CN114637912B - Friend recommendation method and device based on high-coverage community discovery in location social network - Google Patents
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Abstract
The invention discloses a friend recommendation method and device based on high-coverage community discovery in a location social network. The implementation steps are as follows: acquiring a connected k-truss containing a query user; solving all geographical area circle sets meeting the conditions based on the communicated k-truss; calculating a connected k-truss containing the inquiring user in each geographical area circle; directly adding the connected k-trusses into the result set or replacing the minimum unique set with the connected k-trusses according to the community quantity in the current result; the final result set is returned as a friend recommendation community for the querying user. The invention aims to find a given number of friend recommendation communities with high coverage so that the total number of users contained in the communities is the largest and the query users are contained. Therefore, the friend recommendation method and device based on high coverage community discovery in the location social network have great benefits for personalized recommendation.
Description
Technical Field
The invention belongs to the technical fields of computer data mining and Internet, and particularly relates to a friend recommendation method and device based on high-coverage community discovery in a position social network.
Background
With the widespread popularity of wireless communication technology and GPS-equipped mobile devices (e.g., smartphones and tablet computers), people now have easy access to the internet. This has prompted the advent of location social networks, such as Twitter and Foursquare, where social networks are combined with the location information of users, each of which has not only social relationships with other users, but also their corresponding two-dimensional spatial coordinates. Therefore, searching subgraphs with special properties in a location social network has become a subject of intense research in recent years. However, existing studies on location-based social networks are driven by size constraints or other defined constraints, so that the resulting community subgraphs are typically highly overlapping, i.e., high coverage attributes are ignored, which greatly reduces the usefulness of the information contained in the results.
Disclosure of Invention
The invention aims to provide a friend recommendation method and device based on high coverage community discovery in a location social network, aiming at the defects of the prior art.
The technical scheme for solving the technical problems is as follows:
according to a first aspect of the present invention, there is provided a friend recommendation method based on high coverage community discovery in a location social network, the method comprising the steps of:
S1: giving a real position social network, namely G (V, E), wherein V represents all nodes in the position social network G, namely all users, and E represents edges between all users in the position social network G, namely friend relations among the users; any user u in the location social network G is associated with a location information loc (u) = (x u,yu),xu,yu represents the location of the user u in the two-dimensional space;
S2: for query user q, finding a given number of friend recommendation communities with high coverage and meeting distance constraints in the location social network G; the method comprises the following specific steps:
S2.1: initializing a candidate community set D as an empty set, and initializing a geographic area circle set C d as an empty set; solving a connected k-truss containing the query user q in the position social network G as a candidate user set T k;
S2.2: for any two different users u and v in the candidate user set T k, the following operations are performed: if the distance between u and v is not greater than a given distance constraint d, then find a diameter d, and all the geographic area circles for both users u and v are included on the edge, store these circles into a geographic area circle set C d;
S2.3: for each geographic area circle O in the set of geographic area circles C d, the following is performed: computing a connected k-truss containing the querying user q and within the geographic area circle O
S2.4: for the connected k-truss obtained in the step S2.3Maintain operations are performed, maintain operations steps are as follows:
S2.4.1: if the number of connected k-trusses in the current candidate community set D is less than the given community number limit r, the current obtained connected k-trusses Directly putting the candidate community set D, otherwise, aiming at the currently obtained connected k-trussSteps S2.4.2 to S2.4.4 are performed;
S2.4.2: solving a unique user set of each connected k-truss in the candidate community set D, namely all user sets only contained in the connected k-truss and not contained in any other connected k-truss in the candidate community set D; the connected k-truss of the unique user set containing the minimum number of users in the candidate community set D is marked as T min;
s2.4.3: using the currently available connected k-trusses And the candidate community set D calculates a marking set X; the specific method of the marker set X is that for/>Is performed by the following operations: if the candidate community set D does not contain the user v, adding the user v into the tag set X; if the candidate community set D contains a user v, and the user v belongs to the connected k-truss T min with the smallest unique user set, adding the user v into the mark set X;
S2.4.4: comparing the number of users in the tag set X with the number of users of the connected k-truss T min having the smallest unique user set, if the inequality is met Then use the currently available connected k-truss/>Replacing the connected k-trusses T min with the smallest unique user set in the candidate community set D, wherein |U (T min, D) | is the number of users in the unique user set of T min, |cov (D) | is the number of all users contained in the candidate community set D, and|D| is the number of connected k-trusses in the candidate community set D;
s3: the candidate community set D is returned as a set of friend communities recommended to the querying user q.
