Information propagation blocking method and system based on MinMaxC model

Abstract

The invention relates to an information propagation blocking method and system based on a MinMaxC model, and the method comprises the steps: obtaining an initial network diagram; a maximum independent set is searched through a fast heuristic algorithm, the maximum independent set is a secondary node set, nodes in the secondary node set are not adjacent to one another, and the maximum independent set is initialized; and selecting an important node from the cutting point set and inserting the important node into the cutting point set, executing node insertion through a greedy algorithm with a minimum increment, deleting the remaining nodes in the residual image after the minimum increment is completed, and outputting the cutting point set. The invention also provides two improved greedy algorithms, namely an IG + algorithm and an IG-PR algorithm, which improve the calculation efficiency of the node insertion step in the method. Network control personnel can correspondingly delete the communication nodes in the social network according to the cutting point set generated by the method disclosed by the invention, so that distortion information circulation is blocked.

Description

Technical Field

[0001] This application relates to the field of graph data processing technology, and in particular to a method and system for blocking information propagation based on the MinMaxC model. Background Technology

[0002] Solving the MinMaxC problem in network graphs can be applied to prevent distorted information from spreading on network platforms.

[0003] Example scenario: Online social platforms (such as Xiaohongshu and Weibo) are common mediums for information dissemination, and users may accept and spread distorted information such as rumors and pseudoscience without discernment.

[0004] In reality, distorted information spreads rapidly on social networks and tends to cluster more densely than positive information. The spread of distorted information can have serious consequences, such as endangering public health and safety and exacerbating conflicts. Furthermore, the dissemination of AI-generated content may further amplify the impact of negative information. Therefore, developing effective and efficient algorithms and systems to curb the spread of distorted information is of great significance.

[0005] The MinMaxC problem in network graphs refers to selecting b nodes from a given graph G such that deleting these nodes minimizes the maximum connected component of the graph. Blocking propagation at important nodes can prevent the spread of distorted information to the greatest extent.

[0006] Existing methods generally employ a node deletion strategy, calculating connected components by deleting nodes one by one in a graph G until a few important nodes are selected. However, the computational efficiency of existing methods is too low. This application provides an information propagation blocking method and system based on the MinMaxC model, which improves node selection efficiency through a node insertion algorithm. Summary of the Invention

[0007] To overcome the problems existing in related technologies, this application provides an information propagation blocking method based on the MinMaxC model, specifically including: Step 101: Obtain the initial network graph; Step 102: Use a fast heuristic algorithm to find the maximum independent set and the set of cut points in the initial network graph. The maximum independent set is the set of minor nodes, where nodes are not adjacent to each other. Initialize to the largest independent set; where, For residual plots; Step 103: Select an important node from the set of cutting points. Insert into In order to minimize The increment is achieved by performing node insertion using a greedy algorithm, and in After minimizing, delete the remaining nodes in the residual graph and output the set of cut points; where, For the set of cutting points, It is the largest connected component.

[0008] Preferably, step 102 includes: first setting the independent set to an empty set. According to the degree of the node Enumerate each node in non-decreasing order Insert optional minor nodes into the largest independent set and mark their neighbors as non-optional.

[0009] Preferably, step 103 includes: Generate auxiliary graph ', and calculate the corresponding ; By continuously finding the current state through a min-heap... smallest node and will from 'Insert into ; Added to In, to replace In and with Connected nodes It represents A node in a connected component, where ; The neighbors are set as the outer neighbors of the node being replaced; maintain and his neighbors 'in The algorithm updates the heap and repeats the above process until... .

[0010] Preferably, step 103 includes: The auxiliary diagram 'satisfy: Each node is One of the nodes or One of the connected components; ,like ,but ;like ,but .

[0011] Preferred, The calculation formula is: .

[0012] Preferably, it may also include: selecting an important node from the set of cutting points. Insert into In order to minimize The increment is performed by inserting nodes using the IG+ algorithm, and in After minimization, the remaining nodes in the residual graph are deleted, and the set of cut points is output; the IG+ algorithm includes bucket list optimization and delayed update optimization.

