CN113076335A - Network cause detection method, system, equipment and storage medium - Google Patents
Network cause detection method, system, equipment and storage medium Download PDFInfo
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Abstract
The invention discloses a network cause detection method, a system, equipment and a storage medium, wherein the method comprises the following steps: receiving a network vocabulary storage request generated in each time period, digitally storing the network vocabulary generated in each time period, and writing the digitized network vocabulary into a block chain node; storing network vocabularies and user relationship information by using the block link points; constructing a multi-agent model, and acquiring a propagation evolution path of the relation between network words and users in each time period according to the multi-agent model; the popularity of the network vocabularies in each time period is calculated according to the propagation evolution path of the relationship between the network vocabularies and the users in each time period, when the popularity of any network vocabulary in each time period is larger than or equal to a preset modular cause detection popularity threshold value, the network vocabulary is used as a modular cause vocabulary, network modular cause detection based on multiple intelligent agents and block chains is completed, and the method, the system, the equipment and the storage medium can accurately detect the network modular causes.
Description
Technical Field
The invention belongs to the field of information transmission and sharing, and relates to a network cause detection method, a system, equipment and a storage medium.
Background
Network cause. The model cause (Meme) describes basic units in the process of human culture and human language propagation and inheritance, the current research on the model cause can help us to well know the propagation characteristics of the model cause, the propagation process of the network model cause is influenced by a plurality of factors, the change of each factor directly or indirectly changes the propagation behavior of the model cause, and the actually occurring data recorded in the social media is only one of the millions of possible occurring states. The network model refers to the vocabulary with large transmission potential, wide transmission range and special background and meaning in a certain time period in the network vocabulary. The social network is a complex system, and the user, other users and the external environment all affect the online behavior of the user, so that the propagation of the network cause is affected, however, the network cause cannot be detected more accurately in the prior art.
Disclosure of Invention
The present invention is directed to overcome the above-mentioned shortcomings in the prior art, and provides a method, a system, a device and a storage medium for detecting a network cause, which can detect a network cause more accurately.
In order to achieve the above object, the network cause detection method of the present invention comprises:
receiving a network vocabulary storage request generated in each time period, digitally storing the network vocabulary generated in each time period, and writing the digitized network vocabulary into a block chain node;
storing network vocabularies and user relationship information by using the block link points;
constructing a multi-agent model, and acquiring a propagation evolution path of the relation between network words and users in each time period according to the multi-agent model;
and calculating the popularity of the network vocabularies in each time period according to the propagation evolution path of the relationship between the network vocabularies and the users in each time period, and when the popularity of any network vocabulary in each time period is greater than or equal to a preset modular cause detection popularity threshold value, taking the network vocabulary as a modular cause vocabulary to complete network modular cause detection based on the multi-agent and the block chain.
And determining a popularity threshold value of the factor detection according to the existing factor vocabulary and the preset popularity.
Popularity Q of network vocabulary in current time periodi＝α∑Qt.pWherein Q ist.pIn order to count the number of over-forwarded or propagated behaviors to the network vocabulary in the time period t, α is the propagation coefficient of the current time period.
State h of ith node in t +1 periodi(t+1)＝hi(t)+ri+Ii+OiWherein h isi(t) is the state of the ith node for a period of t, riNumber of forwarding for the I-th node for surrounding nodes, IiNumber of input words obtained for i-th node, OiThe number of words forwarded to other nodes for the ith node.
A network cause detection system comprising:
the network vocabulary storage module is used for receiving the network vocabulary storage request generated in each time interval, digitally storing the network vocabulary generated in each time interval and writing the digitized network vocabulary into the block chain nodes;
the storage module is used for storing network vocabularies and user relationship information by using the block chain link points;
the path acquisition module is used for constructing a multi-agent model and acquiring a propagation evolution path of the relation between network vocabularies and users in each time period according to the multi-agent model;
and the modular factor detection module is used for calculating the popularity of the network vocabularies in each time period according to the propagation evolution path of the relationship between the network vocabularies in each time period and the users, and when the popularity of any one network vocabulary in each time period is greater than or equal to a preset modular factor detection popularity threshold value, the network vocabulary is used as a modular factor vocabulary to complete network modular factor detection based on the multi-agent and the block chain.
And determining a popularity threshold value of the factor detection according to the existing factor vocabulary and the preset popularity.
Popularity Q of network vocabulary in current time periodi＝α∑Qt.pWherein Q ist.pIn order to count the number of over-forwarded or propagated behaviors to the network vocabulary in the time period t, α is the propagation coefficient of the current time period.
State h of ith node in t +1 periodi(t+1)＝hi(t)+ri+Ii+OiWherein h isi(t) is the state of the ith node for a period of t, riNumber of forwarding for the I-th node for surrounding nodes, IiNumber of input words obtained for i-th node, OiThe number of words forwarded to other nodes for the ith node.
A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, the processor implementing the steps of the network cause detection method when executing the computer program.
A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out the steps of the network cause detection method.
The invention has the following beneficial effects:
in addition, the network vocabulary generated in each time period can be safely stored in the system by storing the network vocabulary in the current time period based on the block chain technology, all information can not be falsified and forged, and the network dynamics and the effective supervision of public sentiment are ensured.
Drawings
FIG. 1 is a flow chart of the present invention;
FIG. 2 is a diagram of a node connection structure in a multi-agent model.
Detailed Description
In order to make the technical solutions of the present invention better understood, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, not all of the embodiments, and are not intended to limit the scope of the present disclosure. Moreover, in the following description, descriptions of well-known structures and techniques are omitted so as to not unnecessarily obscure the concepts of the present disclosure. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
There is shown in the drawings a schematic block diagram of a disclosed embodiment in accordance with the invention. The figures are not drawn to scale, wherein certain details are exaggerated and possibly omitted for clarity of presentation. The shapes of various regions, layers and their relative sizes and positional relationships shown in the drawings are merely exemplary, and deviations may occur in practice due to manufacturing tolerances or technical limitations, and a person skilled in the art may additionally design regions/layers having different shapes, sizes, relative positions, according to actual needs.
Example one
Referring to fig. 1 and 2, the network cause detection method of the present invention includes:
1) receiving a network vocabulary storage request generated in each time period, digitally storing the network vocabulary generated in each time period, writing the digitized network vocabulary into a block chain node, and establishing digitized network vocabulary information of each time period, wherein, initially, the positions of the vocabulary and the information source are randomly distributed in an online environment, and the network topology of the network node is static and cannot be increased, reduced or re-linked with the time.
2) Storing network vocabularies and user relation information by using the block link points, and storing the user relation information and forwarding information;
3) constructing a multi-agent model, and acquiring a propagation evolution path of the relation between network words and users in each time period according to the multi-agent model;
besides three influencing factors of the user, other users and the environment, the state of the user is also influenced by the state of the user at the previous moment. The primary mode is participated by all agents due to vocabulary propagation, the influence from other users is mainly expressed by the social relationship among the users, the number of neighbor users who have forwarded the post and the influence from other sub-networks, and therefore the state h of the ith node in the period of t +1i(t+1)＝hi(t)+ri+Ii+OiWherein h isi(t) is the state of the ith node for a period of t, riNumber of forwarding for the I-th node for surrounding nodes, IiNumber of input words obtained for i-th node, OiThe number of words forwarded to other nodes for the ith node.
4) And calculating the popularity of the network vocabularies in each time period according to the propagation evolution path of the relationship between the network vocabularies and the users in each time period, and when the popularity of any network vocabulary in each time period is greater than or equal to a preset modular cause detection popularity threshold value, taking the network vocabulary as a modular cause vocabulary to complete network modular cause detection based on the multi-agent and the block chain.
Popularity Q of network vocabulary in current time periodi＝α∑Qt.pWherein Q ist.pIn order to obtain the number of the forwarding or propagation behaviors of the network vocabulary in the time range of t, alpha is the propagation coefficient of the current time period, in the initial operation stage of the system, according to the propagation condition between the existing network modular vocabulary and the nodes,and determining a network modular popularity threshold of the time period, if the influence of the neighbor users on the network modular popularity threshold exceeds the threshold, converting the network modular popularity threshold into a modular propagation activation state by the users, and determining the network vocabulary as a modular candidate vocabulary.
