CN112700124A - Multi-layer traffic network MRWC node importance ranking method, system, electronic equipment and computer readable storage medium - Google Patents
Multi-layer traffic network MRWC node importance ranking method, system, electronic equipment and computer readable storage medium Download PDFInfo
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Abstract
The invention discloses a method, a system, electronic equipment and a computer readable storage medium for ordering the importance of MRWC nodes of a multilayer traffic network, wherein the method comprises the following steps: constructing and obtaining a multi-layer traffic network; calculating the weight of each connecting edge of the multilayer traffic network to obtain a weight matrix; normalizing the obtained weight matrix to obtain a probability matrix M; the multilayer traffic network has N nodes, the importance of each node at the initial moment is the same, and an initial wandering matrix v is obtained; performing migration iteration based on the obtained probability matrix M and the initial migration matrix v, and taking the migration matrix after iteration stabilization as an importance value of each node of the multilayer traffic network; and sequencing and comparing the importance values to obtain a final result of node importance sequencing. According to the invention, by constructing the super-adjacency probability matrix comprehensively considering the interlayer relation in the layers, the evaluation result can be more accurate and efficient for the multilayer real network.
Description
Technical Field
The invention belongs to the technical field of network evaluation methods, relates to the field of key node sequencing in a multilayer traffic network, and particularly relates to a method, a system, electronic equipment and a computer-readable storage medium for sequencing importance of nodes of a multilayer traffic network MRWC (Random walk centering in multi-layer traffic network).
Background
The importance evaluation of complex network nodes is always a hotspot problem of network analysis, and a small number of important nodes in key positions often have greater control right on the network, so that the identification of important nodes in networks such as power networks, social networks, traffic networks, disease propagation and the like has great significance.
The importance research of the network nodes is firstly proposed by social networking scholars and applied to social network analysis. Around a network topological structure, scholars at home and abroad put forward various evaluation indexes such as degree, mesomeric centrality, approximate centrality, PageRank algorithm, Laplacian centrality and the like from different angles and different purposes. Because the importance of the nodes is often related to the overall structure of the network, a single index has certain one-sidedness and limitation, and some scholars try to research the importance of the nodes by using a multi-index comprehensive evaluation method. The Wangli et al selects three evaluation indexes of node betweenness, node connectivity and traffic hour flow at the intersection, and then evaluates the importance of the nodes by using an FCM fuzzy clustering method; screening indexes such as the hydroquinone and the like by adopting a very large uncorrelated method, determining the weight of each index by utilizing an improved entropy weight method, and finally determining the importance of the node by a grey correlation analysis method; dividing degree indexes and betweenness indexes of the network nodes of the river basin into 8 grades and 12 grades by Wu scholar and the like, and analyzing the importance of the nodes at different grades; in meetings and the like, a method based on multi-attribute decision is proposed to comprehensively evaluate the importance of nodes, and although various qualitative and quantitative information in the comprehensive evaluation process is well considered and integrated by using an analytic hierarchy process, randomness in the evaluation process, subjective uncertainty of an evaluation expert and cognition ambiguity are not eliminated in application, and the evaluation result is not objective. Qinli et al determines the weight of each index by combining with improved principal component analysis, and then obtains an importance evaluation result by using an approximate ideal ordering method.
The existing methods are mostly proposed for a single complex network, and a multilayer network is composed of two or more single-layer networks, so that the node importance ranking method in the single-layer network can be applied to the multilayer network after improvement. Sole-Ribalta et al apply betweenness centrality to multi-layer networks, can traverse different network layers when calculating shortest paths, and greatly reduces the time complexity of betweenness centrality by improving the Brandes algorithm. Chakraborty proposes a cross-layer betweenness centrality CBC algorithm in a multilayer network, and the action relation between different layers is blended when the shortest path is calculated. Jiang Guaping et al propose a multi-layer network PageRank algorithm based on interlayer feedback, and when the PageRank algorithm is used for iterating the importance of nodes, the influence of the importance of the nodes in other layers on the importance of the nodes in the layer is considered. In recent years, the Wanjuan and Limeizhu, etc. respectively propose a multi-layer network node ordering algorithm based on information fusion, and the importance of the nodes in all single-layer networks is fused by using the fusion algorithm. Mohammed et al studied the impact of different network topologies on the identification of influential nodes, and considered multi-layer interactions and overlapping links as weights. The Wang Juan defines layer importance weight and influence weight of each layer for index by using analytic hierarchy process, thereby carrying out node importance sequencing according to multilayer network evaluation value of nodes.