[0013] Preferably, step 103 can also be: Insert into In order to minimize The increment is performed by inserting nodes using the IG-PR algorithm, and in After minimization, the remaining nodes in the residual graph are deleted, and the cut point set is output; the IG+ algorithm includes bucket list optimization and delayed update optimization; The IG-PR algorithm will from Find a subset of nodes in the array that has the highest height, where the number of nodes in the subset is the parameter. Multiply by the number of nodes inserted since the last partial deletion, and remove the nodes from the subset of nodes with the highest height number. Remove and put back Recalculate sequentially 'and .

[0014] The second aspect of this application provides an information propagation blocking system based on the MinMaxC model, characterized in that it includes steps used in the information propagation blocking method described in the first aspect of this application.

[0015] The technical solution provided in this application may include the following beneficial effects: Based on the initial network graph, important and secondary nodes are selected. Important nodes are then inserted into the residual graph formed by the secondary nodes. A greedy algorithm is provided to repeatedly perform the node insertion operation. In each iteration, the maximum connectivity of the residual graph is calculated, and the iteration ends when the maximum connectivity is minimized, outputting a set of cut points. Network controllers can then delete communication nodes in the social network according to the cut point set, thereby blocking the flow of distorted information. Extensive experiments on eight real-world datasets demonstrate that the method in this application is more efficient than state-of-the-art heuristic algorithms in graph data processing, and is also approximately 5 to 2 orders of magnitude faster.

[0016] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and do not limit this application. Attached Figure Description

[0017] The above and other objects, features and advantages of this application will become more apparent from the more detailed description of exemplary embodiments thereof in conjunction with the accompanying drawings, wherein the same reference numerals generally represent the same components in the exemplary embodiments thereof.

[0018] Figure 1 This is a flowchart illustrating the method described in the embodiments of this application; Figure 2 This is a code example diagram of the greedy algorithm for node insertion described in the embodiments of this application; Figure 3 This is a code example diagram illustrating the search for the largest independent set as described in the embodiments of this application; Figure 4 These are code example diagrams of the two accelerated and optimized IG+ algorithms described in the embodiments of this application; Figure 5 This is a code example diagram of IG-PR for node insertion as described in the embodiments of this application; Figure 6 This is a comparison chart of the information intervention effects of the IG+ algorithm described in the embodiments of this application and existing algorithms on real datasets; Figure 7 This is a comparison chart of the computational efficiency of the IG+ algorithm shown in the embodiments of this application and existing algorithms on real datasets. Detailed Implementation

[0019] Preferred embodiments of the present application will now be described in more detail with reference to the accompanying drawings. While preferred embodiments of the present application are shown in the drawings, it should be understood that the present application may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided to make the present application more thorough and complete, and to fully convey the scope of the present application to those skilled in the art.

[0020] The terminology used in this application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. The singular forms “a,” “the,” and “the” used in this application and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used herein refers to and includes any or all possible combinations of one or more of the associated listed items.

[0021] It should be understood that although the terms "first," "second," "third," etc., may be used in this application to describe various information, this information should not be limited to these terms. These terms are only used to distinguish information of the same type from one another. For example, without departing from the scope of this application, first information may also be referred to as second information, and similarly, second information may also be referred to as first information. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this application, "multiple" means two or more, unless otherwise explicitly specified.

[0022] like Figures 1 to 5 As shown, this application provides an information propagation blocking method based on the MinMaxC model, including: Step 101: Obtain the initial network graph; Step 102: Use a fast heuristic algorithm to find the maximum independent set and the set of cut points in the initial network graph. The maximum independent set is the set of minor nodes, where nodes are not adjacent to each other. Initialize to the largest independent set; where, For residual plots; Step 103: Select an important node from the set of cutting points. Insert into In order to minimize The increment is achieved by performing node insertion using a greedy algorithm, and in After minimizing, delete the remaining nodes in the residual graph and output the set of cut points; where, For the set of cutting points, It is the largest connected component.

[0023] In step 101, let Represent a simple, undirected, unweighted graph, in which there are vertices and An edge. Given a node. , exist The neighbor set in the middle is represented as , The degree is expressed as .set up , Represents the vertex set Induced Subgraph.

