Information propagation path determination method in group intelligent scene and related equipment

Abstract

The invention discloses a method for determining an information propagation path in a swarm intelligence scene and related equipment, and relates to the technical field of network science, and the method comprises the steps: obtaining a target network which comprises a plurality of nodes and a plurality of links used for connecting the nodes, and the nodes comprise a first type of nodes and a second type of nodes; determining the node similarity corresponding to each node, wherein the node similarity corresponding to one node is used for representing the similarity between the node and all the first type of nodes; determining a link importance degree corresponding to each link; for each node in the target network, determining an information propagation importance degree between the node and each adjacent node according to the node similarity corresponding to the node and a link importance degree of a link between the node and the adjacent node; and determining an information propagation path from the first type of nodes to all the second type of nodes according to the information propagation importance. Therefore, the required communication resources can be reduced, and the communication cost in the information spreading process can be reduced.

Description

Technical Field

[0001] The present application relates to the field of network science and technology, and in particular to a method for determining an information propagation path in a group intelligence scenario and related equipment. Background Art

[0002] In the swarm intelligence scenario, for a network composed of individuals as nodes, information needs to be propagated between individuals (i.e., nodes). At the same time, there may be individuals of different categories in the swarm intelligence scenario, and information needs to be propagated from one category of individuals to another.

[0003] In the related art, for nodes in the network in the swarm intelligence scenario, point-to-point direct communication is usually adopted. For example, if a node wants to spread information to multiple other target nodes, it needs to directly communicate with each target node separately, and it is required that the starting node of the information spread can communicate directly with each target node. The problem with the related art is that the point-to-point direct communication method requires a large amount of communication resources, which is not conducive to reducing the communication cost in the process of information dissemination.

[0004] Therefore, relevant technologies still need to be improved and developed. Summary of the invention

[0005] The main purpose of this application is to provide a method and related equipment for determining an information propagation path in a swarm intelligence scenario, aiming to solve the technical problem in the related technology that information propagation between nodes in a network in a swarm intelligence scenario requires a large amount of communication resources by adopting a point-to-point direct communication method, which is not conducive to reducing the communication cost in the information propagation process.

[0006] In order to achieve the above-mentioned object, the first aspect of the present application provides a method for determining an information propagation path in a swarm intelligence scenario, wherein the method for determining an information propagation path in a swarm intelligence scenario comprises:

[0007] Acquire a target network, wherein the target network includes a plurality of nodes and a plurality of links for connecting the nodes, and the nodes include first-type nodes and second-type nodes;

[0008] Determine the node similarity corresponding to each of the above nodes, wherein the node similarity corresponding to a node is used to represent the similarity between the above node and all the above first-category nodes;

[0009] Determine the link importance corresponding to each of the above links;

[0010] For each node in the target network, determine the importance of information propagation between the node and each adjacent node according to the node similarity corresponding to the node and the link importance of the link between the node and its adjacent nodes;

[0011] The information propagation path from the first type of node to all the second type of nodes is determined according to the information propagation importance.

[0012] Optionally, the first type of nodes are expert nodes, and the second type of nodes are non-expert nodes.

[0013] Optionally, the determining of the node similarity corresponding to each of the above nodes includes:

[0014] For each node, respectively calculate the basic similarity between the above node and each first-category node;

[0015] Based on the sum of the basic similarities between the node and each first-category node, the node similarity corresponding to the node is determined.

[0016] Optionally, for each node, respectively calculating the basic similarity between the node and each first-category node includes:

[0017] For each node in the target network, perform the following operations:

[0018] Obtain the walking direction vector corresponding to the above nodes through random walk;

[0019] The cosine similarity between the wandering vector corresponding to the node and the wandering vector corresponding to each first-category node is calculated respectively as the basic similarity between the node and each first-category node.

[0020] Optionally, the determining of the link importance corresponding to each of the above links includes:

[0021] Calculate the Kemeny constant value corresponding to the above target network;

[0022] For each link, remove the link from the target network to obtain a candidate network corresponding to the link, calculate the kemeny constant value corresponding to the candidate network, and determine the link importance corresponding to the link according to the kemeny constant value corresponding to the target network and the kemeny constant value corresponding to the candidate network.