Further, the communicating k-truss in S2.1 is specifically: 1) Any one of the friendships is contained in at least (k-2) triangles; 2) Is extremely large, i.e. any hypergraph thereof is not a k truss.
According to a second aspect of the present invention, there is provided a friend recommendation device based on high coverage community discovery in a location social network, the device comprising a memory and one or more processors, the memory having executable code stored therein, the processors implementing the friend recommendation method based on high coverage community discovery described above when executing the executable code.
According to a third aspect of the present invention, there is provided a computer-readable storage medium having stored thereon a program which, when executed by a processor, implements the friend recommendation method based on high coverage community discovery described above.
The beneficial effects of the invention are as follows: the invention aims at inquiring users to find a given number of friend recommendation communities which have high coverage and meet distance constraint. The friend recommendation community obtained by the method has strong robustness, and can provide personified personalized recommendation for users. The method can efficiently reduce the search space in consideration of space constraint and high coverage constraint. Therefore, the friend recommending method and device based on high-coverage community discovery in the position social network provided by the invention have great benefits for personalized recommendation of users.
Drawings
FIG. 1 is a flowchart of a friend recommendation method based on high coverage community discovery in a location social network provided by an example embodiment;
FIG. 2 is an original location-based social network diagram provided by an exemplary embodiment;
3-6 are schematic diagrams of operations performed on an original location-based social network diagram provided by an exemplary embodiment;
FIG. 7 is a schematic diagram of a location-based social network in the filtered real world, as provided by an exemplary embodiment.
FIG. 8 is a block diagram of a friend recommender based on high coverage community discovery in a location social network, as provided by an exemplary embodiment.
Detailed Description
In order that the above objects, features and advantages of the invention will be readily understood, a more particular description of the invention will be rendered by reference to the appended drawings.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, but the present invention may be practiced in other ways other than those described herein, and persons skilled in the art will readily appreciate that the present invention is not limited to the specific embodiments disclosed below.
The embodiment of the invention provides a friend recommending method based on high coverage community discovery in a location social network, which is shown in fig. 1, and comprises the following specific implementation flow:
S1: giving a real position social network, namely G (V, E), wherein V represents all nodes in the position social network G, namely all users, and E represents edges between all users in the position social network G, namely friend relations among the users; any user u in the location social network G is associated with a location information loc (u) = (x u,yu),xu,yu represents the location of the user u in two dimensions, in this embodiment, social network golella is used.
S2: for the querying user q, finding a given number r of friend recommendation communities which have high coverage and meet the distance constraint in the location social network G; the method comprises the following specific steps:
S2.1: initializing a candidate community set D as an empty set, and initializing a geographic area circle set C d as an empty set; solving a connected k-truss containing the query user q in the position social network G as a candidate user set T k;
The communicating k-truss is specifically: 1) Any one of the friendships is contained in at least (k-2) triangles; 2) Is extremely large, i.e. any hypergraph thereof is not a k truss;
S2.2: for any two different users u and v in the candidate user set T k, the following operations are performed: if the distance between u and v is not greater than a given distance constraint d, then find a diameter d, and all the geographic area circles for both users u and v are included on the edge, store these circles into a geographic area circle set C d;
S2.3: for each geographic area circle O in the set of geographic area circles C d, the following is performed: computing a connected k-truss containing the querying user q and within the geographic area circle O
S2.4: for the connected k-truss obtained in the step S2.3Maintain operations are performed, maintain operations steps are as follows:
S2.4.1: if the number of connected k-trusses in the current candidate community set D is less than the given community number limit r, the current obtained connected k-trusses Directly putting the candidate community set D, otherwise, aiming at the currently obtained connected k-trussSteps S2.4.2 to S2.4.4 are performed;
S2.4.2: solving a unique user set of each connected k-truss in the candidate community set D, namely all user sets only contained in the connected k-truss and not contained in any other connected k-truss in the candidate community set D; the connected k-truss of the unique user set containing the minimum number of users in the candidate community set D is marked as T min;
s2.4.3: using the currently available connected k-trusses And the candidate community set D calculates a marking set X; the specific method of the marker set X is that for/>Is performed by the following operations: if the candidate community set D does not contain the user v, adding the user v into the tag set X; if the candidate community set D contains a user v, and the user v belongs to the connected k-truss T min with the smallest unique user set, adding the user v into the mark set X;
S2.4.4: comparing the number of users in the tag set X with the number of users of the connected k-truss T min having the smallest unique user set, if the inequality is met Then use the currently available connected k-truss/>Replacing the connected k-truss T min with the smallest unique user set in the candidate community set D, wherein |U (T min, D) | is the number of users in the unique user set of T min, |cov (D) | is the number of all users contained in the candidate community set D, and|D| is the number of connected k-trusses in the candidate community set D.
