CN113486258A - Data analysis method, device, medium and electronic equipment based on social network - Google Patents
Data analysis method, device, medium and electronic equipment based on social network Download PDFInfo
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Abstract
The application provides a data analysis method, a device and a storage medium based on a social network, wherein the method comprises a user relationship data structure, the user relationship data structure comprises connection relationships among user nodes, and the method comprises the following steps: respectively counting the number of first-degree friends of the user node in the user relationship data structure based on user operation behaviors; and setting key user nodes according to the comparison result of the friend number of the user nodes and the friend threshold value.
Description
Technical Field
The application relates to a data analysis method, a data analysis device, a storage medium and electronic equipment, and also relates to a data analysis method, a data analysis device, a storage medium and electronic equipment based on a social network.
Background
With the development of internet technology and the maturation of application software, social networking is becoming more and more important in the business field. In particular, from the perspective of the business model, previous sales of goods have stayed to provide users with convenient services through an online shopping platform, so that users can shop quickly and conveniently. However, with the development of social networks, on one hand, online shopping platforms passively wait for users to download the online shopping platforms, and then shop through the online shopping platforms; on the other hand, based on the user operation data of the social network, the online shopping platform is actively pushed to the specific user, so that the specific user can shop through the online shopping platform. Similarly, based on the user operation data of the social network, business information such as product information, activity information and the like can be pushed to a specific user, so that the corresponding business purpose is achieved.
However, the current push service lacks pertinence, and it is difficult to improve the range of push coverage.
Disclosure of Invention
The invention aims to provide a data analysis method, a data analysis device and a data analysis storage medium based on a social network, and aims to solve the problems that push service in the prior art is lack of pertinence and the push coverage range is difficult to improve.
In order to achieve the object of the present application, a data analysis method based on a social network is provided, including a user relationship data structure, where the user relationship data structure includes connection relationships between user nodes, including: respectively counting the number of first-degree friends of the user node in the user relationship data structure based on user operation behaviors; and setting key user nodes according to the comparison result of the friend number of the user nodes and the friend threshold value.
According to an embodiment of the application, the user operation behavior includes: sharing behavior in social friend circles, and/or forwarding behavior in group chatting.
According to an embodiment of the application, when the user operation behavior is a sharing behavior, the first-degree friend includes: the friend who browses the shared content, the friend who shares the shared content again and/or the friend who forwards the shared content; when the user operation behavior is a forwarding behavior, the first-degree friend includes: the friend who browses the forwarded content, the friend who shares the forwarded content again and/or the friend who forwards the shared content.
According to an embodiment of the present application, the step of setting a key user node according to the comparison result between the friend number of the user node and the friend threshold includes: arranging the user nodes according to the number of the first-degree friends; setting ranking for the user nodes according to the sequence from big to small; and setting the user nodes with the ranking less than or equal to the ranking threshold value as key user nodes.
According to an embodiment of the application, the ranking threshold is any natural number between 5 and 100.
According to an embodiment of the application, the ranking threshold is any natural number between 1% and 10%.
According to an embodiment of the present application, the step of setting a key user node according to the comparison result between the friend number of the user node and the friend threshold includes: and setting the user nodes with the friend number larger than or equal to the number threshold value as key user nodes.
According to an embodiment of the present application, the number threshold is any natural number greater than or equal to 10000.
The method according to an embodiment of the application further comprises: and respectively counting the number of second-degree friends of the user node.
According to an embodiment of the application, the number of friends of the user node is the sum of the number of friends of the first degree and the number of friends of the second degree.
According to the method of an embodiment of the present application, after the first degree friend number of the user node is respectively counted, the method further includes: and respectively counting the number of third-degree friends of the user node, wherein the third-degree friends are direct friends of second-degree friends, and the second-degree friends are direct friends of the first-degree friends.
According to an embodiment of the application, the number of friends of the user node is the sum of the number of friends of the first degree and the number of friends of the third degree.