[0024] In step 102, in Find the largest possible independent set, specifically by setting the independent set to an empty set first. Next, according to the degree of the node Enumerate each node in non-decreasing order Insert optional nodes into an independent set and mark their neighbors as non-optional. Initialize to this maximum independent set, where, This is a residual plot.

[0025] In one implementation of step 104, the node insertion step is performed using the IG algorithm, as follows: Generate an auxiliary diagram based on the following two requirements. , (i) Each node in the middle is either One of the nodes, or (ii) A connected component in . ,if , ,if , ,in This is the set of cutting points.

[0026] according to Calculate the corresponding .

[0027] Next, the algorithm continuously finds the current value using a min-heap. smallest node and will from 'Insert into .Then Added to In, to replace In and with Connected nodes It represents A node in a connected component, where, . The neighbors of the node being replaced are set as the outer neighbors of the node being replaced. In addition, the algorithm also maintains... and his neighbors 'in The algorithm then updates the heap. This process is repeated until... ,Right now .

[0028] In another implementation, based on the above-mentioned IG algorithm, this application embodiment also provides IG+ algorithm execution step 104 in order to perform two acceleration optimizations—bucket list optimization and delayed update optimization.

[0029] The principle of the IG+ algorithm is as follows: (a) We used the following bucket list structure to maintain and find the one with the largest The bucket list consists of nodes representing... Specifically, the bucket list is composed of a doubly linked list, containing... All The bucket list contains n elements, representing numbers from 1 to n. The values ​​are linked in ascending order. Each node... When it is inserted into the bucket list, it becomes the representative. The successor of the element. In addition, the structure uses two arrays of pointers, each pointing to a different element. The value and node elements. In the algorithm, when the vertex... of Increase to At this point, we first use the pointer array mentioned above to find the representative pointer in the linked list. The element is then removed from the linked list, and finally relinked to the representative. The successor position of the element. We also use a variable to record the maximum score of all nodes in the current bucket list, called... and use This is used to perform the operation of finding or popping the largest node from the bucket list. In other words, whenever we find or pop the largest node, we simply return or remove the representative... The successor element of the element. Once it represents The elements due to Or the fraction increment operation is represented +1 element inheritance will increase Until the above conditions no longer apply.

[0030] (b) We further propose a delayed update optimization scheme, that is, when a node's When changes occur, we only record the new ones. Instead of immediately modifying its position in the bucket list, it remains there until it becomes the top element in the bucket list.

[0031] In one implementation, this application also proposes an IG-PR algorithm (partial deletion greedy algorithm) for performing the node insertion step.

[0032] The main idea of ​​the IG-PR algorithm is that, during the basic insertion process of IG, as long as... Doubling the number will initiate a partial deletion operation.

[0033] Specifically, the algorithm will start from Find a subset of nodes with the highest height, the size of which is the parameter. Multiply by the number of vertices inserted since the last partial deletion. The algorithm will then remove these vertices from... Remove and put back Next, the algorithm will recalculate sequentially. and The rest of the IG-PR algorithm is the same as the IG+ algorithm.

[0034] Figure 6 The figures show experimental results of the algorithm's information intervention effect on real datasets, where IG+ is the algorithm running in the proposed method of this application, and the others are existing methods. Figure 6 As shown, the IG+ algorithm can control the propagation of distorted information within a smaller range. Figure 7 This figure shows an experimental comparison of the computational efficiency of the IG+ algorithm on a real dataset, as illustrated in the embodiments of this application. The IG+ algorithm is the one used in the proposed method of this application, while the others are high-performance baseline methods, such as... Figure 7 As shown, the IG+ algorithm has a smaller runtime overhead. like Figures 6 to 7 As shown, the node insertion computation framework proposed in this application has the following advantages: extensive experiments on eight real-world datasets have demonstrated that its results are more efficient than those of state-of-the-art heuristic algorithms, and are also about 5 to 2 orders of magnitude faster.

[0035] Based on the information propagation blocking method described above, this application embodiment also provides an information propagation blocking system based on the MinMaxC model, used to execute the steps in the information propagation blocking method.