[0023] Optionally, for each node in the target network, determining the importance of information propagation between the node and each adjacent node according to the node similarity corresponding to the node and the link importance of the link between the node and its adjacent nodes includes:

[0024] For each node in the target network, the logarithmic value of the node similarity corresponding to the node is calculated, and the logarithmic value is multiplied by the link importance of the link between the node and its adjacent nodes to obtain the information propagation importance between the node and the adjacent nodes.

[0025] Optionally, the target network is a connected network, and the determining of the information propagation path from the first type of node to all the second type of nodes according to the information propagation importance includes:

[0026] Initialize the information propagation path according to the first type of nodes mentioned above;

[0027] Take the first type of node as the starting node;

[0028] A second type node in the target network that is not included in the information propagation path is used as a termination node;

[0029] Select an adjacent node with the greatest importance of information dissemination to the above-mentioned starting node to add it to the above-mentioned information dissemination path, and use the selected adjacent node as the starting node;

[0030] Return to the step of selecting the adjacent node with the greatest importance of information propagation between the starting node and the starting node to add to the information propagation path, and taking the selected adjacent node as the starting node, until the ending node is added to the information propagation path;

[0031] Return to the step of using the first type of node as the starting node until all the second type of nodes are added to the information propagation path.

[0032] A second aspect of the present application provides a system for determining an information propagation path in a swarm intelligence scenario, wherein the system for determining an information propagation path in a swarm intelligence scenario includes:

[0033] A network acquisition module, used to acquire a target network, wherein the target network includes a plurality of nodes and a plurality of links for connecting the nodes, and the nodes include a first type of node and a second type of node;

[0034] A node similarity determination module, used to determine the node similarity corresponding to each of the above nodes, wherein the node similarity corresponding to a node is used to characterize the similarity between the above node and all the above first-category nodes;

[0035] A link importance determination module, used to determine the link importance corresponding to each of the above links;

[0036] An information transmission importance determination module, for each node in the target network, determining the information transmission importance between the node and each adjacent node according to the node similarity corresponding to the node and the link importance of the link between the node and its adjacent nodes;

[0037] The information propagation path determination module is used to determine the information propagation path from the above-mentioned first-category node to all the above-mentioned second-category nodes according to the above-mentioned information propagation importance.

[0038] The third aspect of the present application provides an intelligent terminal, which includes a memory, a processor, and a program for determining an information propagation path in a group intelligence scenario stored in the memory and executable on the processor. When the program for determining an information propagation path in a group intelligence scenario is executed by the processor, the steps of any one of the methods for determining an information propagation path in a group intelligence scenario are implemented.

[0039] A fourth aspect of the present application provides a computer-readable storage medium, on which is stored a program for determining an information propagation path in a group intelligence scenario. When the program for determining an information propagation path in a group intelligence scenario is executed by a processor, the steps of any one of the methods for determining an information propagation path in a group intelligence scenario are implemented.

[0040] As can be seen from the above, the method in the scheme of the present application includes: obtaining a target network, wherein the target network includes multiple nodes and multiple links for connecting the nodes, and the nodes include first-category nodes and second-category nodes; determining the node similarity corresponding to each of the above nodes, wherein the node similarity corresponding to a node is used to characterize the degree of similarity between the above node and all the above-mentioned first-category nodes; determining the link importance corresponding to each of the above links; for each node in the target network, determining the information propagation importance between the above node and each adjacent node according to the node similarity corresponding to the above node and the link importance of the links between the above node and its adjacent nodes; determining the information propagation path from the above-mentioned first-category node to all the above-mentioned second-category nodes according to the above-mentioned information propagation importance.

[0041] Compared with the prior art, the present application scheme does not adopt a point-to-point direct communication method when performing information propagation in the target network (specifically propagating information from the first type of node to the second type of node), but first determines the node similarity corresponding to the node and the link importance corresponding to the link in real time for the target network, and then determines the information propagation importance between the node and each adjacent node in real time, and then determines the information propagation path from the first type of node to the second type of node in the target network based on the information propagation importance. That is, the best propagation path can be determined, based on which the first type of node can propagate information to the second type of node, and the specific communication method can be direct communication or indirect communication through other nodes. In this way, it is not required that any two nodes in the target network can communicate directly. There is no need for the first type of node to directly communicate with each second type of node separately, which is conducive to reducing the required communication resources and reducing the communication cost during information propagation. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0043] Figure 1 It is a flowchart of a method for determining an information propagation path in a group intelligence scenario provided by an embodiment of the present application;

