CN115442243B - Sequential network node centrality evaluation method and device based on sequential path tree - Google Patents

Sequential network node centrality evaluation method and device based on sequential path tree Download PDF

Info

Publication number
CN115442243B
CN115442243B CN202211052281.6A CN202211052281A CN115442243B CN 115442243 B CN115442243 B CN 115442243B CN 202211052281 A CN202211052281 A CN 202211052281A CN 115442243 B CN115442243 B CN 115442243B
Authority
CN
China
Prior art keywords
time sequence
node
time
path
timing
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Active
Application number
CN202211052281.6A
Other languages
Chinese (zh)
Other versions
CN115442243A (en
Inventor
孔盛洲
陶丽
蒋正超
贾韬
张自力
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Southwest University
Original Assignee
Southwest University
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Southwest University filed Critical Southwest University
Priority to CN202211052281.6A priority Critical patent/CN115442243B/en
Publication of CN115442243A publication Critical patent/CN115442243A/en
Application granted granted Critical
Publication of CN115442243B publication Critical patent/CN115442243B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Classifications

    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
    • H04L41/14—Network analysis or design
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
    • H04L41/14—Network analysis or design
    • H04L41/145—Network analysis or design involving simulating, designing, planning or modelling of a network
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L45/00—Routing or path finding of packets in data switching networks
    • H04L45/02—Topology update or discovery
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L45/00—Routing or path finding of packets in data switching networks
    • H04L45/12—Shortest path evaluation
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L45/00—Routing or path finding of packets in data switching networks
    • H04L45/12—Shortest path evaluation
    • H04L45/122—Shortest path evaluation by minimising distances, e.g. by selecting a route with minimum of number of hops
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L45/00—Routing or path finding of packets in data switching networks
    • H04L45/48—Routing tree calculation

Landscapes

  • Engineering & Computer Science (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Signal Processing (AREA)
  • Data Exchanges In Wide-Area Networks (AREA)

Abstract

The application relates to a method and a device for evaluating the centrality of time sequence network nodes based on a time sequence path tree, which are used for identifying influential propagators in a time sequence network based on the centrality (SPT-C) index of the time sequence path tree. The SPT-C method focuses on combining various timing isomerism features to accelerate and maximize true propagation. Secondly, a time Sequence Path Tree (SPT) applies a tree structure to limit the number of possible propagation paths between nodes, compresses specific propagation in a display path flow model, and integrates potential paths into the shortest path from a root node to other nodes by using a contact time set, so that the time sequence information extraction is simple and quick, and the efficiency of identifying influential propagators in a time sequence network is improved.