From the existing research, the research on the identification of the influence nodes in the complex network is mainly focused on a single-layer network, but the research based on the multi-layer network is started, so that the existence of interaction of various networks in the actual complex system is ignored, for example, the transfer relationship exists between a subway network and a public transport network in a public transport network; in reality, most of the nodes of complex systems have multiple functions, each node can provide different functions in each layer, and the interaction among the layers can realize all the functions, so that the information propagation process in the network can be better explained.
In summary, the existing multilayer traffic network key node identification method considers the interlayer action relationship from the network topology perspective, ignores the attribute difference between different layers, and urgently needs a new method and system capable of more accurately and efficiently evaluating the importance of the multilayer network node.
Disclosure of Invention
The present invention is directed to a method, a system, an electronic device, and a computer-readable storage medium for ranking the importance of MRWC nodes in a multi-layer traffic network, so as to solve one or more of the above technical problems. According to the invention, by constructing the super-adjacency probability matrix comprehensively considering the interlayer relation in the layers, the evaluation result can be more accurate and efficient for the multilayer real network.
In order to achieve the purpose, the invention adopts the following technical scheme:
the invention discloses a method for sequencing the importance of MRWC nodes of a multilayer traffic network, which comprises the following steps:
step2, calculating the weight of each connecting edge of the multilayer traffic network, comprising the following steps: stripping the node pairs with the interlayer connection relation from respective single-layer networks to form a cross-layer node set theta; calculating and obtaining the weight of each connecting edge of the multilayer traffic network based on the cross-layer node set theta to obtain a weight matrix;
step3, carrying out normalization processing on the weight matrix obtained in the step2 to obtain a probability matrix M;
step4, the multilayer traffic network has N nodes, the importance of each node is the same at the initial moment, an initial wandering matrix v is obtained,
step5, performing wandering iteration based on the probability matrix M obtained in the step3 and the initial wandering matrix v obtained in the step4, and taking the wandering matrix after iteration stabilization as an importance value of each node of the multilayer traffic network; and sequencing and comparing the importance values to obtain a final result of node importance sequencing.
The invention has the further improvement that the step 1 specifically comprises the following steps:
selecting Q traffic modes according to a preset research target, and determining a networking method aiming at different traffic modes; if two nodes in different traffic modes can be directly transferred, an interlayer connection relation is arranged between the two nodes;
wherein, the multilayer network is represented by Q ═ (ζ, C), ζ ═ Gα| α ∈ {1,2, … m } } is a single-layer network Gα＝(Vα,Eα) M is the number of layers, EαRepresenting connections of nodes within the layer, the collection of nodes in the alpha layer being represented as
The further improvement of the present invention is that, in step2, the calculating the expression of each connection edge weight of the multilayer traffic network specifically includes:
in the formula (I), the compound is shown in the specification,
represents the weight of the connecting edge between node i and its neighbor node j in the alpha layer,
j≠i；
representing the weight of a connecting edge between a node i in an alpha layer and a cross-layer node j ', wherein j ' belongs to theta, and j ' is not equal to i;
and the weight of a connecting edge between a node i 'in the alpha layer and a node j' in the beta layer is shown, and beta is not equal to alpha.
A further improvement of the present invention is that, in step3, the expression of the normalization process specifically includes:
in the formula (I), the compound is shown in the specification,
representing the probability of a node i in the alpha layer wandering to its neighbor node j, j ≠ i,
representing the probability that a node i in an alpha layer walks to a cross-layer node j ', wherein j ' is not equal to i, and j ' belongs to theta;
represents the probability that node i ' in layer α wanders to node j ' in layer β, β ≠ α, i ', j ∈ θ.
The further improvement of the invention is that in the initial probability matrix of step3, the more the two adjacent nodes of the same single-layer network have common adjacent nodes, the higher the similarity of the two nodes is, the larger the side weight is, the higher the probability of random walk is.
A further improvement of the present invention is that, in step5, the step of performing a computation of the walk iteration specifically includes:
the result obtained by multiplying the whole probability matrix M by v is the result after the first wandering, and the result after the first wandering respectively represents the importance value of the wandering reaching each node once; multiplying the first row of the initial probability matrix by v to obtain an importance value of once walking to reach the first node;
multiplying the result after the first wandering by the probability matrix M for the second time to obtain a result after the second wandering;
and (5) performing incremental iteration walking process until the walking matrix converges to a fixed value, and obtaining the stable walking matrix.
