CN115102920B - Individual transmission and management control method based on relational network - Google Patents
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Abstract
The invention provides a transmission control method of individuals based on a relational network, which comprises the steps of constructing the relational network by taking each individual as a node, obtaining the attribution element of each node in the relational network, carrying out mobile search in the relational network according to the attribution element of each node, calculating the entanglement value of the individual, screening out a target group, carrying out transmission control on the individuals in the relational network by the target group, realizing analysis through the data characteristics of mutual attention and access among users, carrying out message control according to the association degree among the individuals and achieving the beneficial effect of effectively reducing harassment information.
Description
Technical Field
The invention belongs to the field of big data processing, and particularly relates to an individual transmission pipe control method based on a relational network.
Background
On the internet social platform, there are large-scale active users, and a large number of users have data access to a plurality of different users in a short time. Different access activities exist among different users, and the technical difficulty of measuring access among individuals is high due to different access frequencies and different access objects. Among individuals of a relationship network, in some cases, receipt of a message from a stranger may adversely affect the user experience of the internet social platform. Therefore, how to manage the transmission of messages according to the familiarity among individuals is a key point for the technical management of the operation of the internet social platform. Although a user relation graph and semantic analysis of microblog contents are fused to detect abnormal users, a certain accuracy is obtained in identifying the abnormal users, the calculation cost and time cost for semantic analysis are high, interactive features of mutual attention and access among social account users are not added into calculation, and the information is still not sufficiently controlled according to the association degree among individuals.
Disclosure of Invention
The present invention is directed to a method and system for controlling individual transmission and control based on a relational network, so as to solve one or more technical problems in the prior art and provide at least one useful choice or creation condition.
The invention provides an individual transmission control method and system based on a relational network, which are characterized in that each individual is taken as a node to construct the relational network, the attribution element of each node in the relational network is obtained, mobile search is carried out in the relational network according to the attribution element of each node, the entanglement value of the individual is calculated, a target group is screened out, and the individual in the relational network is subjected to transmission control by the target group.
In order to achieve the above object, according to an aspect of the present invention, there is provided a method for individual transmission control based on a relationship network, the method comprising the steps of:
s100, acquiring each individual, and constructing a relational network by taking each individual as a node;
s200, acquiring the attribution elements of each node in the relational network;
s300, performing mobile search in a relational network according to the attribution elements of each node, and calculating an individual entanglement value;
s400, screening out a target group according to the individual entanglement values;
and S500, carrying out transmission control on the individuals in the relational network according to the target group.
Further, in S100, the method of acquiring each individual and constructing the relationship network with each individual as a node includes:
acquiring data of a plurality of registered or stored different users from a database of the social network site or the social software, wherein the individuals are users registered on the social network site or the social software, and the data of the users comprise: the user identification, the other user identification concerned by the user and the access frequency of the user to the other concerned user, wherein the identification is a numerical value, the identification is the unique identification of the user, the users are concerned with each other, namely, two different users can be concerned with each other, and the user data at least comprises a pair of users concerned with each other;
according to the data of the user, the data of different users are respectively used as different nodes, the nodes correspond to the users, and the nodes have the corresponding user identity marks;
the access frequency is the arithmetic mean of the number of times one user accesses another user within each day of the week;
for each node, respectively making a directed edge from the node to each node concerned by the node, wherein the direction of the directed edge is from the node concerned to the node concerned, a pair of parallel directed edges with opposite directions and equal length exist between two nodes concerned with each other, so that the nodes are connected to form a relationship network by making the directed edges, and the length of the directed edges is the time cost or the data transmission cost for carrying out data transmission from the node concerned to the node concerned.
Further, in S200, the method for acquiring the home element of each node in the relationship network includes:
the out degree of one node represents the number of directed edges starting from the one node, and the in degree of the one node represents the number of directed edges pointing to the one node;
recording the number of nodes in the relation network as n, recording the serial number of the nodes in the relation network as i, wherein i belongs to [1, n ], and the serial number of each node in the relation network is n different positive integers in [1, n ];
in the relational network, a node with a sequence number i is recorded as Net (i), and the numerical value of the sequence number i is the identity of the Net (i);
the function in () represents the degree of entry of a node with a corresponding sequence number obtained by inputting the sequence number of the node in the parenthesis of the function, and in (i) represents the degree of entry of a node with a sequence number i in the relational network;
the function ex () represents the degree of departure of the node with the corresponding sequence number by inputting the sequence number of the node in the parenthesis of the function, and then ex (i) represents the degree of departure of the node with the sequence number i in the relational network;
recording i1 and i2 as two variables with unequal numerical values in [1, n ], respectively representing the serial numbers of two different nodes in the relational network by i1 and i2, acquiring the node Net (i 1) with the serial number of i1 in the relational network, and acquiring the node Net (i 2) with the serial number of i2 in the relational network;
a function fre () is a function for acquiring access frequency of a user represented by one node to a user represented by another node, wherein the use of the function fre () requires inputting serial numbers of two different nodes into the function, the input serial numbers of the two different nodes are sequentially divided in parentheses, the user corresponding to the node with the previous serial number accesses, the user corresponding to the node with the next serial number accesses, the user corresponding to the node with the previous serial number accesses the user corresponding to the node with the next serial number, and then fre (i 1, i 2) represents the access frequency of Net (i 1) to Net (i 2);
the function Focus () is a set of other nodes concerned by the node corresponding to the acquired input sequence number, and Focus (i) represents a set of other nodes concerned by the node with the sequence number of i;
the function Fans () is a set of other nodes which are concerned about the node corresponding to the input sequence number, and Fans (i) represents a set of other nodes which are concerned about the node with the sequence number i, namely a set formed by all the nodes which are concerned about the node with the sequence number i;
the attribution factor defining a node is an array consisting of the out degree of the node, the in degree of the node, the access frequency of the node to other nodes, the access frequency of other nodes to the node, the set of other nodes concerned by the node, and the set of other nodes concerned by the node (i.e., the set of other nodes concerned by the node).
Further, in S300, according to the attribution factor of each node, a mobile search is performed in the relational network, and the method for calculating the individual entanglement value specifically includes:
s301, acquiring Focus (i) corresponding to each Net (i), acquiring Fans (i) corresponding to each Net (i), acquiring ex (i) corresponding to each Net (i) and acquiring in (i) corresponding to each Net (i) for each node Net (i) in the calculation relationship network;
s302, calculating the access frequency of Net (i) to each node in Focus (i) and accumulating and summing to obtain freFocus (i); calculating the access frequency of each node in the Fans (i) to the corresponding Net (i) and performing accumulation and summation to obtain freFans (i);
s303, for a node Net (i) with a sequence number i, determining whether the corresponding Focus (i) is an empty set or Fans (i) is an empty set, setting a flag value for each node, setting a variable biao (i) as the flag value of the node Net (i), and calculating a shunt value for each node, specifically:
the shunt value corresponding to the node Net (i) with the sequence number of i is recorded as up (i);
then the following condition judgment is carried out;
if the condition that the Focus (i) corresponding to the node Net (i) is an empty set and the Fans (i) is not the empty set is met, setting the marking value of the node Net (i) to be 0, and going to S304;
if the conditions that Fans (i) corresponding to the node Net (i) is an empty set and Focus (i) is not the empty set are met, setting the marking value of the node Net (i) to be 1, and going to S305;
if the condition that the corresponding Focus (i) is an empty set and the corresponding Fans (i) is also an empty set is met, setting the marking value of the node Net (i) to be-1, and going to S306;
if the condition that the corresponding Focus (i) is not an empty set and the corresponding Fans (i) is not an empty set is met at the same time, turning to S307 to set the marking value of the node Net (i);
s304, calculating and obtaining a shunt value of the node Net (i) with the sequence number i by using a first formula, recording and storing the shunt value, wherein an exponential function with a natural constant e as a base is calculated by exp (), and the calculation method of the first formula is as follows:
go to S308;
s305, calculating and obtaining the shunt value of the node Net (i) with the sequence number i by using a second formula, and recording and storing the shunt value, wherein the calculation method of the second formula is as follows:
go to S308;
s306, calculating by using a third formula to obtain the shunt value of the node Net (i) with the sequence number i, recording and storing, wherein the calculation method of the third formula is as follows:
or the following steps:
go to S308;
s307, a fourth formula is used for calculating, recording and storing the shunt value of the node Net (i) with the sequence number i, wherein the fourth formula is calculated by the following method:
or the following steps:
assigning the value of up (i) to the labeled value of Net (i), and going to S308;
the method has the advantages that the existing distinguishing method is used for embedding in a graph network or a clustering algorithm, fitting calculated when each node in the relation network distinguishes the used node is global, global fitting is that the numerical calculation cost of the node fluctuating on the edge and the frequency is high, energy consumption is high, the sensitivity of the global fitting node on the edge and the frequency is low, effective probability distribution fitting is carried out on the numerical value of each node fluctuating on the edge and the frequency in the relation network, each node in the relation network can obtain respective distinguishing basis, and the abnormal node which has the low sensitivity of the numerical value of the edge and the frequency fluctuation and cannot be detected can be quickly screened out by distinguishing each node by using the shunt value.
S308, mobile search is carried out in the relation network: judging whether the set composed of the nodes with the index value of 0 is empty, if so, turning to S309, otherwise, taking the set composed of the nodes with the index value of 0 as an initial set and turning to S312;
s309, judging whether a set composed of nodes with the index value of 1 is empty, if so, turning to S310, and if not, taking the set composed of the nodes with the index value of 1 as an initial set and turning to S312;
s310, judging whether a set formed by nodes with the mark value of-1 is empty, if so, turning to S311, otherwise, taking the set formed by the nodes with the mark value of-1 as an initial set, and turning to S312;
s311, taking a set composed of nodes of which the shunt values in the relational network do not exceed the arithmetic mean of all the node shunt values of the relational network as an initial set and transferring to S312;
s312, selecting a node with the minimum shunt value in the initial set as an initial node, recording the serial number of the initial node in the relation network as S, wherein the initial node is Net (S), the shunt value of the Net (S) is up (S), respectively calculating the ratio of up (S) to the shunt value of each other node in the initial set, taking the arithmetic mean of the ratios of up (S) to the shunt values of each other node in the initial set, and taking the arithmetic mean as an individual entanglement value;
the method has the advantages that dynamic node search is carried out in each subset of which the marking values are divided in advance, the initial node is searched, and then the initial node is calculated and compared with other different nodes to obtain the corresponding mutual difference between the nodes in the normal state, so that the nodes exceeding the corresponding mutual difference in the normal state are identified, and therefore in the process of screening out the abnormal nodes with low numerical sensitivity of edge and frequency fluctuation and incapability of detecting, the individual entanglement value can be used for measuring the mutual difference between the nodes based on the contrast degree of the individual entanglement value and the shunt value of other nodes in the initial set, and the difference does not need to be further traversed and calculated, so that the time cost for transmission control is saved.
Further, in S400, the method for screening out the target group according to the individual entanglement value specifically includes:
in the relational network, acquiring the shunt value of each node, and calculating the ratio of the shunt value of each node to the shunt values of other nodes in the relational network for each node;
setting a set Divg to collect nodes with abnormal conditions, and enabling an initial value of the Divg to be an empty set;
and then, judging whether each node has an abnormal condition according to the individual entanglement value, specifically: if the mode of the ratio of the shunt value of one node to the shunt values of other nodes in the relational network is greater than or equal to the individual entanglement value, taking the node as the node with the abnormal condition, and adding the node with the abnormal condition into the set Divg;
and taking the nodes in the set Divg as target groups.
Further, in S500, according to the target group, the method for performing transmission control on the individuals in the relationship network includes:
for individuals in a target group, when the individuals in the target group want to send messages to other individuals in a relationship network, the individuals in the target group (nodes where abnormal conditions exist) are typically registered users operated by organizations such as fraud numbers or water military numbers, and the abnormal nodes in the relationship network are sources of network false information and network fraud flooding, and the individuals who are sent messages are required to confirm whether to accept the information sent by the individuals.
The invention also provides an individual transmission control system based on the relational network, which comprises: the system for managing and controlling the transmission of individuals based on the relational network can be operated in computing devices such as desktop computers, notebook computers, palm computers, cloud data centers and the like, and the operable system can include, but is not limited to, a processor, a memory and a computer program stored in the memory and operable on the processor, the processor executes the computer program and operates in units of the following systems:
the network construction unit is used for acquiring each individual and constructing a relational network by taking each individual as a node;
an attribution element acquiring unit, configured to acquire an attribution element of each node in the relational network;
the mobile search unit is used for carrying out mobile search in the relational network according to the attribution element of each node and calculating an individual entanglement value;
the screening target group unit is used for screening out a target group according to the individual entanglement value;
and the transmission control unit is used for carrying out transmission control on the individuals in the relational network according to the target group.
The invention has the beneficial effects that: the invention provides a method and a system for transmitting and controlling individuals based on a relational network, wherein each individual is taken as a node to construct the relational network, the attribution element of each node in the relational network is obtained, mobile search is carried out in the relational network according to the attribution element of each node, the entanglement value of the individual is calculated, a target group is screened out, the individual in the relational network is transmitted and controlled by the target group, analysis is carried out through the data characteristics of mutual attention and access among users, the message is controlled according to the association degree among the individuals, and the beneficial effect of effectively reducing harassment information is achieved.
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The above and other features of the present invention will become more apparent by describing in detail embodiments thereof with reference to the attached drawings in which like reference numerals designate the same or similar elements, it being apparent that the drawings in the following description are merely exemplary of the present invention and other drawings can be obtained by those skilled in the art without inventive effort, wherein:
FIG. 1 is a flow chart of a method for individual transmission control based on a relational network;
fig. 2 is a system configuration diagram of an individual transmission control system based on a relational network.
Detailed Description
The conception, the specific structure and the technical effects of the present invention will be clearly and completely described in conjunction with the embodiments and the accompanying drawings to fully understand the objects, the schemes and the effects of the present invention. It should be noted that, in the present application, the embodiments and features of the embodiments may be combined with each other without conflict.
In the description of the present invention, a plurality of means is one or more, a plurality of means is two or more, and greater than, less than, more than, etc. are understood as excluding the essential numbers, and greater than, less than, etc. are understood as including the essential numbers. If there is a description of first and second for the purpose of distinguishing technical features only, this is not to be understood as indicating or implying a relative importance or implicitly indicating the number of technical features indicated or implicitly indicating the precedence of technical features indicated.
Fig. 1 is a flowchart of a method for managing and controlling transmission of individuals based on a relational network according to the present invention, and the method and system for managing and controlling transmission of individuals based on a relational network according to an embodiment of the present invention are described below with reference to fig. 1.
The invention provides an individual transmission control method based on a relational network, which specifically comprises the following steps:
s100, acquiring each individual, and constructing a relational network by taking each individual as a node;
s200, acquiring attribution elements of each node in the relational network;
s300, performing mobile search in a relational network according to the attribution elements of each node, and calculating an individual entanglement value;
s400, screening out a target group according to the individual entanglement values;
and S500, carrying out transmission control on the individuals in the relational network according to the target group.
Further, in S100, the method of acquiring each individual and constructing the relational network with each individual as a node includes:
acquiring data of a plurality of registered different users from a social network site or social software, wherein the individuals are the users registered on the social network site or the social software, and the data of the users comprise: the user identification, the identification of other users concerned by the user and the access frequency of other users concerned by the user, wherein the identification is a numerical value, the identification is the unique identification of the user, and the users are concerned with each other;
according to the data of the user, the data of different users are respectively used as different nodes, the nodes correspond to the users, and the nodes have the corresponding user identity marks;
the access frequency is the arithmetic mean of the number of times one user accesses another user within each day of the week;
for each node, respectively making a directed edge from the node to each node concerned by the node, wherein the direction of the directed edge is from the node concerned to the concerned node, and a pair of parallel directed edges with opposite directions exist between two nodes concerned with each other, so that the nodes are connected to form a relationship network by making the directed edges between the nodes.
Further, in S200, the method for acquiring the attribution element of each node in the relationship network comprises:
the out degree of one node represents the number of directed edges starting from the one node, and the in degree of the one node represents the number of directed edges pointing to the one node;
recording the number of nodes in the relation network as n, recording the serial number of the nodes in the relation network as i, wherein i belongs to [1, n ], and the serial number of each node in the relation network is n different constants in [1, n ];
in the relational network, a node with a sequence number i is recorded as Net (i), and the numerical value of the sequence number i is the identity of the Net (i);
the function in () represents the degree of entry of a node with a corresponding sequence number obtained by inputting the sequence number of the node in the parenthesis of the function, and in (i) represents the degree of entry of a node with a sequence number i in the relational network;
the function ex () represents the degree of departure of the node with the corresponding sequence number by inputting the sequence number of the node in the parenthesis of the function, and then ex (i) represents the degree of departure of the node with the sequence number i in the relational network;
recording i1 and i2 as two variables with unequal numerical values in [1, n ], respectively representing the serial numbers of two different nodes in the relational network by i1 and i2, acquiring the node Net (i 1) with the serial number of i1 in the relational network, and acquiring the node Net (i 2) with the serial number of i2 in the relational network;
a function fre () is a function for acquiring access frequency of a user represented by one node to a user represented by another node, wherein the use of the function fre () requires inputting serial numbers of two different nodes into the function, the input serial numbers of the two different nodes are sequentially divided in parentheses, the user corresponding to the node with the previous serial number accesses, the user corresponding to the node with the next serial number accesses, the user corresponding to the node with the previous serial number accesses the user corresponding to the node with the next serial number, and then fre (i 1, i 2) represents the access frequency of Net (i 1) to Net (i 2);
the function Focus () is a set of other nodes concerned by the node corresponding to the acquired input sequence number, and Focus (i) represents a set of other nodes concerned by the node with the sequence number of i;
the function Fans () is a set of other concerned nodes of the node corresponding to the acquired input sequence number, and Fans (i) represents a set of other concerned nodes of the node with the sequence number i;
the attribution element of the node is defined as an array consisting of the degree of departure of the node, the degree of entrance of the node, the access frequency of the node for accessing other nodes, the access frequency of other nodes for accessing the node, the set of other nodes concerned by the node and the set of other nodes concerned by the node.
Further, in S300, the method for performing mobile search in the relational network and calculating the individual entanglement value according to the attribution factor of each node specifically includes:
s301, respectively calculating each node Net (i) in the relational network, acquiring Focus (i) corresponding to each Net (i), acquiring Fans (i) corresponding to each Net (i), acquiring ex (i) corresponding to each Net (i), and acquiring in (i) corresponding to each Net (i);
s302, calculating the access frequency of Net (i) to each node in Focus (i) and accumulating and summing to obtain freFocus (i); calculating the access frequency of each node in the Fans (i) to the corresponding Net (i) and performing accumulation and summation to obtain freFans (i);
s303, for the node Net (i) with sequence number i, determining whether the corresponding Focus (i) is an empty set or Fans (i) is an empty set, setting a label value for each node, and calculating a shunt value for each node, specifically:
the upper shunt value corresponding to the node Net (i) with the sequence number i is recorded as up (i);
if the condition that only the corresponding Focus (i) is satisfied is that the corresponding Focus (i) is an empty set, setting the marking value of the node Net (i) to be 0, and going to S304;
if only the corresponding Fans (i) is satisfied as the empty set, setting the marking value of the node Net (i) to be 1, and going to S305;
if the condition that the corresponding Focus (i) is an empty set and the corresponding Fans (i) is also an empty set is met, setting the marking value of the node Net (i) to be-1, and going to S306;
if the condition that the corresponding Focus (i) is not an empty set and the corresponding Fans (i) is not an empty set is met at the same time, turning to S307 to set the marking value of the node Net (i);
s304, calculating and recording and storing the shunt value of the node Net (i) with the sequence number i by using a first formula, wherein the calculation method of the first formula is as follows:
go to S308;
s305, calculating and obtaining the shunt value of the node Net (i) with the sequence number i by using a second formula, and recording and storing the shunt value, wherein the calculation method of the second formula is as follows:
go to S308;
s306, calculating by using a third formula to obtain the shunt value of the node Net (i) with the sequence number i, recording and storing, wherein the calculation method of the third formula is as follows:
go to S308;
s307, a fourth formula is used for calculating, recording and storing the shunt value of the node Net (i) with the sequence number i, wherein the calculation method of the fourth formula is as follows:
assigning the value of up (i) to the labeled value of Net (i), and going to S308;
s308, mobile search is carried out in the relation network: judging whether the set composed of the nodes with the index value of 0 is empty, if so, turning to S309, otherwise, taking the set composed of the nodes with the index value of 0 as an initial set and turning to S312;
s309, judging whether a set composed of nodes with the index value of 1 is empty, if so, turning to S310, and if not, taking the set composed of the nodes with the index value of 1 as an initial set and turning to S312;
s310, judging whether a set formed by nodes with the mark value of-1 is empty, if so, turning to S311, otherwise, taking the set formed by the nodes with the mark value of-1 as an initial set, and turning to S312;
s311, taking a set composed of nodes of which the shunt values in the relational network do not exceed the arithmetic mean of all the node shunt values of the relational network as an initial set and transferring to S312;
s312, selecting a node with the minimum shunt value in the initial set as an initial node, recording the serial number of the initial node in the relational network as S, wherein the initial node is Net (S), the shunt value of the Net (S) is up (S), respectively calculating the ratio of up (S) to the shunt values of other nodes in the initial set, taking the arithmetic mean of the ratios of up (S) to the shunt values of other nodes in the initial set, and taking the arithmetic mean as an individual entanglement value.
Further, in S400, the method for screening out the target group according to the individual entanglement value specifically includes:
in the relational network, acquiring the shunt value of each node, and calculating the ratio of the shunt value of each node to the shunt values of other nodes in the relational network for each node;
setting a set Divg to collect nodes with abnormal conditions, and enabling an initial value of the Divg to be an empty set;
furthermore, according to the individual entanglement values, if the mode or the arithmetic mean of the ratios of the shunt value of one node to the shunt values of other nodes in the relational network is greater than or equal to the individual entanglement value in numerical comparison, taking the node as a node with abnormal conditions, and adding the node with abnormal conditions into the set Divg;
and taking the nodes in the set Divg as target groups.
Further, in S500, according to the target group, the method for performing transmission control on the individuals in the relationship network includes:
for the individuals in the target group, when the individuals in the target group want to send messages to other individuals in the relationship network, the individuals needing to be sent messages need to confirm whether to accept the messages sent by the individuals.
The individual transmission control system based on the relation network comprises: the processor executes the computer program to implement the steps in the embodiment of the transmission control method for individuals based on a relational network, and the transmission control system for individuals based on a relational network may be run in computing devices such as a desktop computer, a notebook computer, a palm computer, a cloud data center, and the like, and the executable system may include, but is not limited to, a processor, a memory, and a server cluster.
As shown in fig. 2, the transmission management and control system for individuals based on a relational network according to an embodiment of the present invention includes: a processor, a memory and a computer program stored in the memory and operable on the processor, the processor implementing the steps in an embodiment of the method for transmission control of individuals over a relational network as described above when executing the computer program, the processor executing the computer program to run in the elements of the following system:
the network construction unit is used for acquiring each individual and constructing a relational network by taking each individual as a node;
an attribution element acquiring unit, configured to acquire an attribution element of each node in the relational network;
the mobile search unit is used for carrying out mobile search in the relational network according to the attribution elements of all the nodes and calculating individual entanglement values;
the screening target group unit is used for screening out a target group according to the individual entanglement value;
and the transmission control unit is used for carrying out transmission control on the individuals in the relational network according to the target group.
The individual transmission management and control system based on the relational network can be operated in computing equipment such as desktop computers, notebook computers, palm computers, cloud data centers and the like. The individual transmission control system based on the relational network comprises a processor and a memory, but not limited to the processor and the memory. It will be understood by those skilled in the art that the example is only an example of the individual transmission control method and system based on the relationship network, and does not constitute a limitation to the individual transmission control method and system based on the relationship network, and may include more or less components than the individual transmission control method and system based on the relationship network, or combine some components, or different components, for example, the individual transmission control system based on the relationship network may further include an input and output device, a network access device, a bus, and the like.
The Processor may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other Programmable logic device, discrete component Gate or transistor logic, discrete hardware components, etc. The general purpose processor may be a microprocessor, or the processor may be any conventional processor, and the processor is a control center of the individual transmission control system based on the relational network, and various interfaces and lines are used to connect the respective sub-areas of the entire individual transmission control system based on the relational network.
The memory can be used for storing the computer program and/or the module, and the processor realizes various functions of the individual transmission control method and system based on the relationship network by running or executing the computer program and/or the module stored in the memory and calling the data stored in the memory. The memory may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required by at least one function (such as a sound playing function, an image playing function, etc.), and the like; the storage data area may store data (such as audio data, a phonebook, etc.) created according to the use of the cellular phone, and the like. In addition, the memory may include high speed random access memory, and may also include non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), at least one magnetic disk storage device, a Flash memory device, or other volatile solid state storage device.
The invention provides a method and a system for transmission and control of individuals based on a relational network, wherein each individual is used as a node to construct the relational network, the attribution element of each node in the relational network is obtained, mobile search is carried out in the relational network according to the attribution element of each node, the entanglement value of the individuals is calculated, a target group is screened out, and the individuals in the relational network are transmitted and controlled by the target group, so that the analysis of data characteristics of mutual attention and access among users is realized, the message control is carried out according to the association degree among the individuals, and the beneficial effect of effectively reducing harassment information is achieved.
Although the present invention has been described in considerable detail and with reference to certain illustrated embodiments, it is not intended to be limited to any such details or embodiments or any particular embodiment, so as to effectively encompass the intended scope of the invention. Furthermore, the foregoing describes the invention in terms of embodiments foreseen by the inventor for which an enabling description was available, notwithstanding that insubstantial modifications of the invention, not presently foreseen, may nonetheless represent equivalent modifications thereto.
Claims (3)
1. A method for individual transmission control based on a relational network, the method comprising the steps of:
s100, acquiring each individual, and constructing a relationship network by taking each individual as a node;
s200, acquiring the attribution elements of each node in the relational network;
s300, performing mobile search in a relational network according to the attribution elements of each node, and calculating an individual entanglement value;
s400, screening out a target group according to the individual entanglement values;
s500, carrying out transmission control on individuals in the relational network according to the target group;
in S100, the method of acquiring each individual and constructing a relationship network using each individual as a node includes:
acquiring data of a plurality of registered different users from a social network site or social software, wherein the individuals are users registered on the social network site or the social software, and the data of the users comprise: the user identification, the identification of other users concerned by the user and the access frequency of other users concerned by the user, wherein the identification is a numerical value, the identification is the unique identification of the user, and the users are concerned with each other;
according to the data of the user, the data of different users are respectively used as different nodes, the nodes correspond to the users, and the nodes have the corresponding user identity marks;
the access frequency is the arithmetic mean of the number of times one user accesses another user within each day of the week;
for each node, respectively making a directed edge from the node to each node concerned by the node, wherein the direction of the directed edge is from the node concerned to the concerned node, and a pair of parallel directed edges with opposite directions exist between two nodes concerned with each other, so that the nodes are connected to form a relationship network by making the directed edges and establishing the edges between the nodes;
in S200, the method for acquiring the attribution element of each node in the relational network includes:
the out degree of one node represents the number of directed edges starting from the one node, and the in degree of the one node represents the number of directed edges pointing to the one node;
recording the number of nodes in the relation network as n, recording the serial number of the nodes in the relation network as i, wherein i belongs to [1, n ], and the serial number of each node in the relation network is n different constants in [1, n ];
in the relational network, a node with a sequence number i is recorded as Net (i), and the numerical value of the sequence number i is the identity of the Net (i);
the function in () represents the degree of entry of a node with a corresponding sequence number obtained by inputting the sequence number of the node in the parenthesis of the function, and in (i) represents the degree of entry of a node with a sequence number i in the relational network;
the function ex () represents the degree of departure of the node with the corresponding sequence number by inputting the sequence number of the node in the parenthesis of the function, and then ex (i) represents the degree of departure of the node with the sequence number i in the relational network;
recording i1 and i2 as two variables with unequal numerical values in [1, n ], respectively representing the serial numbers of two different nodes in the relational network by i1 and i2, acquiring the node Net (i 1) with the serial number of i1 in the relational network and acquiring the node Net (i 2) with the serial number of i2 in the relational network;
the function fre () is a function for acquiring the access frequency of a user represented by one node to a user represented by another node, wherein the function fre () is used for inputting the serial numbers of two different nodes into the function, the input serial numbers of the two different nodes are sequentially divided in brackets, the user corresponding to the node with the previous serial number accesses, the user corresponding to the node with the subsequent serial number accesses, the user corresponding to the node with the previous serial number accesses the user corresponding to the node with the subsequent serial number, and then fre (i 1, i 2) represents the access frequency of Net (i 1) to Net (i 2);
the function Focus () is a set of other nodes concerned by the node corresponding to the acquired input serial number, and Focus (i) represents a set of other nodes concerned by the node with the serial number i;
the function Fans () is a set of other concerned nodes of the node corresponding to the acquired input sequence number, and Fans (i) represents a set of other concerned nodes of the node with the sequence number i;
defining the attribution element of the node as an array consisting of the degree of departure of the node, the degree of entrance of the node, the access frequency of the node for accessing other nodes, the access frequency of other nodes for accessing the node, the set of other nodes concerned by the node and the set of other nodes concerned by the node;
in S300, the method for performing mobile search in the relational network and calculating the individual entanglement value according to the attribution factor of each node specifically includes:
s301, obtaining Focus (i) corresponding to each Net (i), fans (i) corresponding to each Net (i), ex (i) corresponding to each Net (i) and in (i) corresponding to each Net (i) respectively for each node Net (i) in the relational network;
s302, calculating the access frequency of each node in the Focus (i) and performing accumulation and summation to obtain the freFocus (i); calculating the access frequency of each node in the Fans (i) to the corresponding Net (i) and performing accumulation and summation to obtain freFans (i);
s303, for a node Net (i) with a sequence number i, determining whether the corresponding Focus (i) is an empty set or Fans (i) is an empty set, setting a mark value for each node, and calculating a shunt value for each node, specifically:
the shunt value corresponding to the node Net (i) with the sequence number i is recorded as up (i);
if only the corresponding Focus (i) is satisfied as an empty set, setting the marking value of the node Net (i) to be 0, and going to S304;
if only the corresponding Fans (i) is satisfied as the empty set, setting the marking value of the node Net (i) to be 1, and going to S305;
if the condition that the corresponding Focus (i) is an empty set and the corresponding Fans (i) is also an empty set is met, setting the marking value of the node Net (i) to be-1, and going to S306;
if the condition that the corresponding Focus (i) is not an empty set and the corresponding Fans (i) is not an empty set is met at the same time, turning to S307 to set the marking value of the node Net (i);
s304, calculating and recording and storing the shunt value of the node Net (i) with the sequence number i by using a first formula, wherein an exponential function with a natural constant e as a base is calculated by exp (), and the calculation method of the first formula is as follows:
go to S308;
s305, calculating and obtaining the shunt value of the node Net (i) with the sequence number i by using a second formula, and recording and storing the shunt value, wherein the calculation method of the second formula is as follows:
go to S308;
s306, calculating and recording and storing the shunt value of the node Net (i) with the sequence number i by using a third formula, wherein the calculation method of the third formula is as follows:
go to S308;
s307, a fourth formula is used for calculating, recording and storing the shunt value of the node Net (i) with the sequence number i, wherein the fourth formula is calculated by the following method:
assigning the value of up (i) to the marking value of Net (i), and going to S308;
s308, mobile search is carried out in the relation network: judging whether the set composed of the nodes with the index value of 0 is empty, if so, turning to S309, otherwise, taking the set composed of the nodes with the index value of 0 as an initial set and turning to S312;
s309, judging whether a set composed of nodes with the index value of 1 is empty, if so, turning to S310, and if not, taking the set composed of the nodes with the index value of 1 as an initial set and turning to S312;
s310, judging whether a set formed by nodes with the mark value of-1 is empty, if so, turning to S311, otherwise, taking the set formed by the nodes with the mark value of-1 as an initial set and turning to S312;
s311, taking a set composed of nodes of which the shunt values in the relational network do not exceed the arithmetic mean of all the node shunt values of the relational network as an initial set and transferring to S312;
s312, selecting a node with the minimum shunt value in the initial set as an initial node, recording the serial number of the initial node in the relational network as S, wherein the initial node is Net (S), the shunt value of the Net (S) is up (S), respectively calculating the ratio of up (S) to the shunt values of other nodes in the initial set, taking the arithmetic mean of the ratios of up (S) to the shunt values of other nodes in the initial set, and taking the arithmetic mean as an individual entanglement value.
2. The method for transmission and control of individuals based on the relational network according to claim 1, wherein in S400, the method for screening out the target group according to the entanglement values of the individuals specifically comprises:
in the relational network, acquiring the shunt value of each node, and calculating the ratio of the shunt value of each node to the shunt values of other nodes in the relational network for each node;
setting a set Divg to collect nodes with abnormal conditions, and enabling an initial value of the Divg to be an empty set;
further, it is determined whether each node has an abnormal condition, specifically: if the mode of the ratio of the shunt value of one node to the shunt values of other nodes in the relational network is greater than or equal to the individual entanglement value, taking the node as the node with the abnormal condition, and adding the node with the abnormal condition into the set Divg;
and taking the nodes in the set Divg as a target group.
3. The method of claim 1, wherein in S500, the method for performing transmission control on the individuals in the relational network according to the target group comprises:
for the individuals in the target group, when the individuals in the target group want to send messages to other individuals in the relationship network, the individuals needing to be sent messages need to confirm whether to accept the information sent by the individuals.
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