CN111177192A

CN111177192A - Method and device for determining group members

Info

Publication number: CN111177192A
Application number: CN201911262922.9A
Authority: CN
Inventors: 齐云飞; 梁秀钦
Original assignee: Beijing Mininglamp Software System Co ltd
Current assignee: Beijing Zhizhi Heshu Technology Co.,Ltd.
Priority date: 2019-12-11
Filing date: 2019-12-11
Publication date: 2020-05-19

Abstract

The application provides a method and a device for determining community members, wherein the method comprises the following steps: acquiring interpersonal relationship data in a preset data set according to a first preset rule; respectively determining a corresponding associated person set of each selected person according to one or more selected persons and the relationship data; determining members of a group according to the selected person and the associated person set corresponding to the selected person; wherein the first predetermined rule is a rule determined according to the relationship characteristics between the community members. The technical scheme determines the members of the community according to the characteristics of the community, and can determine the members of the community more accurately.

Description

Method and device for determining group members

Technical Field

The present invention relates to the field of computers, and in particular, to a method and an apparatus for determining community members.

Background

Compared with the case of single person, the harmfulness of the group project is more serious, the social influence is larger, the anti-reconnaissance means of the group members is more delicate through cooperation, and the significance of the crime group project is great.

In the prior art, the determination of criminal group members is usually based on label propagation and clustering. The disadvantages of this approach are: in the label propagation, in a graph-based network, a structure with dense connections among the interior of a graph (or communities) and sparse connections among different communities is found. The structure mining idea is not matched with the group mining actually, because in a network diagram of a public security system, direct relations often do not exist among group members, indirect relations are possibly few, and common characteristics among the group members have great difference, so that the group members are difficult to be accurately determined in a label propagation and clustering mode.

Disclosure of Invention

The technology to be solved by the application is to provide a method and a device for determining group members, which can determine the group members more accurately.

In order to solve the above technical problem, the present application provides a method for determining community members, the method including:

acquiring interpersonal relationship data in a preset data set according to a first preset rule;

respectively determining a corresponding associated person set of each selected person according to one or more selected persons and the relationship data;

determining members of a group according to the selected person and the associated person set corresponding to the selected person;

wherein the first predetermined rule is a rule determined according to the relationship characteristics between the community members.

Optionally, before determining the associated person set corresponding to each selected person according to one or more selected persons and the relationship data, the method further includes:

determining the selected person according to a second predetermined rule;

wherein the second predetermined rule is a rule determined according to characteristics of members necessarily included in the community.

Optionally, the determining, according to one or more selected persons and the relationship data, the associated person set corresponding to each selected person respectively includes:

step a1, setting the initial value of i as 0;

step a2, using each selected person as a seed person;

step a3, for each seed person, determining a corresponding associated person set of the seed person according to the relationship data;

step a4, i ═ i + 1;

step a5, judging whether i is smaller than a preset iteration number N, if i is smaller than N, taking each person in the associated person set as a seed person, and turning to step a 3; if i is greater than or equal to N, ending the operation of determining the set of the relatives;

wherein N is a positive integer greater than or equal to 1.

Optionally, the determining the members of the community according to the selected people set and all associated people set includes:

merging the one or more selected persons with the determined set of all associated persons;

screening the personnel in the combined set according to a third preset rule, and taking the screened personnel as members of the group;

wherein the third predetermined rule is a rule determined according to an attribute characteristic of the community.

Optionally, the obtaining of the interpersonal relationship data in the predetermined data set according to the first predetermined rule includes:

acquiring interpersonal relationship data in a preset data set through a relationship calculation engine according to the first preset rule;

the determining the associated person set corresponding to the seed person according to the relationship data comprises:

and determining the associator set corresponding to the seed person from the relationship data through a graph propagation algorithm.

The present application further provides an apparatus for determining community members, the apparatus comprising: a memory and a processor;

the memory is used for storing a program for determining community members;

the processor is used for reading and executing the program for determining the community members and executing the following operations:

determining the selected person according to a second predetermined rule;

step a1, setting the initial value of i as 0;

step a2, using each selected person as a seed person;

step a4, i ═ i + 1;

wherein N is a positive integer greater than or equal to 1.