[0036] The solution of this application has been described in detail above with reference to the accompanying drawings. In the above embodiments, the descriptions of each embodiment have different emphases; parts not described in detail in a certain embodiment can be referred to in the relevant descriptions of other embodiments. Those skilled in the art should also understand that the actions and modules involved in the specification are not necessarily essential to this application. Furthermore, it is understood that the steps in the method of this application embodiment can be adjusted, combined, and deleted according to actual needs, and the modules in the device of this application embodiment can be combined, divided, and deleted according to actual needs.

[0037] Furthermore, the method according to this application can also be implemented as a computer program or computer program product, which includes computer program code instructions for performing some or all of the steps in the method described above.

[0038] Alternatively, this application may be implemented as a non-transitory machine-readable storage medium (or computer-readable storage medium, or machine-readable storage medium) storing executable code (or computer program, or computer instruction code) thereon, which, when executed by a processor of an electronic device (or electronic device, server, etc.), causes the processor to perform part or all of the steps of the methods described above according to this application.

[0039] Those skilled in the art will also understand that the various exemplary logic blocks, modules, circuits, and algorithm steps described in connection with the present application can be implemented as electronic hardware, computer software, or a combination of both.

[0040] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems and methods according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than those marked in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0041] The various embodiments of this application have been described above. These descriptions are exemplary and not exhaustive, nor are they limited to the disclosed embodiments. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments. The terminology used herein is chosen to best explain the principles, practical application, or improvement of the technology in the market, or to enable others skilled in the art to understand the embodiments disclosed herein.

Claims

1. A method for blocking information propagation based on the MinMaxC model, characterized in that, include: Step 101: Obtain the initial network graph; Step 102: Use a fast heuristic algorithm to find the maximum independent set and the set of cut points in the initial network graph. The maximum independent set is the set of minor nodes, where nodes are not adjacent to each other. Initialize as the largest independent set; where, For residual plots; Step 103: Select an important node from the set of cutting points. Insert into In order to minimize The increment is achieved by performing node insertion using a greedy algorithm, and in After minimizing, delete the remaining nodes in the residual graph and output the set of cut points; where, For the set of cutting points, It is the largest connected component.

2. The information transmission blocking method as described in claim 1, characterized in that, Step 102 includes: first, setting the independent set to an empty set. According to the degree of the node Enumerate each node in non-decreasing order Insert optional minor nodes into the largest independent set and mark their neighbors as non-optional.

3. The information transmission blocking method as described in claim 1, characterized in that, Step 103 includes: Generate auxiliary graph ', and calculate the corresponding ; By continuously finding the current state through a min-heap... smallest node and will from 'Insert into ; Added to In, to replace In and with Connected nodes It represents A node in a connected component, where ; The neighbors are set as the outer neighbors of the node being replaced; maintain and his neighbors 'in The algorithm updates the heap and repeats the above process until... .

4. The information transmission blocking method as described in claim 3, characterized in that, Step 103 includes: The auxiliary diagram 'satisfy: Each node is One of the nodes or One of the connected components; ,like ,but ;like ,but .

5. The node insertion calculation method as described in claim 3, characterized in that, The calculation formula is: 。 6. The information transmission blocking method as described in claim 3, characterized in that, Step 103 may also include: Select an important node from the set of cutting points. Insert into In order to minimize The increment is performed by inserting nodes using the IG+ algorithm, and in After minimization, the remaining nodes in the residual graph are deleted, and the set of cut points is output; the IG+ algorithm includes bucket list optimization and delayed update optimization.

7. The information transmission blocking method as described in claim 3, characterized in that, Step 103 can also be: Select an important node from the set of cutting points. Insert into In order to minimize The increment is performed by inserting nodes using the IG-PR algorithm, and in After minimization, the remaining nodes in the residual graph are deleted, and the cut point set is output; the IG+ algorithm includes bucket list optimization and delayed update optimization; The IG-PR algorithm will from Find a subset of nodes in the array that has the highest height, where the number of nodes in the subset is the parameter. Multiply by the number of nodes inserted since the last partial deletion, and remove the nodes from the subset of nodes with the highest height number. Remove and put back Recalculate sequentially 'and .

8. An information propagation blocking system based on the MinMaxC model, characterized in that, Used to perform the steps in the information transmission blocking method according to any one of claims 1 to 7.

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