Description

Sequential network node centrality evaluation method and device based on sequential path tree
Technical Field
The present disclosure relates to the field of complex networks and timing networks, and in particular, to a method and apparatus for evaluating the centrality of a timing network node based on a timing path tree.
Background
Identifying nodes that play an important role in complex networks is a concern in recent years. As this may help solve many real world practical problems, such as determining "superpassers-by" in the spread of infectious diseases, determining key personals for information communication in online social networks, and finding the most influential areas in nationwide product promotions, etc.
Currently, the importance of an individual node can be defined from different perspectives, such as the size of infection that an infected propagator can cause, the speed at which the node is affected, and the removal of the node's impact on network survivability. On this basis, various centrality indexes are proposed by the related art. Although these metrics have good performance on static networks, they may not be suitable for solving practical problems because static structures do not fully describe real social relationships.
In a real social network, the connections between individuals are intermittent and transient. For example, in Email networks, the corresponding connection between users is only made when sending or receiving mail. The kinetic mechanism can better cover the fox with a time-ordered network, consisting of a series of contact events. Therefore, how to effectively identify important nodes (such as propagators having influence in the propagation process) in a more realistic time-series network has received a great deal of attention in recent years.
To solve this problem, constructing an effective timing network representation structure in order to extract timing information is the basis for studying the timing network. The representation structure in the related art is a displayed path flow model, which extracts a timing path representing the occurrence of an event between each pair of nodes. The timing path is described as a monotonically increasing sequence of timing stamp edges. By describing the flow path of propagation, the structure is more sensitive to the evolution of the network and achieves impressive performance. However, the process of extracting timing features based on this structure has a high complexity due to different requirements for the features. Thus, there is still much room for improvement in the display path flow model.
Disclosure of Invention
The application provides a method and a device for evaluating the centrality of time sequence network nodes based on a time sequence path tree so as to quantify the influence of propagators in a time sequence network. The technical scheme of the application is as follows:
according to a first aspect of an embodiment of the present application, the present application provides a method for evaluating centrality of a timing network node based on a timing path tree, the method comprising:
step 1: constructing a group of time sequence path trees by taking any node in a time sequence network as a root node, wherein the time sequence path tree also comprises non-root nodes, the root node is provided with three time sequence isomerism characteristics of propagation time, hop count and reachable path count, the propagation time is used for representing time spent by a node infection propagation process, the hop count is used for representing other node numbers needing to pass through in the propagation process, and the reachable path count is used for reflecting the size of potential time sequence path scales from the root node to the non-root node;
step 2: respectively constructing a characteristic matrix of the propagation time, the hop count and the reachable path count, wherein the row vector corresponding to the root node stores time sequence characteristic values of all target nodes calculated based on the time sequence path tree;
step 3: normalizing the time sequence feature matrix, and calculating the time sequence centrality of each root node in the time sequence network based on the normalized time sequence feature matrix;
step 4: and ordering the nodes in the time sequence network according to the time sequence network node centrality evaluation model based on the time sequence path tree so as to identify influential propagators.
Optionally, the timing path tree includes a plurality of nodes, each node representing a propagator having timing characteristic information recorded therein, and each undirected edge indicating at least one contact between a parent node and a child node.
Optionally, when constructing a group of the timing path tree, a first condition and a second condition should be satisfied at the same time, where the first condition is that at least one timing path exists between a root node and any child node, and the second condition is that each timing path in the timing path tree satisfies a time shortest priority principle.
Alternatively, v i Representing root nodes in a time-series path tree, v j Representing non-root nodes in a time-series path tree, the propagation time being specifically defined as a time pathThe maximum timestamp on the last edge of the series, the propagation time and the recovery probability are in a proportional relationship.
Optionally, the hop count passes through a pathLength calculation of (c), wherein if v j And v i Is far away, v j The exponentially decreasing infection rate, the less likely an infection will occur.
Alternatively, the reachable path number may be v in the computation timing path tree j The number of contact time stamps with the parent node approaches indefinitely.
Optionally, the propagation influenceExpressed as:
wherein v is i Representing a root node in a time-series network,representing a time-series path tree, v j (v j E.V) represents the timing path tree +.>Is not a root node in (a); omega 0 (ω 0 ∈[0,1])，ω 1 (ω 1 ∈[0,1]) Represents the weight parameter, ω 0 +ω 1 ≤1； Propagation time +.>Jump count->And the number of reachable paths->Is provided.
Optionally, normalizing the feature matrix of the propagation time, the hop count and the reachable path count, which specifically includes:
wherein R is max And R is min Representing the maximum and minimum number of reachable paths between any pair of nodes, respectively.
According to a second aspect of embodiments of the present application, there is provided a timing network node centrality evaluation device based on a timing path tree, including: a processor; a memory for storing the processor-executable instructions; wherein the processor is configured to execute the instructions to implement the timing network node centrality evaluation method based on a timing path tree of any of the methods mentioned in the first aspect above.
The technical scheme provided by the embodiment of the application at least brings the following beneficial effects:
the invention firstly provides a time sequence network node centrality evaluation method and device based on a time sequence path tree, and the propagators with influence in a time sequence network are identified based on the centrality (SPT-C) index of the time sequence path tree. The SPT-C method focuses on combining various timing isomerism features to accelerate and maximize true propagation. Second, SPT applies a tree structure to limit the number of possible propagation paths between nodes, where one non-root node has and only has one parent node. This structure compresses the specific propagation in the display path flow model and uses the contact time set to integrate the potential path into the shortest path from the root node to the other nodes. On this basis, the timing characteristics in propagation will be embodied on the tree depth and node data. The structure ignores some details of non-shortest paths with less structural influence, so that the time sequence information is extracted simply and quickly.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the application.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the application and together with the description, serve to explain the principles of the application and do not constitute an undue limitation on the application.
FIG. 1 is a flow diagram illustrating a method for centrality evaluation of a timing network node based on a timing path tree, according to an example embodiment;
FIG. 2 is a schematic diagram illustrating a timing path tree constructed based on node I, according to an example embodiment;
FIG. 3 is a schematic diagram illustrating a method of centrality evaluation of time series network nodes based on time series path trees, according to an example embodiment;
fig. 4 is a graph showing Pearson correlation coefficients between node rankings based on different centrality and true rankings based on SIR models versus matching rates at different Top k%, according to an example embodiment.
Detailed Description
In order to enable those skilled in the art to better understand the technical solutions of the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings.
It should be noted that the terms "first," "second," and the like in the description and claims of the present application and the above figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that embodiments of the present application described herein may be implemented in sequences other than those illustrated or otherwise described herein. The implementations described in the following exemplary examples are not representative of all implementations consistent with the present application. Rather, they are merely examples of apparatus and methods consistent with some aspects of the present application as detailed in the accompanying claims.
As shown in fig. 1, the present application proposes a method for evaluating node centrality of a time-series network based on a time-series path tree, which can easily identify the node centrality of a propagator with influence in a time-series network through sequencing. The method mainly comprises the following four steps:
step 1: and constructing a group of time sequence path trees by taking any node in the time sequence network as a root node, wherein the time sequence path tree also comprises non-root nodes, any node in the tree has three time sequence isomerism characteristics of propagation time, hop count and reachable path count, the propagation time is used for representing time spent by a node infection propagation process, the hop count is used for representing other node numbers needing to pass through in the propagation process, and the reachable path count is used for reflecting the size of the scale of potential time sequence paths from the root node to the non-root node.
In some embodiments, each node in the timing network is taken as a root node v i Constructing a set of timing path treesWherein any one node v j With propagation time->Jump count->Number of reachable paths->Three temporal isomerism features, v j For non-root node, the propagation time +.>Representing node v j The time taken for the infection transmission process, said hop count +.>Representing the number of other nodes that need to be traversed during the propagation, the number of reachable paths +.>For reflecting slave node v i To v j Is a potential timing path size of the system.
Step 2: and respectively constructing the characteristic matrixes of the propagation time, the hop count and the reachable path count, wherein the row vector corresponding to the root node stores the time sequence characteristic values of all target nodes calculated based on the time sequence path tree.
In some embodiments, propagation times are separately constructedJump count->Number of reachable paths->T, L and R, wherein any node v i The corresponding row vector holds the time-series path tree based +.>And calculating the time sequence characteristic values of all the target nodes.
Step 3: and carrying out normalization processing on the time sequence feature matrix, and calculating the time sequence centrality of each root node in the time sequence network based on the normalized time sequence feature matrix.
In some embodiments, the normalized timing feature matrix is based onAnd->Calculating each node v in the time sequence network i Timing centrality of->
Step 4: and ordering the nodes in the time sequence network according to the time sequence network node centrality evaluation model based on the time sequence path tree so as to identify influential propagators.
In some embodiments, the present application illustratively proposes a new Sequential-PathTree (SPT) structure for a Sequential network representation. As shown in FIG. 2, the timing path tree is a special type of tree in which each node represents a propagator that records timing characteristic information, and each undirected edge indicates that at least one contact has occurred between a parent node and a child node.
The above described sequential network representation structure compresses specific propagation in the explicit path flow model and integrates the remaining set of contact events and potential paths into the shortest sequential path to satisfy the structural attribute of the tree, i.e., the uniqueness of the parent node. Therefore, the constructed timing path tree should satisfy the following two conditions at the same time:
first condition: at least one time sequence path exists between the root node and any child node;
second condition: each timing path in the timing path tree meets the time shortest priority principle.
According to the definition above, a timing path treeInvolving the slave root node v i Initially, to any other node v j All timing paths of end->
For example, by using a graph traversal algorithm based on the time earliest priority principle, it is possible to traverse a network from a timing including 13 nodes, as shown in (a) of fig. 2. Constructing a time sequence path Tree based on a root node I I As shown in fig. 2 (B). Tree constructed from (B) in fig. 2 I From the root node I to any child node (e.g. P I→D ,P I→M ) In addition to P I→F (indicating that node I cannot reach node F).
It should be noted that in the timing path tree, each timing path is irreversible, i.eThe weighted adjacency matrix corresponding to the timing path tree is thus asymmetric. Meanwhile, in the same time sequence path tree, slave node v i To the point ofThere may be multiple potential timing paths. Therefore, the shortest timing path based on the fastest propagation time or hop count is particularly notable, which means that once v i Infected node v i Infection v j Is a function of the speed of the machine.
In some embodiments, three timing heterogeneity features contemplated herein, including propagation time, may be extracted based on the constructed timing path treeJump count->Number of reachable paths->Three timing characteristics are defined as follows:
propagation time: for each node v j The propagation time, which represents the time taken for the infection to propagate, can be defined asMaximum timestamp on last edge, e.g. T I→D ＝2，T I→M ＝2，T I→F = infinity. The propagation time affects the probability of the propagator to heal, generally the propagation time is proportional to the probability of healing, the longer it takes, the greater the likelihood of healing, resulting in breakage of the infection propagation chain. It is worth mentioning that the node recovering from the infection will not be infected any more.
Hop count: the hop count represents the number of other nodes that need to pass through in the propagation process and can pass through the pathCalculated by length of (L) I→D ＝2，L I→M ＝2，L I→F = infinity. If v j And v i Is far, meaning v j The exponentially decreasing infection rate, the less likely an infection will occur.
Number of reachable paths: the number of reachable paths reflects the slave node v i To v j Is a potential timing path size of the system. Calculating v in a time-series path tree j The number of contact time stamps with the parent node to approximate the number of reachable paths, e.g. R I→D ＝1，R I→M =2 and R I→F ＝0。
In some embodiments, based on the determination of the timing Path Tree, the present application proposes a timing network node Centrality evaluation model (abbreviated as Sequential-Path Tree-Based Centrality, SPT-C) Based on the timing Path Tree to quantify the influence of propagators in the timing network. The centrality defines a node v i Is its propagation influence in the timing path treeIn particular, the method comprises the steps of,
wherein v is j (v j E V) represents a timing path treeIs not a root node in (a); omega 0 (ω 0 ∈[0,1])，ω 1 (ω 1 ∈[0,1]) Represents the weight parameter, ω 0 +ω 1 ≤1；/>Normalized timing characteristic matrices of propagation time, hop count and reachable path count, respectively.
In order to ensure monotonous consistency, the method performs normalization (normalization) treatment on three feature matrixes of the transmission time, the hop count and the reachable path count:
wherein R is max And R is min Representing the maximum and minimum number of reachable paths between any pair of nodes, respectively.
In some embodiments, in the time sequence network node centrality evaluation method based on the time sequence path tree, a propagator with influence can be easily identified, the whole identification process is performed as algorithm 1, and as shown in fig. 3, a time sequence network node centrality evaluation method based on the time sequence path tree is also exemplarily shown.
In some embodiments, table 1 summarizes the basic statistics of the 12 true timing networks used in the present application to verify validity, including the number of nodes, the number of edges, the number of contacts, and the total sampling time for each network.
Illustratively, the present application uses the superimposable-selected-Recovered (SIR) model to simulate an outbreak of infection, and uses simulation results to evaluate the effectiveness of SPT-C in identifying the most influential propagators. SIR model is a propagation model, which is an abstract description of the information propagation process. Meanwhile, the SIR model is the most classical model among infectious disease models, where S (Susceptible) represents a susceptible person, I (active) represents an infected person, and R (Removal) represents a remover. SIR models are applied in the study of information propagation. In SIR models, the propagation process roughly includes: initially, all nodes are in an vulnerable state. Then, after some nodes contact information, the nodes become infected, and the nodes in the infected states try to infect the nodes in other easily infected states or enter a recovery state. Infection of a node is the transfer of information or attitudes to something. The recovery state, i.e. immunization, the nodes in the recovery state are no longer involved in the propagation of the information. Where β represents the infection rate and γ represents the recovery rate.
In a further embodiment, to eliminate the randomness of the SIR model based simulation, the importance of each node is run a certain number (e.g. 2000) and averaged. Meanwhile, the Pearson correlation coefficient and Top k% are used as indexes to evaluate the effectiveness of the time sequence centrality in the aspects of consistency and accuracy. These two evaluation indices ρ, top k％ The definition is as follows:
table 1 time-series network architecture features used in this application
In table 1, N represents the number of nodes, E represents the number of sides in the aggregated static graph (there is at least one contact of a side between two nodes), C represents the number of contact events, and T represents the total sampling time.
In some embodiments, the effectiveness of SPT-C is compared to the other 6 reference timing centralities. The aim is to compare classical and latest centrality indexes, so that the comparison is more convincing. The 6 kinds of benchmark centroids are respectively a snapshot-based temporal centrality (2012), a snapshot-based temporal betweenness centrality (2013), a temporal path-based temporal adjacency centrality (2017), and three temporal gravity model-based centrality indexes (2021).
The results of comparing SPT-C as set forth in the present application with 6 baseline methods are shown in Table 2 and FIG. 4. Table 2 shows Pearson correlation coefficients between each timing center and the node importance calculated by the SIR model, and the bolded numerical terms represent the timing center metrics with the highest correlation. Fig. 4 shows the matching rate of node ranking based on different centrality to true ranking based on SIR model at different Top k%.
In a further embodiment, each weight parameter in the SPT-C method is set to a fixed value, ω 0 ＝0.3，ω 1 =0.1, and the infection rate and recovery rate in SIR model simulation are also assumed to be fixed values, β=0.3 and γ=3, respectively.
TABLE 2 Pearson correlation coefficient between timing centrality and node importance based on SIR model