S3: after the above steps are finished, the candidate community set D is returned to serve as a friend community set recommended to the query user q.
The effect achieved by the invention will be described below by way of example with respect to the location-based social network diagram of fig. 2, in which each user represents a vertex, and if there is a social relationship between users, there are edges connected between users, and in which each edge is contained in at least k-2 triangles within a k-truss of space. Let r=2, d be the empty set. Assuming that the querying user is Bill, when k=4, fig. 3 is a sub-graph processed in step S2.1. The space 4 truss is the user set covered by the shaded portion in fig. 3, namely the connected 4-truss containing the query users Bill. According to step S2.3, obtaining a first 4-truss (community subgraph in the horizontal line circle of FIG. 4); according to step S2.4, when D is an empty set, the 4-truss is stored directly into D. And so on, a second 4-truss (community subgraph in gray background of fig. 5) is obtained, where |d| < r=2, and this 4-truss is stored directly into D. When the last 4-truss is processed, when |d|=r=2, then the unique user set of 2 4-trusses in D is found, and the horizontal line subgraph: 2 unique users, gray subgraph: 4 unique users, then T min is a 4-truss within a cross-line circle. According to step S2.4, the X sets (community subgraphs within the vertical circles of FIG. 6) of the third 4-truss are obtained, and the number of X sets is 4, so that the inequality in step S2.4.4 is satisfied. Thus, T min in D is replaced with a 4-truss in the vertical circle, i.e., a 4-truss in the horizontal circle. Finally, the result D is returned as a friend recommendation community (community subgraph in gray background of FIG. 7) for the query user q.
Given a community quantity limit r, a truss constraint value k and a query user q, the invention aims to provide a friend recommendation method based on high coverage community discovery in a location social network, wherein the required high coverage community needs to meet three conditions: 1. each user relationship is at least contained in k-2 triangles; 2. the obtained community with high coverage contains the largest total number of users; 3. including querying user q. Searching for highly-covered communities has many applications on location social networks, such as personalized event recommendations in Facebook, twitter and Google +, among other applications. The method may recommend activities to a user based on the personal location and social relationships of a given user. For example, a company may want to recommend a number of high coverage communities to a given querying user q by gathering a group of people that are not far apart (bounded by a geographical area circle having a diameter no greater than a given value) and each having many friends in the group (each friend being included in no less than k-2 triangles), if a certain user is set as the querying user q, and the number of high coverage communities is the greatest. This means that the community recommended to querying user q not only meets the geographical, social relationship constraints, but also provides the most friends for q to choose from. Thus, friend recommendation methods based on high coverage community discovery in a location social network have great benefit for personalized recommendations.
Furthermore, the present invention conducted extensive experiments on two real world spatial datasets Brightkite and Gowalla to evaluate the effectiveness and efficiency of the proposed method. To evaluate the performance of the proposed method we performed experiments by varying the parameter k and the distance threshold d. The invention uses the algorithm to consume time to measure the effectiveness and efficiency of the proposed method. All procedures were carried out in a standard c++, and all experiments were performed on a PC equipped with Intel i5-9600KF CPU and 32GB main memory. Experiments show that the method of the invention is faster than the basic baseline algorithm by nearly 2 orders of magnitude.
Corresponding to the embodiment of the friend recommending method based on the high-coverage community discovery in the position social network, the invention further provides an embodiment of a friend recommending device based on the high-coverage community discovery in the position social network.
Referring to fig. 8, the friend recommending device based on high coverage community discovery in the location social network provided by the embodiment of the invention includes a memory and one or more processors, wherein executable codes are stored in the memory, and when the executable codes are executed by the processors, the processor is used for implementing the friend recommending method based on high coverage community discovery in the location social network in the embodiment.