Example two
A network cause detection system comprising:
the network vocabulary storage module is used for receiving the network vocabulary storage request generated in each time interval, digitally storing the network vocabulary generated in each time interval and writing the digitized network vocabulary into the block chain nodes;
the storage module is used for storing network vocabularies and user relationship information by using the block chain link points;
the path acquisition module is used for constructing a multi-agent model and acquiring a propagation evolution path of the relation between network vocabularies and users in each time period according to the multi-agent model;
and the modular factor detection module is used for calculating the popularity of the network vocabularies in each time period according to the propagation evolution path of the relationship between the network vocabularies in each time period and the users, and when the popularity of any one network vocabulary in each time period is greater than or equal to a preset modular factor detection popularity threshold value, the network vocabulary is used as a modular factor vocabulary to complete network modular factor detection based on the multi-agent and the block chain.
Example four
A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that the steps of the network cause detection method are implemented by the processor when executing the computer program.
EXAMPLE five
A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out the steps of the network cause detection method.
The invention distributes network vocabularies through a multi-agent model, and explores the spreading factor propagation rule and the intervention mechanism of public sentiment by changing the values of three factors through the influence of surrounding neighbors, the resistance of a user to new information and the influence of external environment. Meanwhile, the block chain is used for storing and managing the Internet hot words in the current time period in a distributed mode, the multi-agent model is used for constructing a model cause information propagation evolution path, and the model cause words in the current time period are determined according to the generated model cause popularity numerical values.
Claims (10)
1. A network cause detection method is characterized by comprising the following steps:
receiving a network vocabulary storage request generated in each time period, digitally storing the network vocabulary generated in each time period, and writing the digitized network vocabulary into a block chain node;
storing network vocabularies and user relationship information by using the block link points;
constructing a multi-agent model, and acquiring a propagation evolution path of the relation between network words and users in each time period according to the multi-agent model;
and calculating the popularity of the network vocabularies in each time period according to the propagation evolution path of the relationship between the network vocabularies and the users in each time period, and when the popularity of any network vocabulary in each time period is greater than or equal to a preset modular cause detection popularity threshold value, taking the network vocabulary as a modular cause vocabulary to complete network modular cause detection based on the multi-agent and the block chain.
2. The method of claim 1, wherein the popularity threshold is determined based on an existing cause vocabulary and a predetermined popularity.
3. The method according to claim 1, wherein the popularity Q of network vocabulary in the current time period isi＝α∑Qt.pWherein Q ist.pIn order to count the number of over-forwarded or propagated behaviors to the network vocabulary in the time period t, α is the propagation coefficient of the current time period.
4. The network cause detection method of claim 1, wherein the network cause detection method comprisesAt the ith node's state h during the t +1 periodi(t+1)＝hi(t)+ri+Ii+OiWherein h isi(t) is the state of the ith node for a period of t, riNumber of forwarding for the I-th node for surrounding nodes, IiNumber of input words obtained for i-th node, OiThe number of words forwarded to other nodes for the ith node.
5. A network cause detection system, comprising:
the network vocabulary storage module is used for receiving the network vocabulary storage request generated in each time interval, digitally storing the network vocabulary generated in each time interval and writing the digitized network vocabulary into the block chain nodes;
the storage module is used for storing network vocabularies and user relationship information by using the block chain link points;
the path acquisition module is used for constructing a multi-agent model and acquiring a propagation evolution path of the relation between network vocabularies and users in each time period according to the multi-agent model;
and the modular factor detection module is used for calculating the popularity of the network vocabularies in each time period according to the propagation evolution path of the relationship between the network vocabularies in each time period and the users, and when the popularity of any one network vocabulary in each time period is greater than or equal to a preset modular factor detection popularity threshold value, the network vocabulary is used as a modular factor vocabulary to complete network modular factor detection based on the multi-agent and the block chain.
6. The network cause detection system of claim 1, wherein the cause detection popularity threshold is determined based on an existing cause vocabulary and a preset popularity.
7. The system according to claim 1, wherein the popularity Q of network vocabulary in the current time period isi＝α∑Qt.pWherein Q ist.pIn order to count the number of over-forwarded or propagated behaviors to the network vocabulary in the time period t, α is the propagation coefficient of the current time period.
8. The network cause detection system of claim 1, wherein the state h of the ith node during the t +1 periodi(t+1)＝hi(t)+ri+Ii+OiWherein h isi(t) is the state of the ith node for a period of t, riNumber of forwarding for the I-th node for surrounding nodes, IiNumber of input words obtained for i-th node, OiThe number of words forwarded to other nodes for the ith node.
9. Computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that the processor realizes the steps of the network cause detection method according to any of claims 1 to 4 when executing the computer program.
10. A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out the steps of the network cause detection method according to any one of claims 1 to 4.
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