The comparison result of the two evaluation indexes shows that the SPT-C based on the multi-time sequence isomerism characteristic has better performance in the aspects of measuring the node centrality in a time sequence network and identifying the influential propagators. As shown in table 2, the Person correlation coefficient of SPT-C outperforms the other 6 benchmarks on all 12 real data sets. The bolded numerical terms mean that the computed node centrality matches the true value in the current timing network to the highest degree. FIG. 4 shows that the Top k% indicator of SPT-C is always better than other reference timing centrality across all timing networks when the Top node percentage is greater than 0.1. By observation, SPT-C can reach a matching rate of more than 25% at Top 10% and a matching rate of more than 80% at Top 50%.
The application provides a novel node centrality evaluation method based on a time sequence path tree, which takes each node in a time sequence network as a root node v i Constructing a set of timing path treesEach node has three time sequence isomerism characteristics of propagation time, hop count and reachable path count; constructing feature matrices T, L and R of propagation time, hop count and reachable path count, respectively, wherein any node v i The corresponding row vectors hold timing-basedPath Tree->The calculated time sequence characteristic values of all the target nodes; time sequence characteristic matrix based on normalization>And->Calculating each node v in a time sequence network i Timing centrality of (2)The SPT-C values based on the metrics rank the nodes in the time-ordered network, thereby quickly identifying influential propagators. In the context of identifying influential propagators, the importance of the nodes is measured by comprehensively considering various time sequence heterogeneity characteristics such as propagation time, hop count, reachable path count and the like. Meanwhile, the application provides a new time sequence network representation structure, namely a time sequence path tree, which is used for extracting time sequence characteristics and calculating centrality. Also, through evaluation experiments on 12 real timing networks, SPT-C is more accurate than benchmark timing centrality in finding the most influential propagators.
The application relates to a method and a device for evaluating the centrality of time sequence network nodes based on a time sequence path tree, which are used for identifying influential propagators in a time sequence network based on the centrality (SPT-C) index of the time sequence path tree. The SPT-C method focuses on combining various timing isomerism features to accelerate and maximize true propagation. Secondly, a time Sequence Path Tree (SPT) applies a tree structure to limit the number of possible propagation paths between nodes, compresses specific propagation in a display path flow model, and integrates potential paths into the shortest path from a root node to other nodes by using a contact time set, so that the time sequence information extraction is simple and quick, and the efficiency of identifying influential propagators in a time sequence network is improved.
The application scenario described in the embodiments of the present application is for more clearly describing the technical solution of the embodiments of the present application, and does not constitute a limitation on the technical solution provided in the embodiments of the present application, and as a person of ordinary skill in the art can know that, with the appearance of a new application scenario, the technical solution provided in the embodiments of the present application is also applicable to similar technical problems.
Those skilled in the art will appreciate that the various aspects of the present application may be implemented as a system, method, or program product. Accordingly, aspects of the present application may be embodied in the following forms, namely: an entirely hardware embodiment, an entirely software embodiment (including firmware, micro-code, etc.) or an embodiment combining hardware and software aspects may be referred to herein as a "circuit," module "or" system.
In some possible implementations, an electronic device according to the present application may include at least one processor, and at least one memory. The memory stores therein program code that, when executed by the processor, causes the processor to perform the operational data management methods described above in this specification according to various exemplary embodiments of the present application. For example, the processor may perform steps as in an operational data management method.
Further, the timing network node centrality evaluation device based on a timing path tree according to this embodiment of the present application may perform the steps in the timing network node centrality evaluation method based on a timing path tree mentioned in the above-mentioned embodiment.
In an exemplary embodiment, aspects of a method and apparatus for evaluating centrality of a timing network node based on a timing path tree may also be implemented in the form of a program product, which includes program code for causing a computer device to perform the steps of the method for maximizing quality of experience in a multi-antenna unmanned aerial vehicle video transmission system according to various exemplary embodiments of the present application described above, when the program product is run on the computer device.
It should be noted that although several units or sub-units of the apparatus are mentioned in the above detailed description, such a division is merely exemplary and not mandatory. Indeed, the features and functions of two or more of the elements described above may be embodied in one element in accordance with embodiments of the present application. Conversely, the features and functions of one unit described above may be further divided into a plurality of units to be embodied.
Furthermore, although the operations of the methods of the present application are depicted in the drawings in a particular order, this is not required to or suggested that these operations must be performed in this particular order or that all of the illustrated operations must be performed in order to achieve desirable results. Additionally or alternatively, certain steps may be omitted, multiple steps combined into one step to perform, and/or one step decomposed into multiple steps to perform.
It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable image scaling device to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable image scaling device, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable image scaling device to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable image scaling apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
While preferred embodiments of the present application have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. It is therefore intended that the following claims be interpreted as including the preferred embodiments and all such alterations and modifications as fall within the scope of the application.
It will be apparent to those skilled in the art that various modifications and variations can be made in the present application without departing from the spirit or scope of the application. Thus, if such modifications and variations of the present application fall within the scope of the claims and the equivalents thereof, the present application is intended to cover such modifications and variations.