The application includes: acquiring interpersonal relationship data in a preset data set according to a first preset rule; respectively determining a corresponding associated person set of each selected person according to one or more selected persons and the relationship data; determining members of a group according to the selected person and the associated person set corresponding to the selected person; wherein the first predetermined rule is a rule determined according to the relationship characteristics between the community members. The technical scheme determines the members of the community according to the characteristics of the community, and can determine the members of the community more accurately.

Drawings

The accompanying drawings are included to provide an understanding of the present disclosure and are incorporated in and constitute a part of this specification, illustrate embodiments of the disclosure and together with the examples serve to explain the principles of the disclosure and not to limit the disclosure.

FIG. 1 is a flow chart of a method for determining community members according to a first embodiment of the present invention;

fig. 2 is a schematic structural diagram of an apparatus for determining community members according to a first embodiment of the present invention;

FIG. 3 is a flow chart of a method of determining community members according to example one of the present application;

fig. 4 is a schematic diagram of a set of associates in example one of the present application.

Detailed Description

The present application describes embodiments, but the description is illustrative rather than limiting and it will be apparent to those of ordinary skill in the art that many more embodiments and implementations are possible within the scope of the embodiments described herein. Although many possible combinations of features are shown in the drawings and discussed in the detailed description, many other combinations of the disclosed features are possible. Any feature or element of any embodiment may be used in combination with or instead of any other feature or element in any other embodiment, unless expressly limited otherwise.

The present application includes and contemplates combinations of features and elements known to those of ordinary skill in the art. The embodiments, features and elements disclosed in this application may also be combined with any conventional features or elements to form a unique inventive concept as defined by the claims. Any feature or element of any embodiment may also be combined with features or elements from other inventive aspects to form yet another unique inventive aspect, as defined by the claims. Thus, it should be understood that any of the features shown and/or discussed in this application may be implemented alone or in any suitable combination. Accordingly, the embodiments are not limited except as by the appended claims and their equivalents. Furthermore, various modifications and changes may be made within the scope of the appended claims.

Further, in describing representative embodiments, the specification may have presented the method and/or process as a particular sequence of steps. However, to the extent that the method or process does not rely on the particular order of steps set forth herein, the method or process should not be limited to the particular sequence of steps described. Other orders of steps are possible as will be understood by those of ordinary skill in the art. Therefore, the particular order of the steps set forth in the specification should not be construed as limitations on the claims. Further, the claims directed to the method and/or process should not be limited to the performance of their steps in the order written, and one skilled in the art can readily appreciate that the sequences may be varied and still remain within the spirit and scope of the embodiments of the present application.

Example one

As shown in fig. 1, the present embodiment provides a method for determining community members, the method including:

s101, acquiring interpersonal relationship data in a preset data set according to a first preset rule;

s102, respectively determining a related person set corresponding to each selected person according to one or more selected persons and the relationship data;

step S103, determining members of a group according to the selected persons and the associated person sets corresponding to the selected persons;

determining the selected person according to a second predetermined rule;

step a1, setting the initial value of i as 0;

step a2, using each selected person as a seed person;

step a4, i ═ i + 1;

wherein N is a positive integer greater than or equal to 1.

according to the first preset rule, acquiring interpersonal relation data in a preset data set through a relation calculation engine;

The technical scheme determines the members of the community according to the characteristics of the community, and can determine the members of the community more accurately.

As shown in fig. 2, the present embodiment further provides an apparatus for determining a community member, where the apparatus includes: a memory 10 and a processor 11;

the memory 10 for storing a program for determining the community members;

the processor 11 is configured to read and execute the program for determining community members, and perform the following operations:

determining the selected person according to a second predetermined rule;

step a1, setting the initial value of i as 0;

step a2, using each selected person as a seed person;

step a4, i ═ i + 1;

step a5, judging whether i is smaller than a preset iteration number N, if i is smaller than N, taking each person in the associated person set as a seed person, and turning to step a 3; if i is greater than or equal to N, the operation of determining the set of relations is ended.

Wherein N is a positive integer greater than or equal to 1;

Example 1

The method for determining community members of the present embodiment is further described below by specific examples.

In this example, taking the group as "group relating to poison and wealth", as shown in fig. 3, the method for determining the members of "group relating to poison and wealth" may include the following steps:

s301, determining a selected person according to a second preset rule;

in this example, the second predetermined rule is a rule determined based on the characteristics of the members necessarily included in the "group relating to poison and wealth".

The selected persons are typically the most important and stable character persons in the community, and the selected persons may be one or more. For example, a "drug-and-money-related party" is a party with a cross between a financial crime and a drug-related crime, and the party is characterized by "using a thief to nourish the thief", so that a person with a drug-taking and/or drug-dropping forepart can be selected as a selected person.

For example, the second predetermined rule for "concerning a poison and a financial asset" may include:

1) a local person;

2) a drug withdrawal background (e.g., those prior to drug withdrawal and/or those within 1 year of the drug withdrawal).

3) The age is between 20 and 40 years.

Step S302, acquiring interpersonal relationship data in a preset data set according to a first preset rule;

in this example, the first predetermined rule is a rule determined according to a relationship characteristic between the members of the "group relating to poison and wealth".

For example, a "group relating to poison and wealth" is generally a local proposal, so the result from local data is important for the group. The predetermined data set may thus comprise local trip data, local stay data, local relative data. Then, the relationship data of people and person can be obtained through the calculation engine, and the relationship data can comprise ' hotel in the same place ', ' family in the same place ', ' countryside ', the same night, room opening in the same night ', the ' passing through the same gate ', and the like.

Step S303, respectively determining a related person set corresponding to each selected person according to one or more selected persons and the relationship data;

as shown in fig. 4, assume that the selected persons include: zhang III, Li IV and Liu Tian; the preset iteration number N is 3;

the initial value of i is 0;

each selected person is taken as a seed person, and the Pregel algorithm can then be used to complete the computational task. By means of a Spark calculation engine (a fast general calculation engine designed for large-scale data processing and integrated with a Pregel algorithm), a set of relatives who have 'same entrance in a hotel', 'same night for a room', 'same family' or 'same country' with each seed person is expanded from the seeds. The set of associates may include names and identification information of the associates. The following description is given only by name.

Suppose that the set of associated people a1 corresponding to zhang san and zhang hu and liuming is { zhan and zhang hu and liuming };

the associated person set a2 corresponding to lie four is { lie and lie strong };

the related people set A3 corresponding to Liu Tian is { Liu Hu }.

And (5) adding 1 to the value of i to obtain 1, and if the preset iteration number N is 1, finishing the calculation of the associated person set. Since N is 3 in this example, and 1 is less than 3, the set of correspondents needs to be computed continuously.

Next, taking each person in a1 as a seed person, a corresponding associated person set is calculated for each seed person.

Suppose, zhan bin, a corresponding set of associated persons a11 is { zhang one, zhang two };

the related person set A12 corresponding to Zhanghu is { Zhanghu I, Zhanghu II };

the related person set A13 corresponding to Liuming is { Liuyi }.

By analogy, calculating to obtain associated person sets A21 and A22 corresponding to each person in A2;

and each person in A3 corresponds to the associated person set A31.

And (3) adding 1 to the value of i to obtain 2, and if the preset iteration number N is 2, finishing the calculation of the associated person set. Since N is 3 in this example, 2 is less than 3, the set of correspondents needs to be computed continuously.

Next, taking each person in a11 as a seed person, a corresponding associated person set is calculated for each seed person.

Suppose that Zhang one corresponding associated person set A111 is { Wang one, Wang two };

the associated person set A112 corresponding to Zhang two is { Zhao one };

by analogy, calculating to obtain associated person sets a121 and a122 corresponding to each person in a 12;

a set of associated persons a131 corresponding to each person in a 13; .

Associated person set A211 corresponding to each person in A21

The associated person sets A221 and A222 corresponding to each person in A22;

and each person in A31 corresponds to the associated person sets A311 and A312.

And adding 1 to the value of i to obtain 3, wherein N is 3 in this example, and 3 is equal to 3, so that the calculation of the associated person set can be finished. As can be seen from fig. 4, if the number of people in the related person set is 2, and it is further necessary to continue to calculate the next related person set, the number of the corresponding next related person set is 3. Then, by analogy, if the number of people in the related person set is 10, and it is further necessary to continue to calculate the next related person set, the number of the corresponding next related person set is 10.

In this example, the selected person is taken as the layer 1, the associated person set corresponding to the selected person is taken as the layer 2, the associated person set corresponding to each person in the associated person set of the second layer is taken as the layer 3, and so on. When the number of iterations is set to 3 and the initial value of i is set to 0, all the associated person sets included in the 4 th layer need to be calculated. It should be noted that the initial value of i and the value of the iteration number N may be adjusted accordingly, for example, assuming that iteration is required 3 times, that is, all the associated person sets included in the 4 th layer need to be calculated, the initial value of i may also be set to 1, and the corresponding value of N is set to 4.

Step S304, combining the one or more selected persons with all the determined associated person sets;

based on the above-described calculation results assumed in fig. 4, the persons in the set after merging are all the persons in fig. 4.

S305, screening the personnel in the combined set according to a third preset rule, and taking the screened personnel as members of the group;

in this example, the third predetermined rule is a rule determined according to an attribute feature of "group relating to poison and wealth", and the attribute feature may include a business rule of "group relating to poison and wealth".

For example, a third rule for "poison-and-wealth-related group" may include:

1) the group members must have fraud, robbery and theft predecessors;

2) the number of groups must be more than 2 (inclusive);

3) the number of the predecessors in the group accounts for at least half of the total number of the group;

4) the average age of the group is about 30-35.

The members in the set obtained in step S304 are relatively rough results, because there is very large noise data, and members that really meet the requirements of "money groups involved in poison and invasion" cannot be accurately obtained. It is desirable to screen out as much of these noisy data as possible by this step. The filtering rule (i.e., the third predetermined rule) of the noise data may be determined according to the business rules of the community.

It will be understood by those of ordinary skill in the art that all or some of the steps of the methods, systems, functional modules/units in the devices disclosed above may be implemented as software, firmware, hardware, and suitable combinations thereof. In a hardware implementation, the division between functional modules/units mentioned in the above description does not necessarily correspond to the division of physical components; for example, one physical component may have multiple functions, or one function or step may be performed by several physical components in cooperation. Some or all of the components may be implemented as software executed by a processor, such as a digital signal processor or microprocessor, or as hardware, or as an integrated circuit, such as an application specific integrated circuit. Such software may be distributed on computer readable media, which may include computer storage media (or non-transitory media) and communication media (or transitory media). The term computer storage media includes volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data, as is well known to those of ordinary skill in the art. Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, Digital Versatile Disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired information and which can accessed by a computer. In addition, communication media typically embodies computer readable instructions, data structures, program modules or other data in a modulated data signal such as a carrier wave or other transport mechanism and includes any information delivery media as known to those skilled in the art.

Claims

1. A method for determining community members, comprising:

2. The method of claim 1, wherein prior to determining the set of associated people corresponding to each of the selected people based on the one or more selected people and the relationship data, the method further comprises:

determining the selected person according to a second predetermined rule;

3. The method of claim 2, wherein the determining the set of associated persons corresponding to each of the selected persons respectively according to one or more selected persons and the relationship data comprises:

step a1, setting the initial value of i as 0;

step a2, using each selected person as a seed person;

step a4, i ═ i + 1;

wherein N is a positive integer greater than or equal to 1.

4. The method of claim 3, wherein determining the members of the community from the selected set of people and the set of all associated people comprises:

5. The method of claim 4, wherein obtaining interpersonal relationship data in a predetermined data set according to a first predetermined rule comprises:

6. An apparatus for determining community members, the apparatus comprising: a memory and a processor; the method is characterized in that:

the memory is used for storing a program for determining community members;

7. The apparatus of claim 6, wherein before determining the set of associated persons corresponding to each of the selected persons according to the one or more selected persons and the relationship data, the method further comprises:

determining the selected person according to a second predetermined rule;

8. The apparatus of claim 7, wherein the determining the set of associated persons corresponding to each of the selected persons respectively according to the one or more selected persons and the relationship data comprises:

step a1, setting the initial value of i as 0;

step a2, using each selected person as a seed person;

step a4, i ═ i + 1;

wherein N is a positive integer greater than or equal to 1.

9. The apparatus of claim 8, wherein said determining the members of the community based on the selected set of people and the set of all associated people comprises:

10. The method of claim 9, wherein obtaining interpersonal relationship data in a predetermined data set according to a first predetermined rule comprises: