CN108400883B - A Realization Method of Social Hierarchy Model Based on Network Information Flow - Google Patents
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Abstract
一种基于网络信息流的社会分层模型实现方法，包括以下步骤：S1：导入网络；S2：选取智慧节点，假设整个网络有且仅有1个智慧节点；S3：初始化权重，在网络中每条有向连边权重都分配一个固定常数；S4：通过基于级联信息传播真假消息，遍历网络中每个普通节点，循环多次，最终统计每个普通节点作为源节点时整个网络转发真假消息的平均数量；S5：计算网络社会分层模型指标，包括真消息信息扩散能力差异值和假消息信息扩散能力差异值。当社会网络存在智慧节点时，真假消息传播数量在整个网络中会出现社会分层现象。本发明提供一种基于网络信息流的社会分层模型实现方法。该模型能帮助研究人员更好地了解网络中社会分层现象出现的机制，以及智慧节点的作用。
A method for realizing a social stratification model based on network information flow, comprising the following steps: S1: importing the network; S2: selecting a smart node, assuming that there is only one smart node in the entire network; S3: initializing the weight, in the network each Each directed edge weight is assigned a fixed constant; S4: By propagating true and false news based on cascading information, traversing each common node in the network, looping multiple times, and finally counting each common node as a source node The entire network forwards true The average number of fake news; S5: Calculate the index of the network social stratification model, including the difference value of the information diffusion ability of the true news and the difference value of the information diffusion ability of the fake news. When there are smart nodes in the social network, the number of true and false news spreads will appear social stratification in the entire network. The invention provides a method for realizing a social stratification model based on network information flow. This model can help researchers better understand the mechanism of social stratification in the network and the role of intelligent nodes.
Description
Technical Field
The invention relates to the field of network information propagation, in particular to a social layering model implementation method based on network information flow.
Background
With the rapid development of computer communication technology and web 2.0, the way people interact is greatly changed compared with the traditional way of communication. In the traditional society, information exchange of people is limited by a plurality of factors such as time, space and the like, and the appearance of an online social network provides people with a virtual social platform which can realize unlimited interaction only by a computer and a network. Users are not only receivers of information, but also producers and distributors of information. The appearance of social media also changes the traditional information diffusion mode, and a huge interpersonal information transmission network is established when people exchange information through the social network. The change of information transmission paths brings a revolution to individual information transmission behaviors.
In general, social stratification is the relative social status of individuals in a social group, primarily based on their occupation and income, wealth and social status, or derived powers.
Disclosure of Invention
In order to overcome the defect that the existing social network does not consider social layering, the social layering phenomenon in sociology is migrated to the social network information transmission according to the occurrence of the social layering phenomenon in sociology, the invention provides a social layering model implementation method based on network information flow, so that the social layering phenomenon similar to the social layering phenomenon in sociology appears in the true and false information transmission of the social network.
The technical scheme adopted by the invention for solving the technical problems is as follows:
a social layering model implementation method based on intelligent social network information flow comprises the following steps:
s1: the import network set G ═ (V, E, W), and its node set and connected edge set are V ═ { V, respectively1,v2,...,vnAnd
a weight set W and a total number N of nodes;
s2: selecting an intelligent node; labeling network nodes, setting the nodes with the label 1 as intelligent nodes, and assuming that the whole network has only 1 intelligent node for researching the training process of the learning method;
s3: initializing the weight; each directed connecting edge in the network is from vjDirection vkAre assigned a fixed constant of 0.5, i.e., wjk0.5. Weight wjkDenotes vkFor vjThe degree of trust of;
s4: and (3) cascading information propagation: and traversing each node of the network in the order of the labels as a source node for information propagation, and issuing true and false messages. If the source node vjFor intelligent nodes, only true messages are published. If the source node vjAnd the message or the false message is issued with equal probability for the common node. When a node vkNode v, observing messages from its neighbor nodeskOne of the neighboring neighbor nodes will be chosen at first randomly, denoted as vjThen the cascade information propagation model is as follows:
if v iskIs an intelligent node, if it is a true message, it forwards the message with probability p ═ η, otherwise refuse to issue;
if v iskIs a common node and has the probability p ═ η · w that the message is forwardedjkWhether it is true or false. Eta of 0 to 1 is the natural forwarding rate;
each node has only one opportunity to propagate, and the node which can see the information in the network stops after all the nodes are forwarded and inquired, and the propagation process is recorded as 1 time. After stopping, respectively counting the quantity of forwarding true messages and false messages in the whole network when each node is used as a source node;
s5: calculating indexes of the network social layering model; after the transmission of true and false messages in the whole intelligent social network is completed, by counting the number of forwarded true and false messages, the indexes of the network social hierarchical model can be calculated and are respectively represented by DT(i) And DF(i) And (4) showing. Then, we define the true message information diffusion capability difference value D of the ith nodeT(i) And false message information diffusion capability difference value DF(i) The following are:
DT(i)＝nT(i)-nT(i+1)，(i≥2)(1)
DF(i)＝nF(i)-nF(i+1)，(i≥2)(2)
wherein n isT(i) And nF(i) Respectively representing the number of true and false messages forwarded in the whole network when the ith node is taken as a source node;
on the basis, the difference value D of the diffusion capability is spread according to the true message informationT(i) And false message information diffusion capability difference value DF(i) The social layering phenomenon of information flow in the whole intelligent social network can be observed.
Further, the information cascade model in the step S4The node set which has forwarded information in the network is marked as VreachedInitially, only information source nodes are contained; if the neighbor node of the node belongs to VreachedAnd the neighbor nodes of the node are marked as V without forwarding inquirynoworked(ii) a If the neighbor node of the node belongs to VreachedAnd has made forwarding inquiries, the neighbor nodes of the node are marked as Vworked(ii) a After the neighbor node of a certain node k forwards information, the node k can see the information, and the node k forwards the information according to the probability p; setting total cycle times M for each node serving as a source node to issue true and false messages; the process is as follows:
4.1) selecting a common node j in the network as a source node of information propagation, and respectively issuing true and false messages;
4.2) selecting any one of VnoworkedNode k in (1), generates [0,1 ] according to the network information propagation model]If node k is a smart node, if this is a true message and p' ≦ η, the probability that node k will forward the message, otherwise publication is denied. If node k is a normal node and p' ≦ η · wjkThe probability that node k will forward the message regardless of whether the information is true or false. Eta of 0 to 1 is the natural forwarding rate;
4.3)4.2), if node k forwards the information, add to V the neighbor nodes of node knoworkedIn (3), remove node k out of VnoworkedIs added to Vreached(ii) a If node k does not forward, node k enters Vworked；
4.4) when VnoworkedWhen no node exists, the current cycle stops, which indicates that all nodes seeing the information are processed; vreachedI.e., representing the set of nodes propagated to;
4.5) separately counting the number of forwarded accumulated true messages to nT(j) And number of bogus message forwards to nF(j)。
4.6) continuously repeating the steps 4.1) -4.5) until the total cycle number M is reached, stopping propagation, and calculating the average value of the true message forwarding number of the final common nodeMean value
And average of the number of bogus message hops
4.7) traversing each common node of the network, repeating 4.1) -4.6), and finally obtaining the average value of the number of true messages and the number of false messages forwarded by the whole network when the common nodes are used as source nodes.
The invention has the beneficial effects that: information exists in only two states: true message, false message. We propose an information propagation model: the intelligent nodes in the network are considered to have certain capability to distinguish the two different information, and the intelligent nodes can propagate true messages according to a certain mode and prevent false messages. Meanwhile, a social layering concept based on network information flow is provided, and when intelligent nodes exist in the social network, the social layering phenomenon can occur in the whole social network due to the propagation quantity of true and false messages. Based on the method, a social layering model implementation method based on intelligent social network information flow is provided, and through combination with simulation experiments, the model can help researchers to better understand the mechanism of social layering in the intelligent social network and the effect of intelligent nodes.
Drawings
FIG. 1 is an algorithm flow chart of a social hierarchical model implementation method based on network information flow.
FIG. 2 is a diagram of the difference D between different eta values and true message information diffusion capability in the chain network according to the present inventionT(i) And (5) a variation graph.
FIG. 3 is a diagram of the difference D between different eta values and the pseudo message information diffusion capability in the chain network according to the present inventionF(i) And (5) a variation graph.
FIG. 4 is a diagram of the difference D between different eta values and true message information diffusion capability in a double-chain network according to the present inventionT(i) And (5) a variation graph.
FIG. 5 shows the difference of the false message information diffusion capability at different eta values of the double-chain network according to the present inventionValue DF(i) And (5) a variation graph.
Detailed Description
The following detailed description of embodiments of the invention is provided in connection with the accompanying drawings.
Referring to fig. 1 to 5, the invention discloses a social layering model implementation method based on network information flow, and selects a chain network and a double-chain network as experimental networks. The network is simple in structure, but has the representative effect of helping researchers to better understand the intelligent nodes and understand the social layering phenomenon based on the intelligent social network information flow. Setting the number of chain network nodes N chain10, cycle number Mchain10000 ═ 10000; the double-chain network is formed by randomly selecting two nodes to be connected by two chain networks, and the cycle number is 10000 times; propagation probability η ═ 0.3,0.5, 0.7,0.9]。
In this embodiment, a social layering model implementation method based on an intelligent social network includes the following specific steps:
s1: the import network set G ═ (V, E, W), and its node set and connected edge set are V ═ { V, respectively1,v2,...,vnAnd
a weight set W and a total number N of nodes;
s2: selecting an intelligent node; labeling network nodes, setting the nodes with the label 1 as intelligent nodes, and assuming that the whole network has only 1 intelligent node for researching the training process of the learning method;
s3: initializing the weight; each directed connecting edge in the network is from vjDirection vkAre assigned a fixed constant of 0.5, i.e., wjk0.5. Weight wjkDenotes vkFor vjThe degree of trust of;
s4: and (3) cascading information propagation: and traversing each node of the network in the order of the labels as a source node for information propagation, and issuing true and false messages. If the source node vjFor intelligent nodes, only true messages are published. If the source node vjIs a common node toEqual probability distribution messages or false messages. When a node vkNode v, observing messages from its neighbor nodeskOne of the neighboring neighbor nodes will be chosen at first randomly, denoted as vjThen the cascade information propagation model is as follows:
if v iskIs an intelligent node, if it is a true message, it forwards the message with probability p ═ η, otherwise refuse to issue;
if v iskIs a common node and has the probability p ═ η · w that the message is forwardedjkWhether it is true or false. Eta of 0 to 1 is the natural forwarding rate;
each node has only one opportunity to propagate, and the node which can see the information in the network stops after all the nodes are forwarded and inquired, and the propagation process is recorded as 1 time. After stopping, respectively counting the quantity of forwarding true messages and false messages in the whole network when each node is used as a source node;
s5: calculating indexes of the network social layering model; after the transmission of true and false messages in the whole intelligent social network is completed, by counting the number of forwarded true and false messages, the indexes of the network social hierarchical model can be calculated and are respectively represented by DT(i) And DF(i) And (4) showing. Then, we define the true message information diffusion capability difference value D of the ith nodeT(i) And false message information diffusion capability difference value DF(i) The following are:
DT(i)＝nT(i)-nT(i+1)，(i≥2) (1)
DF(i)＝nF(i)-nF(i+1)，(i≥2) (2)
wherein n isT(i) And nF(i) Respectively representing the number of true and false messages forwarded in the whole network when the ith node is taken as a source node;
on the basis, the difference value D of the diffusion capability is spread according to the true message informationT(i) And false message information diffusion capability difference value DF(i) The social layering phenomenon of information flow in the whole intelligent social network can be observed.
Further, in the information concatenation model in step S4, the set of nodes that have forwarded information in the network is denoted as VreachedInitially, only information source nodes are contained; if the neighbor node of the node belongs to VreachedAnd the neighbor nodes of the node are marked as V without forwarding inquirynoworked(ii) a If the neighbor node of the node belongs to VreachedAnd has made forwarding inquiries, the neighbor nodes of the node are marked as Vworked(ii) a After the neighbor node of a certain node k forwards the information, the node can see the information, and the node k forwards the information according to the probability p; setting total cycle times M for each node serving as a source node to issue true and false messages; the process is as follows:
4.1) selecting a common node j in the network as a source node of information propagation, and respectively issuing true and false messages;
4.2) selecting any one of VnoworkedNode k in (1), generates [0,1 ] according to the network information propagation model]If node k is a smart node, if this is a true message and p' ≦ η, the probability that node k will forward the message, otherwise publication is denied. If node k is a normal node and p' ≦ η · wjkThe probability that node k will forward the message regardless of whether the received information is true or false. Eta of 0 to 1 is the natural forwarding rate;
4.3)4.2), if node k forwards the information, add to V the neighbor nodes of node knoworkedIn (3), remove node k out of VnoworkedIs added to Vreached(ii) a If node k does not forward, node k enters Vworked；
4.4) when VnoworkedWhen no node exists, the current cycle stops, which indicates that all nodes seeing the information are processed; vreachedI.e., representing the set of nodes propagated to;
4.5) respectively counting the number n of forwarded accumulated true messagesT(j) And the number n of bogus message forwardingsF(j)；
4.6) repeating the steps 4.1) -4.5) continuously until the total cycle number M is reached and the propagation stopsAnd finally, calculating the average value of the number of true message forwarding of the final common node
And average of the number of bogus message hops
4.7) traversing each common node of the network, repeating 4.1) -4.6), and finally obtaining the average value of the number of true messages and the number of false messages forwarded by the whole network when the common nodes are used as source nodes.
FIG. 2 shows that when 0 < eta.ltoreq.1, all have DT(i) Is greater than 0. The method shows a social layering phenomenon, from the intelligent node to the terminal node, the common nodes are close to the intelligent node and have higher probability of transmitting real information, namely the real information can be forwarded by more other common nodes. FIG. 3 shows that
When D isF(i) Is greater than 0; when in use
When D isF(i) Is less than 0. In other words, a common node v in the middle of the chainm，
Has the highest capability of propagating error information, and the capability is steadily reduced from the middle to the terminal node, which is less influenced by the intelligent node.
In the double-chain network experiment, a node No. 4 (node No. h) in a chain network A and a node No. 8 (node No. l) in a chain network B are randomly selected to form a double-chain network, and fig. 4 shows that in the chain network A, the social layering phenomenon of the node No. 1 to the node No. 4 in the chain network A is weakened compared with the social layering phenomenon in the chain network, and the social layering phenomenon of the node No. 4 to the node 10 in the chain network A is enhanced; FIG. 5 shows that, in the chain network A, when
Or
When compared with the social layering phenomenon in the chain network, the social layering phenomenon is strengthened
In time, the social layering phenomenon is weakened compared with that in a chain network; double-stranded this phenomenon we define as a cross-border dominance.
As described above for the embodiment introduction of the present invention on the chain network and the double-chain network, we propose a social layering concept based on network information flow, and when there are intelligent nodes in the social network, the social layering phenomenon occurs in the whole social network due to the propagation number of true and false messages. Based on the method, a social layering model implementation method based on intelligent social network information flow is provided, and through combination with simulation experiments, the model can help researchers to better understand the mechanism of social layering in the intelligent social network and the effect of intelligent nodes. The present invention is to be considered as illustrative and not restrictive. It will be understood by those skilled in the art that various changes, modifications and equivalents may be made therein without departing from the spirit and scope of the invention as defined in the appended claims.
Claims (2)
1. A social layering model implementation method based on network information flow is characterized by comprising the following steps:
s1: the import network set G ═ (V, E, W), and its node set and connected edge set are V ═ { V, respectively1,v2,...,vnAnd
a weight set W and a total number N of nodes;
s2: selecting an intelligent node; labeling network nodes, setting the nodes with the label 1 as intelligent nodes, and assuming that the whole network has only 1 intelligent node for researching the training process of the learning method;
s3: initializing the weight; each directed connecting edge in the network is from vjDirection vkAre assigned a fixed constant of 0.5, i.e., wjk0.5; weight wjkDenotes vkFor vjThe degree of trust of;
s4: propagating the cascade information; traversing each node of the network in the order of the label as a source node of information propagation, and issuing true and false messages; if the source node vjOnly true messages are issued for intelligent nodes; if the source node vjThe node is a common node and issues true messages or false messages with equal probability; when a node vkUpon receiving a message from its neighbor node, node vkOne of the neighboring neighbor nodes will be chosen at first randomly, denoted as vjThen the cascade information propagation model is as follows:
if v iskIs an intelligent node, if the message that the node can receive is a true message, the node vkThe probability p of the message forwarding is equal to eta, otherwise, the message is refused to be issued;
if v iskIs a common node, node vkThe probability p of forwarding the message is eta wjkWhether the message is true or false; eta is the natural forwarding rate, eta is more than or equal to 0 and less than or equal to 1;
each node has only one opportunity transmission, if the transmission is unsuccessful, the transmission is not continued, and when all the nodes which can receive the message in the network are forwarded and inquired, the transmission is stopped and is recorded as a transmission process for 1 time; after stopping, respectively counting the quantity of forwarding true messages and false messages in the whole network when each node is used as a source node;
s5: calculating indexes of the network social layering model; after the transmission of true and false messages in the whole intelligent social network is completed, calculating the indexes of the network social hierarchical model by counting the number of the forwarded true and false messages, wherein the indexes are respectively represented by DT(i) And DF(i) Represents; then, defining the true message information diffusion capability difference value D of the ith nodeT(i) And fake message information diffusion capabilityDifference value DF(i) The following are:
DT(i)＝nT(i)-nT(i+1)，(i≥2) (1)
DF(i)＝nF(i)-nF(i+1)，(i≥2) (2)
wherein n isT(i) And nF(i) Respectively representing the number of true and false messages forwarded in the whole network when the ith node is taken as a source node;
on the basis, the difference value D of the diffusion capability is spread according to the true message informationT(i) And false message information diffusion capability difference value DF(i) The social layering phenomenon of information flow in the whole intelligent social network can be observed.
2. The method as claimed in claim 1, wherein the step S4 is implemented by using a cascading information propagation model, where a set of nodes that have forwarded a message in the network is denoted as VreachedInitially, only information source nodes are contained; if the neighbor node of the node belongs to VreachedAnd the neighbor node does not make forwarding inquiry, and the neighbor node of the node is marked as Vnoworked(ii) a If the neighbor node of the node belongs to VreachedAnd the neighbor node has made forwarding inquiry, the neighbor node of the node is marked as Vworked(ii) a After the neighbor node of a certain node k forwards the message, the node k can receive the message, and simultaneously the node k forwards the message according to the probability p; setting total cycle times M for each node serving as a source node to issue true and false messages; the process is as follows:
4.1) selecting a common node j in the network as a source node of information propagation, and respectively issuing true and false messages;
4.2) selecting any one of VnoworkedNode k in (1), generates [0,1 ] according to the network information propagation model]If the node k is an intelligent node, if the message received by the node k is a true message and p ' is less than or equal to η, the probability p ' that the node k forwards the message is p ', otherwise, the message is rejected; if node k is a normal node and p' ≦ η · wjkThe probability p ═ p' that node k forwards the message, regardless of whether the message is true or false; eta is the natural forwarding rate, eta is more than or equal to 0 and less than or equal to 1;
4.3)4.2), if node k forwards the message, add to V in the neighbor node of node knoworkedIn (3), remove node k out of VnoworkedIs added to Vreached(ii) a If node k does not forward, node k enters Vworked；
4.4) when VnoworkedWhen there is no node, the current cycle stops, which indicates that all nodes receiving the message have been processed; vreachedI.e., representing the set of nodes propagated to;
4.5) separately counting the number of forwarded accumulated true messages to nT(j) And number of bogus message forwards to nF(j)；
4.6) continuously repeating the steps 4.1) -4.5) until the total cycle number M is reached, stopping the propagation, and calculating the average value of the true message forwarding number of the final common node
And average of the number of bogus message hops
4.7) traversing each common node of the network, repeating 4.1) -4.6), and finally obtaining the average value of the number of true messages and the number of false messages forwarded by the whole network when the common nodes are used as source nodes.
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