The friend recommending apparatus based on high coverage community discovery in the location social network of the present invention can be applied to any device with data processing capability, which can be a device or apparatus such as a computer. The apparatus embodiments may be implemented by software, or may be implemented by hardware or a combination of hardware and software. Taking software implementation as an example, the device in a logic sense is formed by reading corresponding computer program instructions in a nonvolatile memory into a memory by a processor of any device with data processing capability. In terms of hardware, as shown in fig. 8, a hardware structure diagram of an apparatus with data processing capability, where a friend recommending apparatus based on high coverage community discovery is located, in a location social network according to the present invention is shown, except for a processor, a memory, a network interface, and a nonvolatile memory shown in fig. 8, where an apparatus with data processing capability in an embodiment is located, generally, according to an actual function of the apparatus with data processing capability, other hardware may be further included, which is not described herein.
The implementation process of the functions and roles of each unit in the above device is specifically shown in the implementation process of the corresponding steps in the above method, and will not be described herein again.
For the device embodiments, reference is made to the description of the method embodiments for the relevant points, since they essentially correspond to the method embodiments. The apparatus embodiments described above are merely illustrative, wherein the elements illustrated as separate elements may or may not be physically separate, and the elements shown as elements may or may not be physical elements, may be located in one place, or may be distributed over a plurality of network elements. Some or all of the modules may be selected according to actual needs to achieve the purposes of the present invention. Those of ordinary skill in the art will understand and implement the present invention without undue burden.
The embodiment of the invention also provides a computer readable storage medium, wherein a program is stored on the computer readable storage medium, and when the program is executed by a processor, the friend recommendation method based on high coverage community discovery in the position social network in the embodiment is realized.
The computer readable storage medium may be an internal storage unit, such as a hard disk or a memory, of any of the data processing enabled devices described in any of the previous embodiments. The computer readable storage medium may also be an external storage device of any device having data processing capabilities, such as a plug-in hard disk, a smart memory card (SMART MEDIA CARD, SMC), an SD card, a flash memory card (FLASH CARD), etc. provided on the device. Further, the computer readable storage medium may include both internal storage units and external storage devices of any data processing device. The computer readable storage medium is used for storing the computer program and other programs and data required by the arbitrary data processing apparatus, and may also be used for temporarily storing data that has been output or is to be output.
The foregoing description of the preferred embodiment(s) is (are) merely intended to illustrate the embodiment(s) of the present invention, and it is not intended to limit the embodiment(s) of the present invention to the particular embodiment(s) described.
Claims (4)
1. A friend recommendation method based on high coverage community discovery in a location social network, the method comprising the steps of:
S1: giving a real position social network, namely G (V, E), wherein V represents all nodes in the position social network G, namely all users, and E represents edges between all users in the position social network G, namely friend relations among the users; any user u in the location social network G is associated with a location information loc (u) = (x u,yu),xu,yu represents the location of the user u in the two-dimensional space;
S2: for query user q, finding a given number of friend recommendation communities with high coverage and meeting distance constraints in the location social network G; the method comprises the following specific steps:
S2.1: initializing a candidate community set D as an empty set, and initializing a geographic area circle set C d as an empty set; solving a connected k-truss containing the query user q in the position social network G as a candidate user set T k;
S2.2: for any two different users u and v in the candidate user set T k, the following operations are performed: if the distance between u and v is not greater than a given distance constraint d, then find a diameter d, and all the geographic area circles for both users u and v are included on the edge, store these circles into a geographic area circle set C d;
S2.3: for each geographic area circle O in the set of geographic area circles C d, the following is performed: computing a connected k-truss containing the querying user q and within the geographic area circle O
S2.4: for the connected k-truss obtained in the step S2.3Maintain operations are performed, maintain operations steps are as follows:
S2.4.1: if the number of connected k-trusses in the current candidate community set D is less than the given community number limit r, the current obtained connected k-trusses Directly putting into a candidate community set D, otherwise, aiming at the currently obtained connected k-truss/>Steps S2.4.2 to S2.4.4 are performed;
S2.4.2: solving a unique user set of each connected k-truss in the candidate community set D, namely all user sets only contained in the connected k-truss and not contained in any other connected k-truss in the candidate community set D; the connected k-truss of the unique user set containing the minimum number of users in the candidate community set D is marked as T min;
s2.4.3: using the currently available connected k-trusses And the candidate community set D calculates a marking set X; the specific method of the marker set X is that for/>Is performed by the following operations: if the candidate community set D does not contain the user v, adding the user v into the tag set X; if the candidate community set D contains a user v, and the user v belongs to the connected k-truss T min with the smallest unique user set, adding the user v into the mark set X;
S2.4.4: comparing the number of users in the tag set X with the number of users of the connected k-truss T min having the smallest unique user set, if the inequality is met Then use the currently available connected k-truss/>Replacing the connected k-trusses T min with the smallest unique user set in the candidate community set D, wherein |U (T min, D) | is the number of users in the unique user set of T min, |cov (D) | is the number of all users contained in the candidate community set D, and|D| is the number of connected k-trusses in the candidate community set D;
s3: the candidate community set D is returned as a set of friend communities recommended to the querying user q.
2. The method according to claim 1, wherein the communicating k-truss in S2.1 is specifically: any one of the friendships is contained in at least (k-2) triangles; is extremely large, i.e. any hypergraph thereof is not a k truss.
3. Friend recommendation device based on high coverage community discovery in a social network of locations, comprising a memory and one or more processors, the memory having executable code stored therein, characterized in that the processor, when executing the executable code, implements the method according to any of claims 1, 2.
4. A computer readable storage medium having stored thereon a program, which when executed by a processor, implements the method according to any of claims 1, 2.
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| CN115622973B (en) * | 2022-09-29 | 2024-05-10 | 中国人民解放军战略支援部队信息工程大学 | Stranger social instant messaging application user positioning method and device |
| CN116185379B (en) * | 2022-11-17 | 2023-09-22 | 北京东方通科技股份有限公司 | Method for optimizing code hosting platform |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106056455A (en) * | 2016-06-02 | 2016-10-26 | 南京邮电大学 | Group and place recommendation method based on location and social relationship |
| CN106971345A (en) * | 2016-01-08 | 2017-07-21 | 车海莺 | A kind of location recommendation method based on position social networks |
| KR20180067424A (en) * | 2016-12-09 | 2018-06-20 | 성결대학교 산학협력단 | Location-based subcommunity recommendation system with Privacy enhanced function and method thereof |
| CN112948711A (en) * | 2021-03-24 | 2021-06-11 | 深圳人工智能与数据科学研究院(龙华) | Social network friend recommendation system based on geographical position sharing track |
| CN113254797A (en) * | 2021-04-19 | 2021-08-13 | 江汉大学 | Searching method, device and processing equipment for social network community |
| CN113409160A (en) * | 2021-07-14 | 2021-09-17 | 浙江工商大学 | Method for establishing key relationship in social network based on k truss |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US9602606B2 (en) * | 2012-11-30 | 2017-03-21 | Rovi Guides, Inc. | Systems and methods for a social facilitator service |
-
2022
- 2022-03-08 CN CN202210220021.9A patent/CN114637912B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106971345A (en) * | 2016-01-08 | 2017-07-21 | 车海莺 | A kind of location recommendation method based on position social networks |
| CN106056455A (en) * | 2016-06-02 | 2016-10-26 | 南京邮电大学 | Group and place recommendation method based on location and social relationship |
| KR20180067424A (en) * | 2016-12-09 | 2018-06-20 | 성결대학교 산학협력단 | Location-based subcommunity recommendation system with Privacy enhanced function and method thereof |
| CN112948711A (en) * | 2021-03-24 | 2021-06-11 | 深圳人工智能与数据科学研究院(龙华) | Social network friend recommendation system based on geographical position sharing track |
| CN113254797A (en) * | 2021-04-19 | 2021-08-13 | 江汉大学 | Searching method, device and processing equipment for social network community |
| CN113409160A (en) * | 2021-07-14 | 2021-09-17 | 浙江工商大学 | Method for establishing key relationship in social network based on k truss |
Non-Patent Citations (4)
| Title |
|---|
| Jun Zhao ; Renjie Sun, Qiuyu Zhu, Xiaoyang Wang, Chen Chen.Community Identification in Signed Networks: A K-Truss Based Model.《CIKM '20: Proceedings of the 29th ACM International Conference on Information & Knowledge Management》.2020,全文. * |
| 一种大规模双网络中k-连通Truss子图发现算法;李源;盛飞;孙晶;赵宇海;王国仁;;计算机学报;20200915(第09期);全文 * |
| 基于位置的社交网络潜在好友推荐系统研究;李丹霞;;计算机产品与流通;20200519(第06期);全文 * |
| 基于社区划分和用户相似度的好友信息服务推荐研究;张继东;蔡雪;;情报理论与实践;20190109(第04期);全文 * |
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