To achieve the object of the present application, there is provided a data analysis apparatus based on a social network, including a user relationship data structure, wherein the user relationship data structure includes connection relationships between user nodes, including: the counting module is used for respectively counting the number of first-degree friends of the user node in the user relationship data structure based on user operation behaviors; and the setting module is used for setting the key user nodes according to the comparison result of the friend number of the user nodes and the friend threshold value.
According to an embodiment of the application, the user operation behavior includes: sharing behavior in social friend circles, and/or forwarding behavior in group chatting.
According to an embodiment of the application, when the user operation behavior is a sharing behavior, the first-degree friend includes: the friend who browses the shared content, the friend who shares the shared content again and/or the friend who forwards the shared content; when the user operation behavior is a forwarding behavior, the first-degree friend includes: the friend who browses the forwarded content, the friend who shares the forwarded content again and/or the friend who forwards the shared content.
According to an embodiment of the application, the setting module is further configured to arrange the user nodes according to the number of the first-degree friends; the ranking is set for the user nodes according to the sequence from big to small; and the user nodes with the ranking less than or equal to the ranking threshold value are set as key user nodes.
According to an embodiment of the application, the ranking threshold is any natural number between 5 and 100.
According to an embodiment of the application, the ranking threshold is any natural number between 1% and 10%.
According to an embodiment of the application, the setting module is further configured to set the user node with the friend number greater than or equal to the number threshold as a key user node.
According to an embodiment of the present application, the number threshold is any natural number greater than or equal to 10000.
According to an embodiment of the application, the counting module is further configured to count the number of second-degree friends of the user node respectively.
According to an embodiment of the application, the number of friends of the user node is the sum of the number of friends of the first degree and the number of friends of the second degree.
To achieve the object of the present application, a computer-readable storage medium is provided for storing computer program instructions, wherein the computer program instructions, when executed by a processor, implement a social network based data analysis method according to an embodiment of the present application.
To achieve the object of the present application, an electronic device is provided, which includes a memory and a processor, and is characterized in that the memory is configured to store one or more computer program instructions, wherein the one or more computer program instructions are executed by the processor to implement a social network based data analysis method according to an embodiment of the present application.
According to the data analysis method based on the social network, the number of the friends with at least one degree is counted in the user relationship data structure, and the number of the friends is compared with the number threshold value to determine the key user nodes, so that when the service is pushed in the social network based on the user relationship data structure, the push range can be effectively improved and the push cost can be effectively reduced by pushing the key user nodes in a targeted manner on the premise of reducing the push times as much as possible.
According to the data analysis device based on the social network, the number of the friends with at least one degree is counted in the user relationship data structure, and the number of the friends is compared with the number threshold value to determine the key user nodes, so that when the service is pushed in the social network based on the user relationship data structure, the push range can be effectively improved and the push cost can be effectively reduced by pushing the key user nodes in a targeted manner on the premise of reducing the push times as much as possible.
Drawings
Fig. 1a is a schematic flowchart of a data analysis method based on a social network according to an embodiment of the present disclosure.
Fig. 1b is a schematic diagram of a user relationship data structure 100 of a data analysis method based on a social network according to an embodiment of the present application.
Fig. 2 is a schematic flowchart of a data analysis method based on a social network according to another embodiment of the present disclosure.
Fig. 3 is a block diagram of a data analysis apparatus based on a social network according to an embodiment of the present disclosure.
Fig. 4 is a schematic structural diagram of an electronic device for implementing the social network-based data analysis method of the present application.
Detailed Description
For a better understanding of the present application, various aspects of the present application will be described in more detail with reference to the accompanying drawings. It should be understood that the detailed description is merely illustrative of exemplary embodiments of the present application and does not limit the scope of the present application in any way. Like reference numerals refer to like elements throughout the specification. The expression "and/or" includes any and all combinations of one or more of the associated listed items.
In the drawings, the size, dimension, and shape of elements have been slightly adjusted for convenience of explanation. The figures are purely diagrammatic and not drawn to scale. As used herein, the terms "approximately", "about" and the like are used as table-approximating terms and not as table-degree terms, and are intended to account for inherent deviations in measured or calculated values that would be recognized by one of ordinary skill in the art. In addition, in the present application, the order in which the processes of the respective steps are described does not necessarily indicate an order in which the processes occur in actual operation, unless explicitly defined otherwise or can be inferred from the context.
It will be further understood that terms such as "comprising," "including," "having," "including," and/or "containing," when used in this specification, are open-ended and not closed-ended, and specify the presence of stated features, elements, and/or components, but do not preclude the presence or addition of one or more other features, elements, components, and/or groups thereof. Furthermore, when a statement such as "at least one of" appears after a list of listed features, it modifies that entire list of features rather than just individual elements in the list. Furthermore, when describing embodiments of the present application, the use of "may" mean "one or more embodiments of the present application. Also, the term "exemplary" is intended to refer to an example or illustration.
Unless otherwise defined, all terms (including engineering and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. It will be further understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the relevant art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
In addition, the embodiments and features of the embodiments in the present application may be combined with each other without conflict. The present application will be described in detail below with reference to the accompanying drawings in conjunction with embodiments.
Fig. 1a is a schematic flowchart of a data analysis method based on a social network according to an embodiment of the present disclosure. As shown in fig. 1a, a social network-based data analysis method according to an embodiment of the present application may include:
and step 102, setting key user nodes according to the comparison result of the friend number of the user nodes and the friend threshold value.
In the data analysis method based on the social network, the user relationship data structure may include connection relationships between user nodes. The method for generating the user relationship data structure may include: step 1, binding network software and a software development tool (SDK) for data acquisition; step 2, acquiring user dynamic information of the social network software according to the operation behavior of a network software user; step 3, analyzing the user dynamic information to generate a user node; and step 4, connecting different user nodes according to the user dynamic information to generate a user relationship data structure.
The network software may be software with a sharing function, such as software with functions of article sharing, poster sharing, coupon sharing, user interest sharing, and the like. The network software may include: social software such as WeChat, nail, flybook, twitter, facebook, and the like. When the network software is social software, more information about the interpersonal relationship can be acquired, such as sharing behavior, browsing behavior, chatting behavior, and the like. The network software may further include: shopping platform software such as Taobao, Tamao, Kyoto, Amazon, etc. When the network software is shopping platform software, more information about commercial behaviors, such as purchasing behaviors, promotion behaviors and the like, can be acquired.
Fig. 1b is a schematic diagram of a user relationship data structure 100 of a data analysis method based on a social network according to an embodiment of the present application. As shown in FIG. 1b, user relationship data structure 100 includes: a first user node 11, a second user node 12, a third user node 13, a fourth user node 24. The first user node 11 and its friends, the second user node 12 and its friends, the third user node 13 and its friends together form a first user node relationship network 10, and the fourth user node 24 and its friends together form a first user node relationship network 20.
The number of friends of the first user node 11 is 8, and the friends include 6 black solid circles separated from the first user node, and a second user node 12 and a third user node 13. The second user node 12 has a friend number of 7, including 6 black solid circles separated from the second user node and the first user node 11. The third user node 13 has a friend number of 4, including 3 black solid circles separated from the third user node and the first user node 11. The fourth user node 14 has a friend count of 4, including 4 solid black circles that are separated from the fourth user node.
The second user node 12 and the third user node 13 are first degree friends of the first user node 11, respectively. The buddies of the second user node 12 (i.e., the 6 black filled circles separated from the second user node) are second degree buddies of the first user node 11. Likewise, the buddies of the third user node 13 (i.e., the 3 black filled circles separated from the second user node) are second degree buddies of the first user node 11.
It should be understood by those skilled in the art that the user relationship data structure shown in fig. 1b is only used to illustrate the number of friends, friends of the first degree, and friends of the second degree, and in practical cases, the number of friends may be more, and there may be n-th degree friends. Wherein n is a natural number greater than 2.
For the generation method of the user relationship data structure, refer to the chinese patent application No. 202110427784.6.
According to the data analysis method based on the social network, the number of the friends with at least one degree is counted in the user relationship data structure, and the number of the friends is compared with the friend threshold value to determine the key user nodes, so that when the service is pushed in the social network based on the user relationship data structure, the push range can be effectively improved and the push cost can be effectively reduced by pushing the key user nodes in a targeted manner on the premise of reducing the push times as much as possible.
In an embodiment of the data analysis method based on a social network, the user operation behavior may include: sharing behavior in social friend circles and/or forwarding behavior in group chatting. Specifically, when a user node in a user relationship data structure performs a sharing action, the first-degree friend includes: the friend who browses the shared content, the friend who shares the shared content again and/or the friend who forwards the shared content.
Likewise, when a user node in the user relationship data structure performs a forwarding action, the first-degree friend includes: the friend who browses the forwarded content, the friend who shares the forwarded content again and/or the friend who forwards the shared content.
In an embodiment of the data analysis method based on a social network, the step of setting a key user node according to a comparison result between the friend number of the user node and a friend threshold (step 102) may include:
arranging the user nodes according to the number of the first-degree friends;
setting ranking for the user nodes according to the sequence from big to small;
and setting the user nodes with the ranking less than or equal to the ranking threshold value as key user nodes.
In the data analysis method based on the social network, the ranking threshold value is any natural number between 5 and 100.
For example, referring to fig. 1b, it is assumed that there are 50 user nodes in one user relationship data structure, wherein the fifth user node to the fiftieth user node are not illustrated, and the fifth user node to the fiftieth user node are the same as the fourth user node, i.e., there is no connection relationship with other user nodes. The fifth user node, the fifty-th user node, has the same number of friends as it needs, i.e., the fifth user node has 5 friends and the fifty-th user node has 50 friends, respectively.
Referring to fig. 1b, assuming that each black solid circle separated from the user node represents 10000 buddies, the first user node 11 has 60002 buddies, the second user node 12 has 60001 buddies, the third user node 13 has 30001 buddies, and the fourth user node 24 has 30000 buddies.
At this time, the user nodes are arranged according to the number of friends, and a ranking is set for the user nodes, which is specifically as follows: the first user node is 11-bit first name; the second user node 12 is a second name; the third user node 13 is the third name; the fourth user node 24 is the fourth name; the fiftieth user node is the fifth name; … … the fifth user node is the fiftieth.
In the data analysis method based on the social network according to the embodiment of the present application, if the ranking threshold is 5, the first user node 11, the second user node 12, the third user node 13, the fourth user node 24, and the fiftieth user node are all set as key user nodes. If the ranking threshold is 7, then the first user node 11, the second user node 12, the third user node 13, the fourth user node 24, the fiftieth user node, the forty-ninth user node, and the forty-eighth user node are all set as key user nodes.
In the data analysis method based on the social network, the ranking threshold is any natural number between 1% and 10%. For example, it may be 1%, 3%, 5% or 10%.
Continuing with the assumptions made with reference to FIG. 1b, if the ranking threshold is 2%, 1 key user may be set since there are 50 user nodes in the user relationship data structure; if the ranking threshold is 10%, 5 key users may be set because there are 50 user nodes in the user relationship data structure.
Specifically, if the ranking threshold is 2%, the first user node 11 is set as the key user node. If the ranking threshold is 10%, the first user node 11, the second user node 12, the third user node 13, the fourth user node 24, and the fiftieth user node are all set as key user nodes.
It should be understood by those skilled in the art that each user node listed herein is only for illustrating the principles of an embodiment of the present application, and the number of user nodes and the number of friends may be larger in practical situations.
As described in the above embodiment, the ranking threshold may be absolute number or percentage. If the ranking threshold is set to an absolute number, the method is relatively more suitable for a scene with fewer user nodes in the user relationship data structure; if the ranking threshold is set to a percentage, it is relatively more applicable to scenarios where there are more user nodes in the user relationship data structure.
In an embodiment of the data analysis method based on a social network, the step of setting a key user node according to a comparison result between the friend number of the user node and a friend threshold (step 102) includes: and setting the user nodes with the friend number larger than or equal to the number threshold value as key user nodes. Wherein the number threshold is any natural number greater than or equal to 10000.
The assumption made with reference to fig. 1b continues to be used, i.e. the first user node 11 has 60002 buddies, the second user node 12 has 60001 buddies, the third user node 13 has 30001 buddies and the fourth user node 24 has 30000 buddies.
If the number threshold is 40000, both the first user node 11 and the second user node 12 are set as key user nodes.
As described in the above embodiment, if the friend threshold is set as the ranking threshold, the key user node is searched in the user relationship data structure from the ranking perspective; and setting the friend threshold value as a quantity threshold value, and searching key user nodes in a user relationship data structure from the friend quantity perspective. In practical cases, depending on the specifics of the user relationship data structure (e.g., how many user nodes, the size of the number of friends per user node, etc.), one of ordinary skill in the art may use the ranking threshold or the number threshold alone, or may use both the ranking threshold and the number threshold in combination.
Fig. 2 is a schematic flowchart of a data analysis method based on a social network according to another embodiment of the present disclosure. As shown in fig. 2, a social network-based data analysis method according to another embodiment of the present application may include:
Compared with the data analysis method based on the social network in the embodiment of the present application, the data analysis method based on the social network in the another embodiment of the present application has the additional step 202, that is, the step of counting the number of second-degree friends of the user node respectively, and other steps are similar to the steps of the data analysis method based on the social network in the embodiment of the present application, that is, step 201 is similar to step 101, and step 203 is similar to step 102, and thus, details are not repeated.
In the data analysis method based on the social network according to the other embodiment of the application, the number of the friends of the user node is the sum of the number of the friends of the first degree and the number of the friends of the second degree.
The assumptions made with reference to fig. 1b continue to be used. The first user node 11 has 60002 first-degree friends, the second user node 12 has 60001 first-degree friends, and the third user node 13 has 30001 first-degree friends.
As shown in fig. 1b, the second user node 12 and the third user node 13 are first-degree friends of the first user node 11, and friends of the second user node 12 and friends of the third user node 13 are second-degree friends of the first user node 11. The first user node 11 thus has 90002 second degree buddies. At this time, the number of friends (i.e., the sum of the first degree friends and the second degree friends) of the first user node 11 is 150004.
As shown in fig. 1b, the first user node 11 is a first-degree friend of the second user node 12, and the friend of the first user node 11 is a second-degree friend of the second user node 12. The second user node 12 therefore has 60002 second degree buddies. At this time, the number of friends of the second user node 12 is 90003.
As shown in fig. 1b, the first user node 11 is a first-degree friend of the third user node 13, and the friend of the first user node 11 is a second-degree friend of the third user node 13. The third user node 13 therefore has 60002 second degree buddies. At this time, the number of friends of the third user node 13 is 120003.
The method for setting the friend threshold, that is, the method for setting the ranking threshold and the number threshold, has been described in detail in the foregoing embodiments, and therefore, details are not repeated.
According to the data analysis method based on the social network in another embodiment of the application, the number of the friends with two degrees is counted in the user relationship data structure, and the number of the friends is compared with the friend threshold value to determine the key user node, so that when the service is pushed in the social network based on the user relationship data structure, the push range can be effectively improved and the push cost can be effectively reduced by pushing the key user node in a targeted manner on the premise of reducing the push times as much as possible.
In another embodiment of the present application, in the data analysis method based on a social network, the step of respectively counting the number of second-degree friends of the user node (step 202) may be: and respectively counting the number of third degree friends of the user node. The third-degree friends are direct friends of the second-degree friends, and the second-degree friends are direct friends of the first-degree friends. When the friends are counted, the number of the friends is counted every other degree. Of course, it should be understood by those skilled in the art that the number of friends may be counted at two degrees apart, specifically, the number of friends at the first degree and the number of friends at the fourth degree.
Fig. 3 is a block diagram of a data analysis apparatus based on a social network according to an embodiment of the present disclosure. As shown in fig. 3, an apparatus for social network based data analysis according to an embodiment of the present application may include:
a counting module 31, configured to count, in the user relationship data structure, the number of first-degree friends of the user node respectively based on a user operation behavior;
and the setting module 32 is configured to set a key user node according to the comparison result between the friend number of the user node and the friend threshold.
In an embodiment of the data analysis apparatus based on a social network, the user relationship data structure may include connection relationships between user nodes. The method for generating the user relationship data structure may include: step 1, binding network software and a software development tool (SDK) for data acquisition; step 2, acquiring user dynamic information of the social network software according to the operation behavior of a network software user; step 3, analyzing the user dynamic information to generate a user node; and step 4, connecting different user nodes according to the user dynamic information to generate a user relationship data structure.
Since each step is described in detail in the data analysis method based on the social network in an embodiment of the present application, it is not repeated here.
According to the data analysis device based on the social network, the number of the friends of at least one degree of users is counted in the user relationship data structure, and the number of the friends is compared with the number threshold value to determine the key user nodes, so that when the service is pushed in the social network based on the user relationship data structure, the push range can be effectively improved and the push cost can be effectively reduced by pushing the key user nodes in a targeted manner on the premise of reducing the push times as much as possible.
In an embodiment of the data analysis apparatus based on a social network, the user operation behavior may include: sharing behavior in social friend circles and/or forwarding behavior in group chatting. Specifically, when a user node in a user relationship data structure performs a sharing action, the first-degree friend includes: the friend who browses the shared content, the friend who shares the shared content again and/or the friend who forwards the shared content.
Likewise, when a user node in the user relationship data structure performs a forwarding action, the first-degree friend includes: the friend who browses the forwarded content, the friend who shares the forwarded content again and/or the friend who forwards the shared content.
In the data analysis apparatus based on a social network according to an embodiment of the present application, the setting module 32 is further configured to arrange the user nodes according to the number of the first-degree friends; the ranking is set for the user nodes according to the sequence from big to small; and the user nodes with the ranking less than or equal to the ranking threshold value are set as key user nodes.
At this time, the ranking threshold is any natural number between 5 and 100.
At this time, the ranking threshold is any natural number between 1% and 10%.
In the data analysis apparatus based on a social network according to an embodiment of the present application, the setting module 32 is further configured to set the user node with the friend number greater than or equal to the number threshold as a key user node. In this case, the number threshold is any natural number greater than or equal to 10000.
In the data analysis apparatus based on a social network according to an embodiment of the present application, the statistics module 31 is further configured to respectively count the number of second-degree friends of the user node. At this time, the number of friends of the user node is the sum of the number of friends of the first degree and the number of friends of the second degree.
In the data analysis method based on the social network in the embodiment of the application, the friend threshold values, that is, the ranking threshold value and the number threshold value, the statistics of the first-degree friends and the statistics of the multiple-degree friends are described in detail, and thus, the details are not repeated.
Fig. 4 is a schematic structural diagram of an electronic device for implementing the social network-based data analysis method of the present application. The electronic device of the present embodiment may include, but is not limited to, a mobile terminal such as a mobile phone, a notebook computer, a digital broadcast receiver, a PDA (personal digital assistant), a PAD (tablet computer), a PMP (portable multimedia player), a vehicle-mounted terminal (e.g., a car navigation terminal), and the like, and a stationary terminal such as a digital TV, a desktop computer, and the like.
As shown in fig. 4, electronic device 800 may include at least a portion of a processing means 801, a Read Only Memory (ROM)802, a Random Access Memory (RAM)803, a bus 804, an input/output interface 805, an input means 806, an output means 807, a storage means 808, and a communication means 809.
The processing device 801 may be a central processing unit, a graphics processing unit, or the like, and may perform various corresponding actions and processes according to a program stored in the read only memory 802 or a program loaded from the storage device 808 into the random access memory 803.
The processing device 801, the read only memory 802 and the random access memory 803 are connected to each other by a bus 804. An input/output (I/O) interface 805 is connected to bus 804.
The input/output interface 805 may be connected to an input device 806, an output device 807, a storage device 808, and a communication device 809. Among other things, the input device 806 may include: touch screen, touch pad, keyboard, mouse, camera, microphone, accelerometer, gyroscope, etc. The output device 807 may include: liquid Crystal Displays (LCDs), speakers, vibrators, and the like. The storage 808 may include: magnetic tape, hard disk, etc. And a communication device 809 for the electronic device 800 to perform wireless or wired communication with other devices to exchange data.
In particular, the steps illustrated in the flow charts of the method embodiments according to the present application may be implemented as computer software programs. For example, embodiments of the present application include a computer program product comprising a computer program embodied on a computer readable storage medium, the computer program comprising program code for performing the steps illustrated in the flowchart. In such embodiments, the computer program may be downloaded and installed from a network via the communication means 809, or read and installed from the storage means 808, or read and installed from the read-only memory 802. The computer program, when executed by the processing apparatus 801, performs the above-described functions defined in the methods of the embodiments of the present application. It should be noted that the computer readable storage medium described in the embodiments of the present application may be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In embodiments of the application, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In embodiments of the present application, however, a computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable storage medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable storage medium may be transmitted using any appropriate medium, including but not limited to: electrical wires, optical cables, RF (radio frequency), etc., or any suitable combination of the foregoing.
The computer-readable storage medium may be included in the electronic device; or may exist separately without being assembled into the electronic device. The computer readable storage medium carries one or more programs which, when executed by the electronic device, cause the electronic device to: in response to receiving the problem to be identified, identifying the slot position in the problem by adopting at least two identification algorithms to obtain at least two slot position result sets; constructing at least two candidate paths according to the position conflict relationship of each slot position result in the at least two slot position result sets in the problem; for each candidate path, scoring each slot position result in the candidate path, and calculating the sum of scores of each slot position result in the candidate path as the slot position score of the candidate path; and outputting a slot result included by the candidate path with the highest slot score.
Computer program code for carrying out operations for embodiments of the present application may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C + +, and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the other devices through any kind of network, in particular, through a local area network or a wide area network to the user's computer, or through the internet to external computers.
The objects, technical solutions and advantageous effects of the present invention are further described in detail with reference to the above-described embodiments. It should be understood that the above description is only a specific embodiment of the present invention, and is not intended to limit the present invention. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.
Claims (24)
1. A data analysis method based on a social network, comprising a user relationship data structure, wherein the user relationship data structure comprises connection relationships between user nodes, the method comprising:
respectively counting the number of first-degree friends of the user node in the user relationship data structure based on user operation behaviors; and
and setting key user nodes according to the comparison result of the friend number of the user nodes and the friend threshold value.
2. The social network-based data analysis method of claim 1, wherein the user-operated behavior comprises:
sharing behavior in a circle of social friends, and/or
Forwarding behavior in group chat.
3. The social network based data analysis method of claim 2,
when the user operation behavior is a sharing behavior, the first-degree friend includes: the friend who browses the shared content, the friend who shares the shared content again and/or the friend who forwards the shared content;
when the user operation behavior is a forwarding behavior, the first-degree friend includes: the friend who browses the forwarded content, the friend who shares the forwarded content again and/or the friend who forwards the shared content.
4. The social network-based data analysis method of claim 1, wherein the step of setting key user nodes according to the comparison result of the friend number of the user node and the friend threshold comprises:
arranging the user nodes according to the number of the first-degree friends;
setting ranking for the user nodes according to the sequence from big to small; and
and setting the user nodes with the ranking less than or equal to the ranking threshold value as key user nodes.
5. The social network-based data analysis method of claim 4, wherein the ranking threshold is any natural number between 5 and 100.
6. The social network-based data analysis method of claim 4, wherein the ranking threshold is any natural number between 1% and 10%.
7. The social network-based data analysis method of claim 1, wherein the step of setting key user nodes according to the comparison result of the friend number of the user node and the friend threshold comprises:
and setting the user nodes with the friend number larger than or equal to the number threshold value as key user nodes.
8. The social network based data analysis method of claim 7,
the number threshold is any natural number greater than or equal to 10000.
9. The social network-based data analysis method according to any one of claims 1 to 8, further comprising, after counting the number of first degree friends of the user node respectively:
and respectively counting the number of second-degree friends of the user node.
10. The social network based data analysis method of claim 9,
and the friend number of the user node is the sum of the friend number of the first degree and the friend number of the second degree.
11. The social network-based data analysis method according to any one of claims 1 to 8, further comprising, after counting the number of first degree friends of the user node respectively:
respectively counting the number of third degree friends of the user node,
the third-degree friends are direct friends of the second-degree friends, and the second-degree friends are direct friends of the first-degree friends.
12. The social network based data analysis method of claim 11,
and the friend number of the user node is the sum of the friend number of the first degree and the friend number of the third degree.
13. A social network-based data analysis apparatus comprising a user relationship data structure, wherein the user relationship data structure comprises connection relationships between user nodes, comprising:
the counting module is used for respectively counting the number of first-degree friends of the user node in the user relationship data structure based on user operation behaviors; and
and the setting module is used for setting key user nodes according to the comparison result of the friend number of the user nodes and the friend threshold value.
14. The social network-based data analytics device of claim 13, wherein the user-operated behavior comprises:
sharing behavior in a circle of social friends, and/or
Forwarding behavior in group chat.
15. The social network based data analytics device of claim 14,
when the user operation behavior is a sharing behavior, the first-degree friend includes: the friend who browses the shared content, the friend who shares the shared content again and/or the friend who forwards the shared content;
when the user operation behavior is a forwarding behavior, the first-degree friend includes: the friend who browses the forwarded content, the friend who shares the forwarded content again and/or the friend who forwards the shared content.
16. The social network-based data analysis device of claim 13, wherein the setting module is further configured to rank the user nodes according to the number of first degree friends; the ranking is set for the user nodes according to the sequence from big to small; and the user nodes with the ranking less than or equal to the ranking threshold value are set as key user nodes.
17. The social network-based data analytics device of claim 16, wherein the ranking threshold is any natural number between 5-100.
18. The social network-based data analytics device of claim 16, wherein the ranking threshold is any natural number between 1% -10%.
19. The social network-based data analysis device of claim 14, wherein the setting module is further configured to set the user node with the friend number greater than or equal to a number threshold as a key user node.
20. The social network based data analytics device of claim 19,
the number threshold is any natural number greater than or equal to 10000.
21. The social network-based data analysis device of any one of claims 13-20, wherein the statistics module is further configured to count the number of second degree friends of the user node respectively.
22. The social network based data analytics device of claim 21,
and the friend number of the user node is the sum of the friend number of the first degree and the friend number of the second degree.
23. A computer-readable storage medium storing computer program instructions for implementing the method according to any one of claims 1-11 when the computer program instructions are executed by a processor.
24. An electronic device comprising a memory and a processor, wherein the memory is configured to store one or more computer program instructions, wherein execution of the one or more computer program instructions by the processor implements the method of any of claims 1-11.
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