[0044] Figure 2 It is a specific flow chart of a method for determining an information propagation path in a group intelligence scenario provided by an embodiment of the present application;

[0045] Figure 3 This is a schematic diagram of the components of a system for determining an information propagation path in a group intelligence scenario provided by an embodiment of the present application;

[0046] Figure 4 It is a block diagram of the internal structure principle of a smart terminal provided in an embodiment of the present application. DETAILED DESCRIPTION

[0047] In the following description, specific details such as specific system structures and technologies are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present application. However, it should be clear to those skilled in the art that the present application may also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to prevent unnecessary details from obstructing the description of the present application.

[0048] In the following description, many specific details are set forth to facilitate a full understanding of the present application, but the present application may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present application. Therefore, the present application is not limited to the specific embodiments disclosed below.

[0049] In the swarm intelligence scenario, for a network composed of individuals as nodes, information needs to be propagated between individuals (i.e., nodes). At the same time, there may be individuals of different categories in the swarm intelligence scenario, and information needs to be propagated from one category of individuals to another. For example, it is necessary to propagate the information of expert nodes with higher knowledge levels to non-expert nodes with lower knowledge levels, so as to make better use of the information of experts. In some application scenarios, better use of expert information can improve the level of swarm intelligence, thereby improving the effectiveness of subsequent decision-making.

[0050] Therefore, it is necessary to consider how to propagate the information of individual experts to other individuals. That is, to realize information propagation between nodes in the target network (such as a social network). In the related art, for nodes in the network under the swarm intelligence scenario, a point-to-point direct communication method is usually adopted. For example, if a node wants to propagate information to multiple other target nodes, it needs to directly communicate with each target node in a point-to-point manner, and it is required that the starting node of the information propagation can communicate directly with each target node. The problem with the related art is that the point-to-point direct communication method requires a large amount of communication resources, which is not conducive to reducing the communication cost in the process of information propagation.

[0051] Social learning can significantly increase the speed and influence of information dissemination in information dissemination, but in real-world scenarios, if an individual disseminates information with all social partners, the cost is high. In fact, it is not necessary to send messages to all social partners, so that messages can be spread through multiple optimal paths, so that messages can be delivered to other individuals. Therefore, in the context of group intelligence with social learning, by finding the optimal dissemination path from experts to non-experts, it is possible to make the information of experts spread quickly, effectively and at low cost in the group.

[0052] In order to solve at least one of the above-mentioned technical problems, the present application provides a method for determining the information propagation path in a group intelligence scenario. Specifically, a target network is obtained, wherein the target network includes multiple nodes and multiple links for connecting the nodes, and the nodes include first-class nodes and second-class nodes; the node similarity corresponding to each of the above-mentioned nodes is determined, wherein the node similarity corresponding to a node is used to characterize the similarity between the above-mentioned node and all the above-mentioned first-class nodes; the link importance corresponding to each of the above-mentioned links is determined; for each node in the target network, the information propagation importance between the above-mentioned node and each adjacent node is determined according to the node similarity corresponding to the above-mentioned node and the link importance of the link between the above-mentioned node and its adjacent nodes; the information propagation path from the above-mentioned first-class node to all the above-mentioned second-class nodes is determined according to the above-mentioned information propagation importance.

[0053] Compared with the prior art, the present application scheme does not adopt a point-to-point direct communication method when performing information propagation in the target network (specifically propagating information from the first type of node to the second type of node), but first determines the node similarity corresponding to the node and the link importance corresponding to the link in real time for the target network, and then determines the information propagation importance between the node and each adjacent node in real time, and then determines the information propagation path from the first type of node to the second type of node in the target network based on the information propagation importance. That is, the best propagation path can be determined, based on which the first type of node can propagate information to the second type of node, and the specific communication method can be direct communication or indirect communication through other nodes. In this way, it is not required that any two nodes in the target network can communicate directly. There is no need for the first type of node to directly communicate with each second type of node separately, which is conducive to reducing the required communication resources and reducing the communication cost during information propagation.

[0054] like Figure 1 As shown, the embodiment of the present application provides a method for determining an information propagation path in a swarm intelligence scenario. Specifically, the method includes the following steps:

[0055] Step S100, obtaining a target network, wherein the target network includes a plurality of nodes and a plurality of links for connecting the nodes, and the nodes include a first type of nodes and a second type of nodes.

[0056] Wherein, the above-mentioned target network is a social network, and each node in the target network can be an individual in the social network, for example, it can be a user device, or an intelligent robot, or other types of individuals, which are not specifically limited here. The above-mentioned links are used to communicate with the nodes, and the specific communication method can be wired communication or wireless communication, which is not specifically limited here. The above-mentioned links can be regarded as edges for connecting nodes in the target network. It should be noted that in the embodiments of the present application, it is not required that there are links for connection between any two nodes.

[0057] In the embodiment of the present application, the categories of the various nodes in the above-mentioned target network may be different, for example, they may include first-class nodes and second-class nodes, and the specific classification criteria may be determined and adjusted according to actual needs. It should be noted that the above-mentioned target network may include multiple or one first-class nodes, and may also include multiple or multiple second-class nodes. In the embodiment of the present application, a specific description is given by taking one first-class node and multiple second-class nodes as an example to determine the optimal information propagation path from the first-class node to all second nodes.

[0058] In an application scenario, when there are multiple first-class nodes, the optimal information propagation path from each required first-class node to all second nodes can be determined. It should be noted that the information propagation paths corresponding to different first-class nodes can be the same or different, and the same path means that the first-class nodes are on the same path.

[0059] In an embodiment of the present application, the first type of node is an expert node, and the second type of node is a non-expert node. The knowledge level (or intelligence level) of the expert node is higher than that of the non-expert node. Specifically, the expert node has sufficient expertise in the evaluation task and can make a more accurate evaluation. The non-expert individuals have little or no expertise in the evaluation task, and the accuracy of the evaluation made is not high. In an embodiment of the present application, by determining the optimal path from the expert node to all other non-expert nodes, information can be efficiently transmitted from the expert node to the non-expert node during the social learning process.

[0060] In an application scenario, the above-mentioned expert nodes and non-expert nodes are individuals with social learning capabilities. The individuals can be mapped into a social network (i.e., the target network), and the edges of the network (i.e., the links used to connect the nodes) indicate that there are direct social connections between the nodes.

[0061] Step S200: determining the node similarity corresponding to each of the above nodes, wherein the node similarity corresponding to a node is used to represent the similarity between the above node and all the above first-category nodes.

[0062] In the embodiment of the present application, for each node in the target network, the corresponding node similarity is calculated and determined to characterize its similarity with the expert node. The higher the similarity with the expert node, the better the propagation efficiency and effect of the node in the information propagation process, and the more it should be selected.

[0063] Specifically, the above-mentioned determining the node similarity corresponding to each of the above-mentioned nodes includes:

[0064] For each node, respectively calculate the basic similarity between the above node and each first-category node;

[0065] Based on the sum of the basic similarities between the node and each first-category node, the node similarity corresponding to the node is determined.

[0066] The above-mentioned calculation of the basic similarity between each node and each first-category node includes:

[0067] For each node in the target network, perform the following operations:

[0068] Obtain the walking direction vector corresponding to the above nodes through random walk;

[0069] The cosine similarity between the wandering vector corresponding to the node and the wandering vector corresponding to each first-category node is calculated respectively as the basic similarity between the node and each first-category node.

[0070] Specifically, in the embodiment of the present application, the constructed social network G is used as the target network, which includes n nodes, and the node set is recorded as NodeSet. Two subsets are obtained by dividing the two types of nodes in the node set, namely, the expert node set ESet and the non-expert node NESet. The target network also corresponds to an edge set EdgeSet, which stores all links (i.e., edges) corresponding to the target network.

[0071] For the target network with n nodes, set the number of random walk steps m (the value of m can be set and adjusted according to actual needs), and then perform a random walk on each node to obtain n m-dimensional walk vectors Random_walk_vector, where the elements of the vector are the nodes passed by the random walk. The similarity between vectors is calculated based on cosine similarity. For example, for two given m-dimensional walk vectors A and B, the cosine similarity between the two is calculated based on the following formula (1):

[0072]

[0073] For each node i∈NodeSet, the cosine similarity C between the vector between it and the expert node j∈ESet can be calculated:ij , the total node similarity corresponding to node i is C i =∑ j∈ESet C ij .

[0074] Step S300: determining the link importance corresponding to each of the above links.

[0075] The link importance corresponding to the above links is used to characterize the importance of each link in the information transmission process. The link importance can be determined according to a preset weight value or in real time according to the actual situation of the network.

[0076] In the embodiment of the present application, the determining of the link importance corresponding to each of the above links includes:

[0077] Calculate the Kemeny constant value corresponding to the above target network;

[0078] For each link, remove the link from the target network to obtain a candidate network corresponding to the link, calculate the kemeny constant value corresponding to the candidate network, and determine the link importance corresponding to the link according to the kemeny constant value corresponding to the target network and the kemeny constant value corresponding to the candidate network.

[0079] Specifically, the Kemeny constant value can be calculated by combining graph theory and Markov chain. It should be noted that the embodiment of the present application is an undirected network, and the degree of each node is at least 2 to ensure that the Markov process is still met during the process of removing edges.

[0080] Consider a discrete-time, finite-state, and homogeneous Markov chain. In this case, the Markov chain is a discrete-time random process x k , k∈n, and is represented by the following equation, as shown in formula (2):

[0081]

[0082] Where n represents the number of states in the Markov process, which is specifically the number of nodes in the target network in the present embodiment. p(E|F) represents the probability of event E occurring when event F occurs. A Markov chain containing n states is completely described by an n×n transition probability matrix P, whose elements P ij Indicates state S i Exactly one step to state S j The probability of . P is a row random non-negative matrix because the elements in each row are probabilities and their sum is 1.

[0083] Let P denote the transition probability matrix of a finite irreducible Markov chain, and there exists a set of states satisfying Pu = u and π TP=π T The steady-state probability vector π and the all-1 vector u, from state S i To state S j The average first pass time m ij Indicates that when the starting point is S i Arrive at target S j The expected number of steps, then the kemeny constant can be expressed by the following formula (3):

[0084]

[0085] Furthermore, calculation in, is the link importance corresponding to the link connecting nodes i and i, is the Kemeny constant value corresponding to the candidate network after removing the link, K ori It is the kemeny constant value corresponding to the target network when the link is not removed.

[0086] Step S400, for each node in the target network, according to the node similarity corresponding to the node and the link importance of the link between the node and its adjacent nodes, determine the importance of information propagation between the node and each adjacent node.

[0087] Specifically, for each node in the target network, according to the node similarity corresponding to the node and the link importance of the link between the node and its adjacent nodes, determining the importance of information propagation between the node and each adjacent node includes:

[0088] For each node in the target network, the logarithmic value of the node similarity corresponding to the node is calculated, and the logarithmic value is multiplied by the link importance of the link between the node and its adjacent nodes to obtain the information propagation importance between the node and the adjacent nodes.

[0089] In the embodiment of the present application, for any node, for example, node i, the importance of information propagation between it and its adjacent node j is calculated based on the following formula (4):

[0090]

[0091] The larger the value of L is, the more important the point connected by the selected edge is. It should be noted that the logarithm in formula (4) is based on 2. In actual use, it can be adjusted according to actual needs and is not specifically limited here.

[0092] Step S500: determining the information propagation path from the first type of node to all the second type of nodes according to the information propagation importance.

[0093] Specifically, the target network is a connected network, and determining the information propagation path from the first type of node to all the second type of nodes according to the information propagation importance includes:

[0094] Initialize the information propagation path according to the first type of nodes mentioned above;

[0095] Take the first type of node as the starting node;

[0096] A second type node in the target network that is not included in the information propagation path is used as a termination node;

[0097] Select an adjacent node with the greatest importance of information dissemination to the above-mentioned starting node to add it to the above-mentioned information dissemination path, and use the selected adjacent node as the starting node;

[0098] Return to the step of selecting the adjacent node with the greatest importance of information propagation between the starting node and the starting node to add to the information propagation path, and taking the selected adjacent node as the starting node, until the ending node is added to the information propagation path;

[0099] Return to the step of using the first type of node as the starting node until all the second type of nodes are added to the information propagation path.

[0100] In one application scenario, the optimal propagation path from expert node i to non-expert node j is selected as follows:

[0101] ①. Get the neighbor set NeighborSet of node i. For m∈NeighborSet, select the node m with the largest corresponding L value. If j=m, end the path selection and obtain the optimal path.<i,j> , otherwise go to ②;

[0102] ② Select the neighbor set NeighborSet of m. For t∈NeighborSet, select the node t with the largest corresponding L value. If j=t, end the path selection and obtain the optimal path.<i,t,j> , otherwise continue to execute ②.

[0103] Finally, multiple optimal paths from expert node i to all non-expert nodes j can be obtained in the form of<i,...t,...j> .

[0104] It should be noted that, in the embodiment of the present application, the i and j in node i and node j only represent the corresponding node identifiers, and whether node i and node j are expert nodes or non-expert nodes depends on the specific description.

[0105] Figure 2is a specific flow chart of a method for determining an information propagation path in a group intelligence scenario provided by an embodiment of the present application, such as Figure 2 As shown, in the embodiment of the present application, the target network is obtained, the node similarity is determined based on random walk, the link importance is determined using the Kemeny constant, the information propagation importance is determined based on the node similarity and the link importance, and finally the information propagation path is determined based on the information propagation importance. In this way, the optimal propagation path can be directly found among experts and non-experts, and the information does not need to be propagated on all social contact edges and can also be propagated to other non-expert nodes, which can save information propagation resources and improve information propagation efficiency.

[0106] As can be seen from the above, in the method for determining the information propagation path in the swarm intelligence scenario provided by the embodiment of the present application, a target network is obtained, wherein the target network includes multiple nodes and multiple links for connecting the nodes, and the nodes include first-class nodes and second-class nodes; the node similarity corresponding to each of the above nodes is determined, wherein the node similarity corresponding to a node is used to characterize the degree of similarity between the above node and all the above-mentioned first-class nodes; the link importance corresponding to each of the above-mentioned links is determined; for each node in the target network, the information propagation importance between the above node and each adjacent node is determined according to the node similarity corresponding to the above node and the link importance of the link between the above node and its adjacent nodes; the information propagation path from the above-mentioned first-class node to all the above-mentioned second-class nodes is determined according to the above-mentioned information propagation importance.

[0107] Compared with the prior art, the present application scheme does not adopt a point-to-point direct communication method when performing information propagation in the target network (specifically propagating information from the first type of node to the second type of node), but first determines the node similarity corresponding to the node and the link importance corresponding to the link in real time for the target network, and then determines the information propagation importance between the node and each adjacent node in real time, and then determines the information propagation path from the first type of node to the second type of node in the target network based on the information propagation importance. That is, the best propagation path can be determined, based on which the first type of node can propagate information to the second type of node, and the specific communication method can be direct communication or indirect communication through other nodes. In this way, it is not required that any two nodes in the target network can communicate directly. There is no need for the first type of node to directly communicate with each second type of node separately, which is conducive to reducing the required communication resources and reducing the communication cost during information propagation.

[0108] like Figure 3 As shown in , corresponding to the information propagation path determination method in the above-mentioned group intelligence scenario, the embodiment of the present application also provides an information propagation path determination system in the group intelligence scenario, and the information propagation path determination system in the above-mentioned group intelligence scenario includes:

[0109] A network acquisition module 310 is used to acquire a target network, wherein the target network includes a plurality of nodes and a plurality of links for connecting the nodes, and the nodes include a first type of node and a second type of node;

[0110] A node similarity determination module 320 is used to determine the node similarity corresponding to each of the above nodes, wherein the node similarity corresponding to a node is used to represent the similarity between the above node and all the above first-category nodes;

[0111] A link importance determination module 330, used to determine the link importance corresponding to each of the above links;

[0112] The information transmission importance determination module 340 is used to determine, for each node in the target network, the information transmission importance between the node and each adjacent node according to the node similarity corresponding to the node and the link importance of the link between the node and its adjacent nodes;

[0113] The information propagation path determination module 350 is used to determine the information propagation path from the first type of node to all the second type of nodes according to the information propagation importance.

[0114] In this way, when information is transmitted in the target network (specifically, information is transmitted from the first type of node to the second type of node), point-to-point direct communication is not adopted. Instead, the node similarity corresponding to the node and the link importance corresponding to the link are first determined in real time for the target network, and then the information transmission importance between the node and each adjacent node is determined in real time. Then, based on the information transmission importance, the information transmission path from the first type of node to the second type of node in the target network is determined. That is, the best transmission path can be determined, based on which the first type of node can transmit information to the second type of node, and the specific communication method can be direct communication or indirect communication through other nodes. In this way, it is not required that any two nodes in the target network can communicate directly. It is not necessary for the first type of node to directly communicate with each second type of node separately, which is conducive to reducing the required communication resources and reducing the communication cost during the information transmission process.

[0115] It should be noted that the specific structure and implementation method of the information propagation path determination system and its various modules or units in the above-mentioned swarm intelligence scenario can refer to the corresponding description in the above-mentioned method embodiment, and will not be repeated here.

[0116] It should be noted that the division method of each module of the information propagation path determination system in the above-mentioned group intelligence scenario is not unique and is not specifically limited here.

[0117] Based on the above embodiments, the present application also provides a smart terminal, whose principle block diagram can be as follows: Figure 4 As shown. The above-mentioned intelligent terminal includes a processor, a memory, a network interface and a display screen connected through a system bus. Among them, the processor of the intelligent terminal is used to provide computing and control capabilities. The memory of the intelligent terminal includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and an information propagation path determination program in a group intelligence scenario. The internal memory provides an environment for the operation of the operating system in the non-volatile storage medium and the information propagation path determination program in the group intelligence scenario. The network interface of the intelligent terminal is used to communicate with an external terminal through a network connection. When the information propagation path determination program in the group intelligence scenario is executed by the processor, the steps of any of the above-mentioned information propagation path determination methods in the group intelligence scenario are implemented. The display screen of the intelligent terminal can be a liquid crystal display screen or an electronic ink display screen.

[0118] Those skilled in the art will understand that Figure 4 The principle block diagram shown in the figure is only a block diagram of a partial structure related to the solution of the present application, and does not constitute a limitation on the smart terminal to which the solution of the present application is applied. The specific smart terminal may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0119] In one embodiment, a smart terminal is provided, which includes a memory, a processor, and a program for determining an information propagation path in a group intelligence scenario stored in the memory and executable on the processor. When the program for determining an information propagation path in a group intelligence scenario is executed by the processor, the steps of any one of the methods for determining an information propagation path in a group intelligence scenario provided in the embodiments of the present application are implemented.

[0120] An embodiment of the present application also provides a computer-readable storage medium, on which is stored a program for determining an information propagation path in a group intelligence scenario. When the program for determining an information propagation path in a group intelligence scenario is executed by a processor, the steps of any method for determining an information propagation path in a group intelligence scenario provided in an embodiment of the present application are implemented.

[0121] It should be understood that the serial numbers of the steps in the above embodiments do not imply a sequence of execution. The execution sequence of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0122] The technicians in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In practical applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the above-mentioned device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated into a processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned device can refer to the corresponding process in the aforementioned method embodiment, which will not be repeated here.

[0123] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0124] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0125] In the embodiments provided in the present application, it should be understood that the disclosed system / terminal device and method can be implemented in other ways. For example, the system / terminal device embodiments described above are only schematic, for example, the division of the above modules or units is only a logical function division, and in actual implementation, other division methods can be used, for example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed.

[0126] If the above-mentioned integrated module / unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present application implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The above-mentioned computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Among them, the above-mentioned computer program includes computer program code, and the above-mentioned computer program code can be in source code form, object code form, executable file or some intermediate form. The above-mentioned computer-readable medium may include: any entity or device capable of carrying the above-mentioned computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electric carrier signal and software distribution medium, etc. It should be noted that the content contained in the above-mentioned computer-readable storage medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction.

[0127] The embodiments described above are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. However, these modifications or replacements do not deviate from the spirit and scope of the technical solutions of the embodiments of the present application, and should be included in the protection scope of the present application.

Claims

1. A method for determining an information propagation path in a swarm intelligence scenario, characterized in that: The method comprises: Acquire a target network, wherein the target network includes a plurality of nodes and a plurality of links for connecting the nodes, and the nodes include a first type of nodes and a second type of nodes; Determine the node similarity corresponding to each of the nodes, wherein the node similarity corresponding to a node is used to represent the similarity between the node and all the first-type nodes; Determining the link importance corresponding to each of the links; For each node in the target network, determine the importance of information propagation between the node and each adjacent node according to the node similarity corresponding to the node and the link importance of the link between the node and its adjacent nodes; An information propagation path from the first type of nodes to all the second type of nodes is determined according to the information propagation importance.

2. The method for determining information propagation paths in a swarm intelligence scenario according to claim 1, characterized in that: The first type of nodes are expert nodes, and the second type of nodes are non-expert nodes.

3. The method for determining information propagation paths in a swarm intelligence scenario according to claim 1, characterized in that: The determining the node similarity corresponding to each of the nodes includes: For each node, respectively calculate the basic similarity between the node and each first-category node; Based on the sum of the basic similarities between the node and each first-category node, a node similarity corresponding to the node is determined.

4. The method for determining information propagation paths in a swarm intelligence scenario according to claim 3, characterized in that: The step of calculating, for each node, the basic similarity between the node and each first-category node includes: For each node in the target network, perform the following operations respectively: Obtaining the walking direction vector corresponding to the node through random walking; The cosine similarity between the wandering vector corresponding to the node and the wandering vector corresponding to each first-category node is calculated respectively as the basic similarity between the node and each first-category node.

5. The method for determining information propagation paths in a swarm intelligence scenario according to claim 1, characterized in that: The determining the link importance corresponding to each of the links includes: Calculate the kemeny constant value corresponding to the target network; For each link, remove the link from the target network to obtain a candidate network corresponding to the link, calculate the kemeny constant value corresponding to the candidate network, and determine the link importance corresponding to the link according to the kemeny constant value corresponding to the target network and the kemeny constant value corresponding to the candidate network.

6. The method for determining information propagation paths in a swarm intelligence scenario according to claim 1, characterized in that: The step of determining, for each node in the target network, the importance of information propagation between the node and each adjacent node according to the node similarity corresponding to the node and the link importance of the link between the node and its adjacent nodes, includes: For each node in the target network, the logarithmic value of the node similarity corresponding to the node is calculated, and the logarithmic value is multiplied by the link importance of the link between the node and its adjacent nodes as the information propagation importance between the node and the adjacent nodes.

7. The method for determining the information propagation path in a swarm intelligence scenario according to any one of claims 1 to 6, characterized in that: The target network is a connected network, and determining the information propagation path from the first type of node to all the second type of nodes according to the information propagation importance includes: Initialize the information propagation path according to the first type of nodes; Taking the first type of node as the starting node; Taking a second type node in the target network that is not included in the information propagation path as a termination node; Select an adjacent node with the greatest importance of information propagation between the starting node and the adjacent node to add to the information propagation path, and use the selected adjacent node as the starting node; Return to the step of selecting the adjacent node with the greatest importance of information propagation between the starting node and the starting node to add to the information propagation path, and taking the selected adjacent node as the starting node, until the ending node is added to the information propagation path; Return to the step of taking the first type of node as the starting node until all the second type of nodes are added to the information propagation path.

8. A system for determining information propagation paths in a swarm intelligence scenario, characterized in that: The system comprises: A network acquisition module, used to acquire a target network, wherein the target network includes a plurality of nodes and a plurality of links for connecting the nodes, and the nodes include a first type of node and a second type of node; A node similarity determination module, used to determine the node similarity corresponding to each of the nodes, wherein the node similarity corresponding to a node is used to characterize the similarity between the node and all the first-type nodes; A link importance determination module, used to determine the link importance corresponding to each of the links; An information propagation importance determination module, used to determine, for each node in the target network, the importance of information propagation between the node and each adjacent node according to the node similarity corresponding to the node and the link importance of the link between the node and its adjacent nodes; An information propagation path determination module is used to determine the information propagation path from the first type of nodes to all the second type of nodes according to the information propagation importance.

9. An intelligent terminal, characterized in that: The intelligent terminal includes a memory, a processor, and an information propagation path determination program in a group intelligence scenario stored in the memory and executable on the processor. When the information propagation path determination program in the group intelligence scenario is executed by the processor, the steps of the information propagation path determination method in the group intelligence scenario as described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a program for determining an information propagation path in a swarm intelligence scenario, and when the program for determining an information propagation path in a swarm intelligence scenario is executed by a processor, the steps of the method for determining an information propagation path in a swarm intelligence scenario as described in any one of claims 1 to 7 are implemented.

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