The invention relates to a system for sequencing the importance of MRWC nodes of a multilayer traffic network, which comprises the following components:
the system comprises a multi-layer traffic network acquisition module, a multi-layer traffic network acquisition module and a traffic information acquisition module, wherein the multi-layer traffic network acquisition module is used for constructing and acquiring a multi-layer traffic network which comprises a plurality of single-layer networks; the nodes in the single-layer network are provided with an intra-layer connection relation in a side connection mode, and the nodes capable of being directly transferred between the single-layer networks are provided with an inter-layer connection relation in the side connection mode;
the weight matrix acquisition module is used for calculating the weight of each connecting edge of the multilayer traffic network, and comprises: stripping the node pairs with the interlayer connection relation from respective single-layer networks to form a cross-layer node set theta; calculating and obtaining the weight of each connecting edge of the multilayer traffic network based on the cross-layer node set theta to obtain a weight matrix;
the probability matrix acquisition module is used for carrying out normalization processing on the obtained weight matrix to obtain a probability matrix M;
an initial wandering matrix obtaining module, configured to obtain an initial wandering matrix v according to N nodes shared by the multi-layer traffic network, where importance of each node is the same at an initial time,
the sequencing module is used for carrying out wandering iteration according to the obtained probability matrix M and the initial wandering matrix v, and taking the wandering matrix after iteration stabilization as an importance value of each node of the multilayer traffic network; and sequencing and comparing the importance values to obtain a final result of node importance sequencing.
An electronic device of the present invention includes: a processor; a memory for storing computer program instructions; when the computer program instructions are loaded and executed by the processor, the processor executes the multi-layer traffic network MRWC node importance ranking method.
The invention relates to a computer readable storage medium, which stores computer program instructions, wherein the computer program instructions are loaded and executed by a processor, and the processor executes any one of the above-mentioned methods for ranking the importance of MRWC nodes in a multi-layer traffic network according to the invention.
Compared with the prior art, the invention has the following beneficial effects:
in the method, the super-adjacency probability matrix comprehensively considering the interlayer relation in the layers is constructed, and the iteration stability value is taken as the node importance degree measurement standard, so that the evaluation result can be more accurate and efficient for the multilayer real network. Specifically, the existing identification method for key nodes of a multilayer traffic network ignores heterogeneity of connection relations between layers and within layers and utilizes interlayer connection edges to compound the multilayer network into a large network, identifies important nodes based on network physical topological structure indexes, does not distinguish difference of interlayer connection edges and interlayer connection edges, actually, the interlayer connection edges in the traffic network represent accessibility among lines or stations of vehicles in the layer, and the different interlayer connection edges represent transfer relations among different traffic modes. More specifically, the invention is convenient for objectively reflecting the connection relation among different vehicle lines or stations in a city; in addition, the key node identification method considers the difference of connection relations between layers and in-layer and the difference of connection relations between different layers, combines the practical utilization of transfer rates among different transportation modes to represent different inter-layer relations, improves the defect that all inter-layer connection relations are regarded as consistent in the existing method, and enables the evaluation result to be more accurate and efficient for a multi-layer real network.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art are briefly introduced below; it is obvious that the drawings in the following description are some embodiments of the invention, and that for a person skilled in the art, other drawings can be derived from them without inventive effort.
Fig. 1 is a schematic flow chart of a method for ranking the importance of MRWC nodes in a multi-layer traffic network according to an embodiment of the present invention;
fig. 2 is a schematic diagram of random walk of a multi-layer network in the embodiment of the present invention.
Detailed Description
In order to make the purpose, technical effect and technical solution of the embodiments of the present invention clearer, the following clearly and completely describes the technical solution of the embodiments of the present invention with reference to the drawings in the embodiments of the present invention; it is to be understood that the described embodiments are only some of the embodiments of the present invention. Other embodiments, which can be derived by one of ordinary skill in the art from the disclosed embodiments without inventive faculty, are intended to be within the scope of the invention.
The method for sequencing the importance of the MRWC nodes of the multilayer traffic network disclosed by the embodiment of the invention can enable an evaluation result to be more accurate and efficient for a multilayer real network by constructing the super-adjacency probability matrix comprehensively considering the interlayer relation of layers, and specifically comprises the following steps:
stepl: selecting a proper network building mode according to the node connection condition in the actual network to build a multilayer complex network; for example, an interconnected multilayer network may be represented as M ═ (ζ, C), where ζ ═ GαI α ∈ {1, 2., m } } is a single-layer network Gα＝(Vα,Eα) M is the number of layers, EαThe edges in (a) are called connections of nodes in the layer, and the collection of nodes in the alpha layer is represented as
EαβIs the set of interlayer nodes in M connecting layers alpha and beta.
Corresponds to EαβBetween layers of an adjacency matrix of
The expression formula is as follows:
step 2: and constructing the weighting matrix according to the relation of the connecting edges in the network. The nodes between the connection layers are stripped from the respective networks to form a set theta, the nodes can also be called cross-layer nodes, and the nodes are nodes with interlayer connection edges and play a connection role in different network layers. Definition of
Weight for representing the connecting edge between the node i and the neighbor node j ≠ i in alpha layer
Represents the weight of the connecting edge between the node i in the alpha layer and the cross-layer node j '≠ i (j' is belonged to theta),
the weight of a connecting edge between a node i 'in an alpha layer and a node j' in beta ≠ alpha is represented, and the calculation formula is as follows:
in the formula, ki＝∑jaijRepresents the node i andthe number of connected edges between all neighboring nodes, and the same holds for kj′Representing the number of connecting edges between the cross-layer node j' and all its neighbor nodes. RhoαRepresenting the probability of a vehicle going straight on the alpha layer, σαRepresenting the probability of an intra-floor transfer, τ, of a vehicle riding on the alpha floorαβRepresenting the transfer rate between alpha and beta layer vehicles. Wherein, tauαβThe larger, indicating a greater probability of transferring other vehicles, the greater the likelihood that the node will wander to other levels.
Step 3: and constructing a probability matrix M in the network. In order to describe the conversion rule of node migration dynamics in the layer and the interlayer in unit time, a probability transition matrix is defined.
Probability that node i in alpha layer roams to its neighbor node j ≠ i
Representing the probability that node i in the alpha layer wanders to a cross-layer node j '≠ i (j' ∈ theta),
represents the probability (i ', j' ∈ θ) that node i 'in the α layer wanders to node j' in layer β ≠ α. The result of normalization processing of the defined probability matrix, i.e. the weight matrix, is as follows:
step 4: assuming that the importance of each node is the same at the initial moment, N nodes are shared in the network, and the probability of each node when the node starts to walk is the same, and the value is 1/N;
the walk start matrix is
Step 5: and (5) performing iterative computation. Multiplying v by the first row of the probability matrix to obtain the importance value of the first node reached by the wandering motion once, and multiplying v by the second row of the probability matrix to obtain the importance value of the second node reached by the wandering motion once, and similarly, multiplying the whole probability matrix M by v to obtain the result MRWC(1)Representing the importance of walking once to each node. MRWC (first migration result)(1)Assigning a new wandering matrix, and then MRWC the second wandering result(2)That is, the probability matrix M is multiplied by the new walk matrix MRWC(1)Third migration results MRWC(3)That is, the probability matrix M is multiplied by MRWC(2)Iterate until MRWC(n)Equal to MRWC(n+1)The iteration terminates.
Step 6: taking converged MRWC(n)As a node importance measure index, memory MRWC(n)The value of the ith row is MRWC(n)(i) And represents the importance value of the node i in the network after the nth walk is finished. Mixing MRWC(n)All the elements in the node are compared and sequenced to obtain the final result of the node importance sequencing.
The method provided by the embodiment of the invention is not only suitable for the multilayer urban traffic network constructed by the same modeling space, but also suitable for the network constructed by different modeling spaces. Due to the fact that urban traffic lines are complicated and complex, the number and distribution of stations are greatly different, and therefore modeling spaces suitable for different vehicles are different. The invention is convenient for objectively reflecting the connection relation between different traffic tool lines or stations in a city; the key node identification method takes the difference of the connection relation between the layers in the layer and the interlayer and the difference of the connection relation between different layers into consideration, combines the practical transfer rate between different traffic modes to represent different interlayer relations, and overcomes the defect that all interlayer connection relations are regarded as consistent in the existing method.
Referring to fig. 1 and fig. 2, a method for ranking the importance of MRWC nodes in a multi-layer traffic network according to an embodiment of the present invention accurately and efficiently estimates key nodes in a real traffic network by calculating a multi-layer construction probability matrix, including the following steps:
step 1): according to the current development situation of urban transportation vehicles, M transportation vehicles are selected as research objects, and for each transportation vehicle, factors such as site distribution, line number and the like are comprehensively considered to select a proper networking mode for networking.
Step 2): each traffic mode constructs a layer of network, if direct transfer relations exist among nodes of different traffic network layers, connecting edges exist among the nodes, and if no transfer relations exist, no connecting relations exist, so that an M-layer traffic network is constructed.
Step 3): a multilayer network super-adjacent matrix is constructed according to the connection condition of each node in an actual network, the connection relation among the nodes is shown as a formula (1), when alpha is equal to beta, the connection relation among the nodes in the layer is shown, and when alpha is equal to beta, the connection relation among the nodes in the layer is shown.
Step 4): in consideration of the problem of network heterogeneity, interlayer node similarity and transfer probability are introduced to represent the weight of each connecting edge in the network, and the calculation formulas are shown as formulas (2), (3) and (4):
in the formula, ki∩kjRepresenting the number of common neighbors owned by two adjacent nodes, rhoαRepresenting the direct rate, σ, of a vehicle riding on the alpha horizonαRepresenting the floor transfer rate, tau, of a vehicle riding on the alpha floorαβRepresenting the transfer rate between alpha and beta layer vehicles.
Weight for representing the connecting edge between the node i and the neighbor node j ≠ i in alpha layer
Represents the weight of the connecting edge between the node i in the alpha layer and the cross-layer node j' ≠ i (j belongs to theta),
and the weight (i ', j' epsilon theta) of a connecting edge between the node i 'in the alpha layer and the node j' in the beta ≠ alpha is represented.
Step 5): and carrying out normalization processing on the obtained weight matrix to obtain a probability matrix M.
Step 6): assuming that the importance of each node is the same at the initial moment and N nodes are shared in the multilayer network, all elements in the initial wandering matrix v are assigned to be 1/N, the wandering initial matrix v is multiplied by the probability matrix M, and the obtained result is the importance value of once wandering arrival and is recorded as MRWC(1)Multiplying the probability matrix M and the first wandering result Mv for the second time to obtain a result MRWC after the second wandering(2). By continuously repeating the walking process, the iteration result can gradually tend to be stable and converge to a fixed value.
Step 7): taking the stabilized MRWC(n)And (3) as an importance metric value of each node of the multilayer network, n is the iteration number, and the results are sorted and compared to obtain the final result of the node importance sorting.
In summary, the present invention relates to the technical field of ordering key nodes in a multi-layer traffic network, and in particular, to a method for ordering the importance of MRWC nodes in a multi-layer traffic network, which measures the importance of the nodes by calculating a centrality value MRWC (Random traffic center in multi-layer traffic network) of each node. Firstly, lines and nodes of M kinds of transportation means are considered respectively to establish M single-layer networks, if the nodes of different network layers can be directly transferred, a connection edge relation is established, and an M-layer transportation network is constructed. And then, calculating a weight matrix, a probability matrix and an initial walking matrix by combining a random walking model aiming at the constructed multilayer network, and then performing walking iteration by multiplying the matrixes, wherein an iteration result MRWC (maximum likelihood ratio) is finally converged to a fixed value, and the convergence result is the importance value of the network node.
The electronic equipment for the MRWC node importance ranking method of the multilayer traffic network comprises the following steps:
on the hardware level, the electronic device comprises: the processor optionally further comprises an internal bus, a network interface and a memory. The memory may include a memory, such as a high speed random access memory, and may also include a non-volatile memory, such as at least one disk memory. Of course, the electronic device may also include hardware required for other services.
The processor, network interface, memory are interconnected by an internal bus, which may be an industry standard architecture bus, a peripheral component interconnect standard bus, an extended industry standard architecture bus, or the like. The bus may be divided into an address bus, a data bus, a control bus, etc.
The memory is used for storing programs. In particular, the program may include program code, which includes computer operating instructions. The memory may include both memory and non-volatile storage and provides instructions and data to the processor.
The processor reads the corresponding computer program from the nonvolatile memory into the memory and then runs the computer program to form the electronic equipment on the logic level. When the processor executes the program, the following operations are specifically executed:
step2, calculating the weight of each connecting edge of the multilayer traffic network, comprising the following steps: stripping the node pairs with the interlayer connection relation from respective single-layer networks to form a cross-layer node set theta; calculating and obtaining the weight of each connecting edge of the multilayer traffic network based on the cross-layer node set theta to obtain a weight matrix;
step3, carrying out normalization processing on the weight matrix obtained in the step2 to obtain a probability matrix M;
step4, the multilayer traffic network has N nodes, the importance of each node is the same at the initial moment, an initial wandering matrix v is obtained,
step5, performing wandering iteration based on the probability matrix M obtained in the step3 and the initial wandering matrix v obtained in the step4, and taking the wandering matrix after iteration stabilization as an importance value of each node of the multilayer traffic network; and sequencing and comparing the importance values to obtain a final result of node importance sequencing.
A computer-readable storage medium of an embodiment of the present invention stores computer program instructions, and when the computer program instructions are loaded and executed by a processor, the processor executes a method including:
step2, calculating the weight of each connecting edge of the multilayer traffic network, comprising the following steps: stripping the node pairs with the interlayer connection relation from respective single-layer networks to form a cross-layer node set theta; calculating and obtaining the weight of each connecting edge of the multilayer traffic network based on the cross-layer node set theta to obtain a weight matrix;
step3, carrying out normalization processing on the weight matrix obtained in the step2 to obtain a probability matrix M;
step4, the multilayer traffic network has N nodes, the importance of each node is the same at the initial moment, and the obtained result is obtainedThe start-of-walk matrix v is,
step5, performing wandering iteration based on the probability matrix M obtained in the step3 and the initial wandering matrix v obtained in the step4, and taking the wandering matrix after iteration stabilization as an importance value of each node of the multilayer traffic network; and sequencing and comparing the importance values to obtain a final result of node importance sequencing.
In particular, the computer-readable storage medium includes, but is not limited to, volatile memory and/or non-volatile memory, for example. The volatile memory may include Random Access Memory (RAM) and/or cache memory (cache), among others. The non-volatile memory may include a Read Only Memory (ROM), hard disk, flash memory, optical disk, magnetic disk, and the like.
As will be appreciated by one skilled in the art, 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 flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams 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 data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, 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 data processing apparatus 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 data processing 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.
Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art can make modifications and equivalents to the embodiments of the present invention without departing from the spirit and scope of the present invention, which is set forth in the claims of the present application.
Claims (9)
1. A method for sequencing the importance of MRWC nodes of a multi-layer traffic network is characterized by comprising the following steps:
step 1, constructing and obtaining a multilayer traffic network, wherein the multilayer traffic network comprises a plurality of single-layer networks; the nodes in the single-layer network are provided with an intra-layer connection relation in a side connection mode, and the nodes capable of being directly transferred between the single-layer networks are provided with an inter-layer connection relation in the side connection mode;
step2, calculating the weight of each connecting edge of the multilayer traffic network, comprising the following steps: stripping the node pairs with the interlayer connection relation from respective single-layer networks to form a cross-layer node set theta; calculating and obtaining the weight of each connecting edge of the multilayer traffic network based on the cross-layer node set theta to obtain a weight matrix;
step3, carrying out normalization processing on the weight matrix obtained in the step2 to obtain a probability matrix M;
step4, the multilayer traffic network has N nodes, the importance of each node is the same at the initial moment, an initial wandering matrix v is obtained,
step5, performing wandering iteration based on the probability matrix M obtained in the step3 and the initial wandering matrix v obtained in the step4, and taking the wandering matrix after iteration stabilization as an importance value of each node of the multilayer traffic network; and sequencing and comparing the importance values to obtain a final result of node importance sequencing.
2. The method for ranking the importance of the MRWC nodes in the multi-layer transportation network according to claim 1, wherein step 1 specifically comprises:
selecting Q traffic modes according to a preset research target, and determining a networking method aiming at different traffic modes; if two nodes in different traffic modes can be directly transferred, an interlayer connection relation is arranged between the two nodes;
wherein, the multilayer network is represented by Q ═ (ζ, C), ζ ═ Gα| α ∈ {1,2, … m } } is a single-layer network Gα＝(Vα，Eα) M is the number of layers, EαRepresenting connections of nodes within the layer, the collection of nodes in the alpha layer being represented as
3. The method for ranking the importance of the MRWC nodes in the multi-layer transportation network according to claim 1, wherein the step2 of calculating the expression of the weight of each connecting edge of the multi-layer transportation network specifically includes:
in the formula (I), the compound is shown in the specification,
represents the weight of the connecting edge between node i and its neighbor node j in the alpha layer,
j≠i；
representing the weight of a connecting edge between a node i in an alpha layer and a cross-layer node j ', wherein j ' belongs to theta, and j ' is not equal to i;
representing the weight of a connecting edge between a node i 'in the alpha layer and a node j' in the beta layer, wherein beta is not equal to alpha; k is a radical ofi∩kjRepresenting the number of common neighbors owned by two adjacent nodes, rhoαRepresenting the direct rate, σ, of a vehicle riding on the alpha horizonαRepresenting an intra-floor transfer rate of a vehicle riding an alpha floor; k is a number ofiRepresents the number of connecting edges, k, between node i and all its neighborsj′Representing the number of connecting edges between the cross-layer node j' and all its neighbor nodes.
4. The method for ranking the importance of the MRWC nodes in the multi-layer traffic network according to claim 3, wherein in step3, the expression of the normalization process specifically includes:
in the formula (I), the compound is shown in the specification,
representing the probability of a node i in the alpha layer wandering to its neighbor node j, j ≠ i,
representing the probability that a node i in an alpha layer walks to a cross-layer node j ', wherein j ' is not equal to i, and j ' belongs to theta;
represents the probability that node i ' in layer α wanders to node j ' in layer β, β ≠ α, i ', j ∈ θ.
5. The method as claimed in claim 4, wherein in the initial probability matrix of step3, the more common neighbor nodes two neighbor nodes of the same single-layer network have, the higher the similarity of the two nodes, the greater the side weight, and the greater the probability of random walk.
6. The method for ranking the importance of the MRWC nodes of the multi-layer traffic network according to claim 4, wherein in the step5, the step of performing the computation of the walk iteration specifically includes:
the result obtained by multiplying the whole probability matrix M by v is the result after the first wandering, and the result after the first wandering respectively represents the importance value of the wandering reaching each node once; multiplying the first row of the initial probability matrix by v to obtain an importance value of once walking to reach the first node;
assigning the result after the first wandering to be a new wandering matrix and multiplying the new wandering matrix by the probability matrix M for the second time to obtain the result after the second wandering;
and (5) performing incremental iteration walking process until the walking matrix converges to a fixed value, and obtaining the stable walking matrix.
7. A multi-tier traffic network MRWC node importance ranking system, comprising:
the system comprises a multi-layer traffic network acquisition module, a multi-layer traffic network acquisition module and a traffic information acquisition module, wherein the multi-layer traffic network acquisition module is used for constructing and acquiring a multi-layer traffic network which comprises a plurality of single-layer networks; the nodes in the single-layer network are provided with an intra-layer connection relation in a side connection mode, and the nodes capable of being directly transferred between the single-layer networks are provided with an inter-layer connection relation in the side connection mode;
the weight matrix acquisition module is used for calculating the weight of each connecting edge of the multilayer traffic network, and comprises: stripping the node pairs with the interlayer connection relation from respective single-layer networks to form a cross-layer node set theta; calculating and obtaining the weight of each connecting edge of the multilayer traffic network based on the cross-layer node set theta to obtain a weight matrix;
the probability matrix acquisition module is used for carrying out normalization processing on the obtained weight matrix to obtain a probability matrix M;
an initial wandering matrix obtaining module, configured to obtain an initial wandering matrix v according to N nodes shared by the multi-layer traffic network, where importance of each node is the same at an initial time,
the sequencing module is used for carrying out wandering iteration according to the obtained probability matrix M and the initial wandering matrix v, and taking the wandering matrix after iteration stabilization as an importance value of each node of the multilayer traffic network; and sequencing and comparing the importance values to obtain a final result of node importance sequencing.
8. An electronic device, comprising: a processor; a memory for storing computer program instructions; it is characterized in that the preparation method is characterized in that,
the computer program instructions, when loaded and executed by the processor, cause the processor to perform the method of multi-level traffic network MRWC node importance ranking of any one of claims 1 to 6.
9. A computer readable storage medium storing computer program instructions, which when loaded and executed by a processor, performs the method of multi-tier traffic network MRWC node importance ranking of any one of claims 1-6.
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