Claims (9)

1. A method for centrality evaluation of a time sequence network node based on a time sequence path tree, the method comprising:
step 1: constructing a group of time sequence path trees by taking any node in a time sequence network as a root node, wherein the time sequence path tree also comprises non-root nodes, the root node is provided with three time sequence isomerism characteristics of propagation time, hop count and reachable path count, the propagation time is used for representing time spent by a node infection propagation process, the hop count is used for representing other node numbers needing to pass through in the propagation process, and the reachable path count is used for reflecting the size of potential time sequence path scales from the root node to the non-root node;
step 2: respectively constructing a characteristic matrix of the propagation time, the hop count and the reachable path count, wherein the row vector corresponding to the root node stores time sequence characteristic values of all target nodes calculated based on the time sequence path tree;
step 3: normalizing the time sequence feature matrix, and calculating the time sequence centrality of each root node in the time sequence network based on the normalized time sequence feature matrix;
step 4: and ordering the nodes in the time sequence network according to the time sequence network node centrality evaluation model based on the time sequence path tree so as to identify influential propagators.
2. The method of claim 1, wherein the timing path tree comprises a plurality of nodes, each node representing a propagator having timing characteristic information recorded therein, each undirected edge indicating at least one contact between a parent node and a child node.
3. The method according to claim 2, wherein a first condition and a second condition are satisfied simultaneously when constructing a set of the timing path trees, wherein the first condition is that at least one timing path exists between a root node and any child node, and the second condition is that each timing path in the timing path tree satisfies a time shortest priority principle.
4. A method according to claim 3, wherein v i Representing root nodes in a time-series path tree, v j Representing non-root nodes in a time-series path tree, the propagation time being specifically defined as a time pathThe maximum timestamp on the last edge of the series, the propagation time and the recovery probability are in a proportional relationship.
5. The method of claim 4, wherein the hop count passes through a pathLength calculation of (c), wherein if v j And v i Is far away, v j The exponentially decreasing infection rate, the less likely an infection will occur.
6. The method of claim 5, wherein the number of reachable paths is v in a computation timing path tree j The number of contact time stamps with the parent node approaches indefinitely.
7. The method according to claim 6, characterized in that a node v is defined i Propagation influence in a time-series path treeThe concrete steps are as follows:
wherein omega 0 ∈[0，1]，ω 1 ∈[0，1]Are all weight parameters omega 0 +ω 1 ≤1；Propagation time +.>Jump count->And the number of reachable paths->Normalization of (2)A timing characteristic matrix.
8. The method of claim 7, wherein normalizing the feature matrix of propagation time, hop count, and reachable path count specifically comprises:
wherein R is max And R is min Representing the maximum and minimum number of reachable paths between any pair of nodes, respectively.
9. A timing network node centrality evaluation device based on a timing path tree, comprising:
a processor;
a memory for storing the processor-executable instructions;
wherein the processor is configured to execute the instructions to implement the timing path tree based timing network node centrality assessment method of any one of claims 1 to 8.
CN202211052281.6A 2022-08-31 2022-08-31 Sequential network node centrality evaluation method and device based on sequential path tree Active CN115442243B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202211052281.6A CN115442243B (en) 2022-08-31 2022-08-31 Sequential network node centrality evaluation method and device based on sequential path tree

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202211052281.6A CN115442243B (en) 2022-08-31 2022-08-31 Sequential network node centrality evaluation method and device based on sequential path tree

Publications (2)

Publication Number Publication Date
CN115442243A CN115442243A (en) 2022-12-06
CN115442243B true CN115442243B (en) 2024-04-16

Family

ID=84244920

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202211052281.6A Active CN115442243B (en) 2022-08-31 2022-08-31 Sequential network node centrality evaluation method and device based on sequential path tree

Country Status (1)

Country Link
CN (1) CN115442243B (en)

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN110213164A (en) * 2019-05-21 2019-09-06 南瑞集团有限公司 A kind of method and device of the identification network key disseminator based on topology information fusion
CN111222029A (en) * 2020-01-16 2020-06-02 西安交通大学 Method for selecting key nodes in network public opinion information dissemination
WO2020177366A1 (en) * 2019-03-07 2020-09-10 平安科技（深圳）有限公司 Data processing method and apparatus based on time sequence data, and computer device
CN113378470A (en) * 2021-06-22 2021-09-10 常熟理工学院 Time sequence network-oriented influence maximization method and system

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2020177366A1 (en) * 2019-03-07 2020-09-10 平安科技（深圳）有限公司 Data processing method and apparatus based on time sequence data, and computer device
CN110213164A (en) * 2019-05-21 2019-09-06 南瑞集团有限公司 A kind of method and device of the identification network key disseminator based on topology information fusion
CN111222029A (en) * 2020-01-16 2020-06-02 西安交通大学 Method for selecting key nodes in network public opinion information dissemination
CN113378470A (en) * 2021-06-22 2021-09-10 常熟理工学院 Time sequence network-oriented influence maximization method and system

Also Published As

Publication number Publication date
CN115442243A (en) 2022-12-06

Similar Documents

Publication Publication Date Title
CN110380896B (en) Network security situation awareness system and method based on attack graph
Kivelä et al. Multiscale analysis of spreading in a large communication network
CN110147387B (en) Root cause analysis method, root cause analysis device, root cause analysis equipment and storage medium
JP2021533474A (en) Node classification method, model training method, and its equipment, equipment and computer program
CN110213164B (en) Method and device for identifying network key propagator based on topology information fusion
CN108427725A (en) Data processing method, device and system
CN102870092A (en) System and method for determining application dependency paths in a data center
CN105279397A (en) Method for identifying key proteins in protein-protein interaction network
CN110138638B (en) Network traffic processing method and device
CN114928548A (en) Social network information propagation scale prediction method and device
Sashittal et al. Sampling and summarizing transmission trees with multi-strain infections
CN115442243B (en) Sequential network node centrality evaluation method and device based on sequential path tree
Junuthula et al. Leveraging friendship networks for dynamic link prediction in social interaction networks
CN116469039B (en) Hot video event determination method and system, storage medium and electronic equipment
Chen et al. Identifying the superspreader in proactive backward contact tracing by deep learning
CN110955705B (en) Data acquisition and analysis method and device for fortress machine
CN109409411A (en) The problem of based on operation management localization method, device and storage medium
CN114116853A (en) Data security analysis method and device based on time sequence correlation analysis
CN114628041A (en) Key node identification method and system based on approximate centrality calculation
CN113673811A (en) Session-based online learning performance evaluation method and device
CN112365994A (en) Infectious disease data monitoring processing method, system and storage medium
CN112256756A (en) Influence discovery method based on ternary association diagram and knowledge representation
CN114756731A (en) Advertisement channel data processing method and device, storage medium and electronic equipment
Ding et al. DyGMamba: Efficiently Modeling Long-Term Temporal Dependency on Continuous-Time Dynamic Graphs with State Space Models
CN111522722A (en) Data analysis method, electronic equipment and storage medium

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant