibility,
So that subsequent identification device 100 is operated in small-scale order and is carried out.
In step 202, cyberrelationship figure is established, wherein the figure includes the M service provider section for representing the business service quotient
Point and the N number of user node for representing the user, and every line between service provider's node and user node represents institute
State an order in multiple trade orders.
In step 203, setting is used for the k value K of service provider's node1And the k value K for user node2.In step 204,
Utilize the K1And K2Respectively to M service provider's node and N number of user node iterated application K-core algorithm.Such as M
I-th of node in service provider's node calculates it and spends and judge whether to be less than K1, wherein if it is less than K1, then i-th of clothes is deleted
Business quotient's node and coupled whole lines；Simultaneously for any one user node such as j-th of section in N number of user node
Point calculates it and spends and judge whether to be less than K2, wherein if it is less than K2, then j-th of user node and coupled whole are deleted
Line.The cyberrelationship figure Graph updated after K-core algorithm process1。
In step 205, confirm that there are still service provider's nodes of line and user to save in updated cyberrelationship figure
Point, and be risk trade by the transaction ID between the business service quotient and user corresponding to it, it thus can also identify possibility
The business service quotient and user of implement general plan transaction, it is clear that whole orders between the two are risk trade.
Illustrate to be likely to occur in using present invention identification below in conjunction with this business of film booking cinema and ox it
Between risk trade, that is, belong to arbitrage marketing fraudulent trading.Since this behavior of cinema is not " individual traveler " property, lead to
Often ticket-holder, that is, ox cooperating is fixed with a batch.Since the subsidy dynamics of third-party platform is usually very big, such as 100 yuan
Film ticket need to only be bought with 10 yuan of even lower prices, and the interests space that wherein cinema and ox are divided the spoils is very big, third party
Platform is suffered heavy losses.Business service quotient is cinema in this example, and user is exactly film ticket-holder, it is clear that ox also belongs to booking
A part of person.
Identification device 100 for example washes in a pan ticket net by third party transaction platform and obtains a large amount of purchases that user occurs on the platform
Ticket order, as shown in Fig. 3 left-half, the information of each order includes cinema's title, User ID, projection time and film
Title；The information of obvious order further includes the (not shown)s such as order price.It can be appreciated that these orders relate to it is different
Movie theatre and different users, or even be also likely to be more play films of the different time between same influence and same user.
Selecting module 101 selects roughly to be identified order from acquired a large amount of film ticket orders according to certain standard
Single (corresponding step 201).As previously mentioned, only putting into large scale in third party transaction platform in booking arbitrage marketing behavior
Marketing money when just there is interests space to carry out arbitrage in movie theatre and ticket-holder, and third-party platform only gives the order subsidized on a small quantity
Substantially it is considered that no risk.Therefore, as a selection criteria example, original cost of the selecting module 101 based on each order
(i.e. user's actual delivery admission fee) and the original cost (i.e. the original cost of every film of cinema projection) of corresponding film are from a large amount of booking orders
In only choose the order that the subsidy amount of money is more than order original cost 50%, such as the third party transaction platform in upper example subsidizes 90 yuan of admission fees
Order.In as an example, ordering between same movie theatre and ticket-holder can also will occur in selected order
It is single to remove.
Composition module 102 establishes booking cyberrelationship figure Graph based on selected order0.Specifically, for selected
Each order is establishing booking cyberrelationship figure Graph0When only with reference to two realities of cinema and ticket-holder on every order
Body, and network composition (corresponding step 202) is carried out using this two entity informations.Specifically, composition module 102 by cinema and
Ticket-holder is instantiated as the node on network, i.e. movie theatre node and spectators' node respectively, wherein each cinema and each booking
Person is an independent node and has independent identity such as cell-phone number etc..As shown in figure 3, for first booking
Order, 102 example of composition module dissolves AA movie theatre node and spectators' node 123****9876, and is based on the order, in AA movie theatre
It is attached between node and spectators' node 123****9876 with line.Similarly to second booking order in figure, due to
It is the order occurred in same movie theatre AA, therefore only example dissolves spectators node 123****9875, while base to composition module 102
In the order, it is attached between AA movie theatre node and spectators' node 123****9875 with line.By this method, composition mould
Block 102 handles other orders in selected order, to establish cyberrelationship figure, such as it comprises M movie theatre nodes
And N number of spectators' node, and it is based on each order, come between movie theatre node and spectators' node involved in order with line
Connection.Fig. 4 shows a schematic representation of the cyberrelationship figure constructed by this method, and wherein filled circles represent movie theatre section
Point, and open circles then represent spectators' node, here only symbolically by taking 5 movie theatre nodes and 10 spectators' nodes as an example.
Computing module 103 is followed by k-core algorithm to the cyberrelationship figure between the cinema and ticket-holder constructed
Graph0It is handled and (corresponds to step 203).In an embodiment of the present invention, computing module 103 sets two K values, that is, uses
In the k value K of movie theatre node1And the k value K for spectators' node2.The K1 meaning set herein, which refers to, realizes that arbitrage is handed over to expectation
For easy movie theatre and ox, every, movie theatre required ox ticket-holder quantity, what K2 was indicated is each ox while servicing
In movie theatre quantity.Due to the property of this black industry business, the two is usually all of certain scale to get a profit.Here K1With
And K2Value can rule of thumb be arranged.Then, computing module 103 utilizes set K1And K2Respectively to cyberrelationship
Scheme Graph0In movie theatre node and spectators' node iterated application K-core algorithm.Computing module 103 calculates each movie theatre section
The degree of point, determines whether its degree is less than K1, wherein if it is less than K1, then delete the movie theatre node and be connected with spectators' node complete
Portion's line.In addition, computing module 103 calculates the degree of each spectators' node, determine whether its degree is less than K2, wherein if it is less than
K2, then whole lines for deleting spectators' node and being connected with movie theatre node.It is less than the movie theatre section of K1 by constantly degree of deletion
Point and degree are less than spectators' node of K2, until that cannot delete more point positions, to generate the cyberrelationship figure of update
Graph1。
Mark module 104 can be based on cyberrelationship figure Graph as a result,1Determine Graph1In remaining movie theatre node and spectators
Node is there may be the cinema of risk trade and ox, and booking order between the two is risk trade.
Fig. 5 shows an exemplary illustration of the K-core algorithm as performed by computing module 103, below in conjunction with Fig. 4
The cyberrelationship figure of foundation illustrates.As shown in figure 5, computing module sets the movie theatre that K-core algorithm uses in step 501
The k value K of node1With the k value K of spectators' node2.Then in step 502, computing module obtains cyberrelationship figure Graph0In about
The relation data of movie theatre node, and select cyberrelationship figure Graph0In a movie theatre node, such as movie theatre nodeThen
In step 503, computing module 103 calculates movie theatre nodeDegree, i.e. nodeWith cyberrelationship figure Graph0In other spectators
The number in the connected sideline of node.In step 504, movie theatre node is judgedDegree whether be less than K1.When movie theatre nodeDegree
Less than K1When, step 505 is proceeded to, computing module 103 deletes movie theatre nodeAnd its line with spectators' node 3. 5..Then
Step 505 is proceeded to, judges cyberrelationship figure Graph0In whether there are also untreated other movie theatre nodes, if there are also do not locate
The movie theatre node of reason then returns to step 502, continues to select next movie theatre node, such as movie theatre nodeThen it repeats
Step 503-506, until having handled whole movie theatre nodes.
The K of movie theatre node is set in 501 computing module of above-mentioned steps1With spectators' node K2It later, can also be simultaneously to sight
Many nodes execute k-core algorithm.Similar with the processing of movie theatre node, in step 502', computing module obtains cyberrelationship figure
Graph0About the relation data of spectators' node, and select cyberrelationship figure Graph0In spectators' node, such as spectators
Node is 1..Then in step 503', the degree of 103 calculate node of computing module 1..In step 504', judge movie theatre node 1.
Whether degree is less than K2.When the degree of node 1. is less than K2When, proceed to step 505', computing module 103 delete spectators' node 1. and its
With movie theatre nodeLine.Step 506' is then proceeded to, judges cyberrelationship figure Graph0In whether there are also untreated
Other spectators' nodes continue to select next spectators' node, example if there are also step 502' is returned to if untreated spectators' node
2. such as spectators' node, step 503'-506' is then repeated, until having handled whole spectators' nodes.
Fig. 6, which is shown using K1=3 and K2=2, handles the Graph of cyberrelationship figure shown in Fig. 40Obtained update figure
Graph1。
As shown in fig. 6, relational graph Graph after treatment1In, only movie theatre nodeDegree be greater than K1 (=
3), therefore it is retained in relational graph Graph1.Only the degree of spectators' node 3. 5. 7. 9. is greater than K2 value simultaneously, is retained in relational graph
Graph1.It can be seen that the relational graph Graph obtained after K-core algorithm process1One is indicated by cinemaThe community 3. 5. 7. 9. constituted with spectators, i.e. in Fig. 6 shown in dotted line frame, wherein all spectators ticket-holders in community
All from community K2 or more than cinema buy saver ticket, while K1 or more all into community of all movie theatres in community
A ticket-holder sells saver ticket, and the community in dotted line frame constitutes the subject of implementation there are risk trade here.
According to relational graph Graph1Shown in community, for example 1. mark module 104 can determine the node outside dotted line frame
2. it is normal spectators Deng spectators representated by (being not entirely shown in figure), and movie theatre nodeEtc. being normal movie institute.Same markers
Knowing module 104 can be confirmed that 3. 5. 7. 9. corresponding ticket-holder may be ox to spectators' node, with movie theatre node
There may be the transaction for the marketing resource for extracting third-party platform, i.e. movie theatre node between corresponding cinemaIt is corresponding
Cinema and the ticket-holder that 3. 7. indicates of spectators' node between order belong to risk trade, movie theatre nodeCorresponding electricity
The order of movie theatre and spectators' node 3. 5. 9. between indicated ticket-holder belongs to risk trade, movie theatre nodeCorresponding electricity
The order of movie theatre and spectators' node 5. 7. 9. between indicated ticket-holder belongs to risk trade.Identification device 100 can be with as a result,
The mark of output identification module 104 as a result, and the main platform of third can execute corresponding control according to the mark result, thus
It avoids losing.Such as identifying movie theatre nodeCorresponding cinema and spectators 3. 7. between order belong to risk trade
As a result, third party transaction platform can stop to cinemaIt is sold to the order transaction of ticket-holder 3. 7. to subsidize, cancel
Its preferential qualification etc..
What needs to be explained here is that due to the property of this arbitrage marketing, it is desirable that both movie theatre and ox are usually all
It is of certain scale to get a profit.Therefore, in carrying out the present invention, for the k value of movie theatre node used by k-core algorithm
With k value, that is, K of spectators' node1With K2, can choose from lesser value and begin trying the net established using k-core algorithm process
Network relational graph, such as suggest that K1 selects 1/10, the K2 of every film seat quantity to select 5, then each trial is handled to obtain
Community result assessed.When assessment result is dissatisfied or unreasonable, K can be gradually increased1With K2Value, until generate conjunction
The community of reason finds result.In another embodiment of the present invention, for the k value of movie theatre node used by k-core algorithm
With the k value K of spectators' node1With K2, it also can choose and begun trying from biggish value, the society that then each trial is handled
Group's result is assessed.When assessment result is dissatisfied or unreasonable, K can be gradually reduced1With K2Value, it is reasonable until generating
Community finds result.As for assessment community it is whether reasonable, then can for example by assessment community involved in cinema's range,
Ticket-holder's quantity carries out, and can also be assessed by investigating the returned ticket quantity on order occurred therebetween, these belong to ability
Domain common sense, details are not described herein.
After utilizing present invention determine that going out the trading activities such as fraud, disadvantage knowledge, lower identified ticket-holder, such as its can record
The Terminal Equipment Identifiers such as cell-phone number, and prevention and control are carried out by the blacklists such as black equipment, black phone mode, i.e., it will occur afterwards
The equipment of cheating, cell-phone number are placed on record, typing blacklist, and prevention and control are carried out in booking next time.
Shown in Fig. 5 utilizes K-core algorithm process Graph0Process in, be to movie theatre node and spectators' node
It is performed simultaneously k-core algorithm.Another embodiment according to the invention, can also on the basis of having handled all movie theatre nodes,
Reprocess spectators' node.For example, firstly, computing module 103 is directed to cyberrelationship figure Graph0It is each in middle M movie theatre node
A node, determines whether its degree is less than k value K1, if it is less than K1, then the movie theatre node and coupled whole lines are deleted,
To form primary network relational graph.The primary network relational graph data are then based on, computing module is directed to again in N number of spectators' node
Each node, determine whether its degree in primary network relational graph is less than k value K2, wherein if it is less than the 2nd k value K2, then
Spectators' node and coupled whole lines are deleted, to generate the cyberrelationship figure Graph of the update1.Obviously,
K-Core algorithm can be implemented to spectators' node in cyberrelationship figure first, then again to movie theatre node processing.By the party
Calculation amount can be effectively reduced in method.
It in another embodiment of the invention, can also optional Graph0In a node, either movie theatre node is also
It is spectators' node, k-core algorithm process is carried out to it, until has handled Graph0In whole movie theatre nodes and spectators' node.
Fig. 7 shows the process flow diagram according to the embodiment.
As shown, computing module sets the K of movie theatre node in step 7011With the K of spectators' node2.Then in step
702, computing module receives cyberrelationship figure Graph0In the node set number that is made of M movie theatre node and N number of spectators' node
According to, and a node in set is chosen as present node, then in step 703, computing module 103, which calculates, currently to be chosen
The degree of node.In step 704, the current type for choosing node is judged, that is, judge that the node is movie theatre node or spectators' section
Point.If it is determined that present node is movie theatre node, then step 705 is arrived before process.In step 705, according to the k value of movie theatre node
The node currently chosen is handled, including judging whether the degree of the current movie theatre node is less than K1, wherein when movie theatre node
Degree is less than K1When, computing module 103 deletes the movie theatre node currently chosen and its line with spectators' node.Then proceed to step
Rapid 707, relational graph Graph is updated herein0, it is deleted due to deleting current movie theatre node and its line, will affect pass
System figure Graph0In associated other spectators' nodes line i.e. spend.
If judging that the current type for choosing node is spectators' node in step 704, then arriving step 706 before process.
In step 706, according to the k value K of spectators' node2The node currently chosen is handled, including judging current spectators' node
Whether degree is less than K2, wherein the degree when spectators' node is less than K2When, computing module 103 delete the spectators' node currently chosen and its
With the line of movie theatre node.Step 707 is then proceeded to, updates relational graph Graph herein0, due to deleting current spectators' section
Point and its line, therefore will affect relational graph Graph0In associated other movie theatre nodes line i.e. spend.
Then in step 708, cyberrelationship figure Graph is judged0In whether there are also untreated other any nodes (including
Movie theatre node or spectators' node), if there are also step 702 is returned to if untreated movie theatre node, continue to select next section
Then point repeats step 703-708, until having handled whole nodes in whole set.
Although being to describe preferred reality of the invention by taking the arbitrage deal between cinema-ox as an example in the present embodiment
Example is applied, it is apparent that the invention is not limited thereto, but can be adapted for the identification of other risk trades.In addition, each module in Fig. 1
It may include processor, electronic equipment, hardware device, electronic component, logic circuit, memory, software code, firmware code
Deng or their any combination.Technical staff will also be appreciated that in conjunction with the various illustrative of disclosure description
Logic block, module and method and step can be implemented as the combination of electronic hardware, computer software or both.It is implemented in software
For, it is by processor by computer corresponding in nonvolatile memory as the identification device on a logical meaning
Program instruction reads what operation in memory was formed.For hardware view, as shown in figure 8, in one implementation, according to this
The identification device of invention can realize by one or more computers, in addition to processor shown in Fig. 8, memory, network interface with
And except nonvolatile memory, realize that the computer of identification device generally according to its actual functional capability, can also wrap in embodiment
Other hardware are included, this is repeated no more.
Another embodiment of the present invention provides machine readable media on be stored with machine readable instructions, the machine readable instructions
When being computer-executed, computer is made to execute any method above-mentioned disclosed herein.Specifically, it can provide with organic
The system or device of device readable medium store on the machine readable media and realize any embodiment in above-described embodiment
The software program code of function, and make the machine of the system read and execute be stored in it is machine readable in the machine readable media
Instruction.In this case, any one of above-described embodiment can be achieved in the program code itself read from machine readable media
The function of embodiment, therefore the machine readable media of machine readable code and storage machine readable code constitutes of the invention one
Part.The embodiment of machine readable media includes floppy disk, hard disk, magneto-optic disk, CD, tape, non-volatile memory card and ROM.
Selectively, can by communication network download program code from server computer or on cloud.
It should be noted that above-mentioned each process and step or module not all in the structure chart of identification device are all must
Must, certain steps or module can be ignored according to the actual needs.Each step execution sequence be not it is fixed, can basis
It needs to be adjusted.System structure described in the various embodiments described above can be physical structure, be also possible to logical construction, that is,
Some modules may be realized by same physical entity, be realized alternatively, some modules may divide by multiple physical entities, alternatively, can
To be realized jointly by certain components in multiple autonomous devices.
Detailed displaying and explanation carried out to the present invention above by attached drawing and preferred embodiment, however the present invention is not limited to
These embodiments having revealed that, base could be aware that with above-mentioned multiple embodiment those skilled in the art, can combine above-mentioned difference
Code audit means in embodiment obtain the more embodiments of the present invention, these embodiments also protection scope of the present invention it
It is interior.
Claims (12)
1. a kind of recognition methods for risk trade, including
Multiple trade orders are chosen, wherein each order represents one of one of a business service quotient and more than second a users more than first
Between transaction；
Primitive network relational graph is established, wherein the primitive network relational graph includes more than first clothes for representing the business service quotient
Business quotient's node and more than the second a user nodes for representing the user, and every between service provider's node and user node
Line represents an order in the multiple trade order；
Twoth k value of the setting for the first k value of service provider's node and for the user node；
Using the first k value and the 2nd k value respectively in the primitive network relational graph service provider's node and the use
Family node iterated application K-core algorithm is to generate first network relational graph；
Identify in the first network relational graph that there are still business corresponding to service provider's node of line and user node
Transaction between service provider and user is risk trade.
2. method as claimed in claim 1, wherein including: to generate first network relational graph using K-core algorithm
The degree of each service provider's node and the degree of each user node are calculated separately,
By deleting its service provider node of the degree less than the first k value and coupled whole from the primitive network relational graph
Line and its angle value generate the first network relationship less than the user node of the 2nd k value and coupled whole lines
Figure.
3. further comprising such as the method for claims 1 or 2:
The first k value and the 2nd k value are adjusted according to the risk trade identified；
The service provider in the first network relational graph is saved respectively using the first k value being adjusted and the 2nd k value
Point is with the user node iterated application K-core algorithm to generate the second cyberrelationship figure；
Identify in the second cyberrelationship figure that there are still business corresponding to service provider's node of line and user node
Transaction between service provider and user is risk trade.
4. further comprising such as the method for claims 1 or 2:
The first k value and the 2nd k value are adjusted according to the risk trade identified；
The service provider in the primitive network relational graph is saved respectively using the first k value being adjusted and the 2nd k value
Point is with the user node iterated application K-core algorithm to generate third relational graph；
Identify in the third cyberrelationship figure that there are still business corresponding to service provider's node of line and user node
Transaction between service provider and user is risk trade.
5. a method as claimed in any preceding claim, wherein the business service quotient is cinema, the user is film booking
Person.
6. a kind of identification device of risk trade, comprising:
Obtain module, be configured to choose multiple trade orders, wherein each order represent one of a business service quotient more than first with
Transaction more than second between one of a user；
Composition module is configured to establish primitive network relational graph, and wherein the primitive network relational graph includes to represent the business clothes
More than first a service provider's nodes of business quotient and more than the second a user nodes for representing the user, and service provider's node and use
Every line between the node of family represents an order in the multiple trade order；
Computing module is configured that twoth k value of the setting for the first k value of service provider's node and for user node；It utilizes
The first k value and the 2nd k value respectively change to service provider's node in the primitive network relational graph with the user node
In generation, is using K-core algorithm to generate first network relational graph；And
Mark module, is configured to identify in the first network relational graph that there are still service provider's node of line and users to save
Transaction between point corresponding business service quotient and user is risk trade.
7. identification device as claimed in claim 6, wherein the computing module is further configured to:
The degree of each service provider's node and the degree of each user node are calculated separately,
By deleting its service provider node of the degree less than the first k value and coupled whole from the primitive network relational graph
Line and its angle value generate the first network relationship less than the user node of the 2nd k value and coupled whole lines
Figure.
8. such as the identification device of claim 6 or 7, wherein the computing module is further configured to: according to the risk identified
Transaction adjusts the k value and the 2nd k value；Using the first k value being adjusted and the 2nd k value respectively to the first network
Service provider's node and the user node iterated application K-core algorithm in relational graph is to generate the second cyberrelationship figure；
Wherein the mark module further identifies service provider's node in the second cyberrelationship figure there are still line
Transaction between business service quotient and user corresponding to user node is risk trade.
9. such as the identification device of claim 6 or 7, wherein the computing module is further configured to: according to the risk identified
Transaction adjusts the first k value and the 2nd k value；Using the first k value being adjusted and the 2nd k value respectively to described original
Service provider's node and the user node iterated application K-core algorithm in cyberrelationship figure is to generate third relational graph；
Wherein the mark module further identifies service provider's node in the third cyberrelationship figure there are still line
Transaction between business service quotient and user corresponding to user node is risk trade.
10., wherein the business service quotient is cinema, the user is film such as the identification device of one of claim 5-9
Ticket-holder.
11. a kind of machine readable media with instruction, described instruction makes described when determining that system executes by one or more
Determine that system executes method described in any one of -5 according to claim 1.
12. a kind of identification device of risk trade, comprising:
Memory is stored thereon with instruction；
Processor, the processor can be configured to execute described instruction to realize according to claim 1 described in any one of -5
Method.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN112907380A (en) * | 2021-03-25 | 2021-06-04 | 中国科学院计算技术研究所 | Liquidity evaluation method for financial market supervision |
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| US20160042355A1 (en) * | 2014-08-06 | 2016-02-11 | Alibaba Group Holding Limited | Method and Apparatus of Identifying a Transaction Risk |
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