188 results about "Clustering coefficient" patented technology

The invention discloses a multimodal brain network feature fusion method based on multi-task learning, and the multimodal brain network feature fusion method based on the multi-task learning includes the steps of preprocessing the obtained functional magnetic resonance imaging (fMRI) images and diffusion tensor imaging (DTI) images, registrating the preprocessed fMRI image to the standard AAL template, carrying out a fiber tracking for preprocessed DTI images, calculating fiber anisotropy (FA) value, and constructing structure connection matrix through the AAL template. Clustering coefficient of each brain area in a function connection matrix and the structure connection matrix is calculated to be regarded as function features and structure features. As two different tasks, the function features and the structure features assess an optimal feature set by solving the problem of multi-task learning optimization. The method uses information with multiple modalities complementing each other to learn simultaneously and to classify, improves the classification accuracy, solves the problems that a single task feature does not consider the correlation between features, and the fact that only one modality feature is used for pattern classification can bring to insufficient amount of information.

The invention provides an electroencephalogram signal characteristic extracting method. Network average route lengths and clustering coefficients are calculated through wavelet reconstruction, windowing horizontal visibility map complex network conversion and complex network analysis. The average route lengths and clustering coefficients composed of electroencephalogram signals are calculated to achieve characteristic analysis of electroencephalogram signals and chaotic time sequence signals of the electroencephalogram signals of different rhythms. The electroencephalogram signal characteristic extracting method has the advantages that one-dimensional chaotic time sequences are converted into complex networks, according to analysis of network characteristic parameters, fractal characters of the electroencephalogram signals are revealed, the complex non-linearity signals of the electroencephalogram signals are depicted from a brand new angle. The electroencephalogram signal characteristics can be applied to automatic diagnosis of mental disease and a characteristic identifying module of a brain-machine port system. The electroencephalogram signal characteristic extracting method can effectively distinguish the electroencephalogram signals of an epilepsia attach stage and an epilepsia non-attach stage, the equation p<0.1 is met after Mann-Whitney detection, and the electroencephalogram signal characteristic extracting method can be applied to epilepsia electroencephalogram automatic identification.

The invention discloses a method for identifying key proteins in a protein-protein interaction network. According to the method, an undirected graph G is constructed according to the protein-protein interaction data, and the edge clustering coefficient of the graph is calculated. Compared with the prior art, the method provided by the invention has the advantages of combining the gene expression profile data and the gene function annotation information data on the basis of considering the topological structure characteristics of the protein-protein interaction network, and integrating three groups of data to predict the key proteins, so that the influence caused by the data noise of a single data source on the prediction correctness can be effectively decreased, and the key proteins in the network can be predicted through the key protein characteristics embodied by three types of data, such as the edge clustering coefficient in the protein-protein interaction network, the Pearson's correlation coefficient of the gene expression value and the gene function similarity index. According to the method, the identification correctness of the key proteins in the protein-protein interaction network can be remarkably improved, and abundant key proteins can be predicted once, so that the problem that the biological experiment method is high in cost and time-consuming is solved.

The invention provides a complex weighted traffic network key node identification method based on semi-local centrality, comprising the following steps: S1, constructing a complex weighted traffic network: constructing a traffic network by adopting an original method, and taking road sections as nodes and road sections as edges; generating a corresponding adjacent matrix; according to the road grade, obtaining a weighted adjacency matrix through the adjacency matrix; S2, processing the weighted adjacency matrix, and analyzing network characteristics: calculating degree distribution of nodes, calculating an average clustering coefficient, and calculating an average path length; analyzing network characteristics according to the degree distribution of the nodes, the average clustering coefficient and the average path length; S3, identifying key nodes by adopting a semi-local centrality algorithm; and S4, sorting the key nodes of the traffic network: sorting the nodes in a descending order according to the importance degree to obtain the key nodes in the traffic network. The road grade is used as the weight, and the semi-local centrality algorithm is adopted, so that the problems thatthe key node identification calculation complexity of the existing traffic network is high and the traffic network characteristics are not considered are solved.

The invention provides a hybrid clustering method aiming at a complicated network, and belongs to the field of data mining of a community network. The method comprises the following steps of: partitioning the complicated network by a heuristic clustering method, and setting an optimization terminating function and an iterative terminating threshold value to obtain a relatively good network community division result; combining the networks by an optimization-based clustering method, and storing an optimal network community division result by calculating a final optimization terminating function; and finally outputting a community structure. By the method, a large-scale complicated network can be subjected to community division or clustering, so that a community division result is good; furthermore, the Girvan-Newman (GN) algorithm precision based on connection clustering coefficients is improved, and the time loss is avoided; and high user experience can be guaranteed.

The invention relates to an edge clustering coefficient-based social network group division method. Specifically, the method comprises the following steps: reading the social network data; constructing a social network planning which takes social network users as nodes and takes user relationships as edges; randomly endowing each user with a unique label value; updating the labels of the user nodes by adopting an edge clustering coefficient-based label propagation algorithm; and after several iteration, owning, by the tightly connected nodes, same specific label value. By adopting the social network group division method, the user groups are divided through improving the label propagation algorithm according to the edge clustering coefficient attribute of the user relationship graph, and the division result has preferable application value for the monitoring of network public opinions and the searching of commercial customers.

The invention discloses a brain age prediction method based on a brain connection network. The method comprises the following steps: firstly, a function connection network is extracted from a brain image; secondly, a clustering coefficient of a network node is calculated as a node feature; then, LASSO is used for carrying out feature selection on weights of edges, and edges without determination are eliminated; then, the node feature and the feature of the edge with determination are fused as a fused feature; and finally, a supper vector regression (SVR) is used for predicting a brain edge. The brain age prediction method based on the brain connection network provided by the invention has the advantages that features can be effectively extracted, and the regression effects are good.

The invention relates to a link prediction method and device based on node similarities; the method comprises the following steps: carrying out node and link expression for a to-be-parsed network; obtaining two nodes having no direct link connection in the network; respectively reading a neighbor node set of the two nodes having no direct link connection; obtaining the intersection of the neighbor node sets of the two nodes having no direct link connection, thus obtaining the common neighbor set; considering the common neighbor set as a subnet, and calculating the subnet global cluster coefficient and a random common neighbor node cluster coefficient in the subnet; calculating the node similarity of the two nodes having no direct link connection according to the subnet global cluster coefficient and the random common neighbor node cluster coefficient in the subnet; carrying out link prediction according to the calculated node similarity. The method can parse the mutual relation of the nodes in the complex network local structure from the cluster coefficient angle, thus defining the node similarity calculating new index based on the local cluster coefficient.

The invention relates to a priori knowledge based microblog user group division method. The method specifically comprises the steps of reading social network data; constructing a social network graph taking social network users as nodes and user relationships as edges; constructing a user similarity matrix; when labels of user nodes are initialized, giving the same labels to the nodes with high similarity, and updating the labels of the user nodes by adopting a label propagation algorithm; in a label propagation process, when a plurality of labels with highest frequencies exist in neighbor nodes of the updated nodes, randomly selecting a label with highest frequency to the label of the corresponding node; and after multi-step iterative updating, ensuring that the closely connected nodes have the same specific label values. According to the social network group division method provided by an embodiment of the invention, user groups are divided by improving the label propagation algorithm according to edge clustering coefficient attributes of a user relationship graph, and a division result has relatively high application values for network public opinion monitoring, commercial user mining and the like.

The present invention relates to a user interest mining method based on complex network characteristics and neural network clustering. The method is designed mainly from the following three aspects: 1) establishing a complex network model; 2) determining a node importance index; and 3) carrying out neural network clustering. According to the method disclosed by the present invention, the complex network model is used in mining user software interests, a complex network of software used by a user is established, a node degree, a clustering coefficient, a node betweenness and a node probability characteristic index are used to determine an importance rank of nodes so as to form a user interest set, and then a neural network model is used for mining interest set clustering and determining a final user interest set, so that a new method is provided to mine a user interest model, accuracy of mining user software interest is significantly improved, and a plurality of interests of the user can be mined by using the method.

The invention discloses a key protein predicating method based on priori knowledge and network topology characteristics. Based on the analysis of the topological relation between known key proteins, the close relation between key proteins is found; edge clustering coefficients are taken as parameters for assessing the close degree of two key proteins; and parts of known key proteins and the shared-clustering coefficients of neighbor nodes and the known key proteins are utilized for predicating new key proteins. The key protein predicating method is simple to implement and unknown key proteins can be accurately predicated just according to PPI (protein-protein interaction) information and information of parts of known key proteins, the method is applicable to not only non-weighted PPI networks but also weighted PPI networks, and the problems such as high cost and time wasting of a chemical experiment method are solved.

A marine observation big data visualization analysis method based on a complex network comprises the steps of performing grid division on original marine observation big data, constructing daily average data in a grid into a single Gaussian model and a mixed Gaussian model, and obtaining nodes represented by probability feature vectors; Determining the similarity between any two nodes in the single Gaussian network and the multi-Gaussian network to obtain a similarity matrix; And setting a threshold value to obtain an adjacent matrix, calculating the degree, the clustering coefficient and thenode betweenness of each node according to the adjacent matrix, and visualizing or drawing the degree, the clustering coefficient and the node betweenness on double logarithm coordinates or on a map.According to the invention, the Gaussian mixture model is combined with the complex network theory for the first time; The invention provides a marine observation big data analysis and visualization method, the fluctuation of ocean motion reflected on the data is restored to the maximum extent, and model parameters are used for expressing high-dimensional ocean data, so that the defect that a network model constructed on the basis of Pearson similarity can only measure time sequence data is overcome, and the calculation speed is also improved.

The invention relates to a clustering and multi-hop communication method of network nodes of a wireless sensor, in particular to a clustering and multi-hop communication method of a wireless sensor network based on a small-world model. The algorithm utilizes the small-world model to cluster the sensor network, which improves the clustering coefficient at the network side and enables that the sensor network has favorable performances of small-world network and large clustering coefficient; nodes in each cluster and average hop numbers are restricted so that the load of each cluster can be balanced; and the node with largest residual energy can take turns a cluster head so that the energy of all nodes can be oppositely balanced. The invention can balance the energy consumption of the networknodes in the whole situation, avoids the situations that the network is partitioned into a plurality of complementary connections as a part of nodes are premature failure, prolongs the life cycle ofthe whole network and improves the robustness of the network simultaneously.

The invention discloses a prediction method for disease dynamic states on the basis of a network structure. The prediction method comprises the following steps: firstly, analyzing a great quantity of spatio-temporal data collected by mobile equipment carried by a volunteer in an epidemic situation region to evaluate influence of the network structure on the disease dynamic states; adopting three features including a clustering coefficient, degree distribution and degree correlation to describe the network structure; then, on the basis of an evaluation result, designing a structure identification model to identify a dynamically changed network structure; and finally, according to a relationship model of disease dynamic property and the network structure, predicting the dynamic change of a disease. According to the method, disease prediction accuracy can be effectively improved, and the method has an important significance for infectious disease control and prevention.

The invention discloses a protein biological network motif identification method integrating topological attributes and functions. The protein biological network motif identification method integrating the topological attributes and functions (Ecc-GOSS) is based on the biology significance of a motif, and comprehensively evaluates the biological significance of protein-protein interaction by integrating edge clustering coefficients and semantic similarity of GO phrases. The protein biological network motif identification method integrating the topological attributes and functions is easy to implement, a great amount of network motifs with biological significance can be identified accurately according to PPI information and gene ontology information, and the protein biological network motif identification method integrating the topological attributes and functions has good robustness on high-percentage false positive prevailing among the large-scale data of the protein-protein interaction.

The invention discloses a hierarchical clustering-based suspicious taxpayer detection method. The hierarchical clustering-based suspicious taxpayer detection method includes steps of 1) according to interest relationship between the taxpayers, performing clustering based on a hierarchical clustering algorithm, and according to association degrees between the taxpayers, dividing the taxpayers into clusters which are interest associated societies; 2) with analysis of marked taxpayer sample data, extracting distinction features between the normal taxpayers and the abnormal taxpayers based on complex network indicators, such as degree distribution and clustering coefficient; and 3) calculating feature similarity degree between identified interest associated societies and the normal taxpayers, as well as between the identified interest associated societies and the abnormal taxpayers, and further detecting the suspicious taxpayers.

An oil-water phase content measurement method based on a multivariate phase space complex network and a verification method thereof are provided. The measurement method includes the steps: building the multivariate phase space complex network; calculating oil-water two-phase flow parameter measurement information; drawing a measurement chart of linear relationships of the interactive clustering coefficient and the interactive transitivity with the oil content, and realizing the measurement of the oil content of a horizontal oil-water two-phase flow; and according to the drawn measurement chart, analyzing the relationships of interactive clustering coefficient and interactive transitivity network subprocess quantitative characteristics with flow pattern evolution dynamics, and revealing a horizontal oil-water two-phase flow flow-pattern evolution dynamics mechanism. Verification is carried out by a four-sector distributed conductance sensor. The invention provides the multivariate phase space complex network method for information fusion of measurement signals of the two-phase flow distributed conductance sensor, and different oil-water two-phase flow patterns can be effectively identified; and the multivariate phase space complex network information fusion method based on the four-sector distributed conductance sensor can obtain a good oil content measurement effect.

A vertical oil-water phase content measurement and verification method based on frequency complex network is as below: constructing a frequency complex network; calculating parameter measurement information of a vertical oil-water two-phase flow; mapping an average frequency clustering coefficient, an average frequency betweenness and a transitivity and oil content linearly relation measurement plate, so as to achieve the measurement of oil content of the vertical oil-water two-phase flow; and analyzing the dynamic relationship of average frequency clustering coefficient, average frequency betweenness and transitivity with flow pattern evolution according to the measurement plate, in order to reveal the flow pattern evolution dynamic mechanism of the vertical oil-water two-phase flow. The verification method employs a multi-electrode distributed conductance sensor for verification. The invention conducts information fusion on the measuring signals of the two-phase flow multi-electrode distributed conductance sensor; a multivariate time series frequency complex network information fusion method can effectively identify different flow patterns of the oil-water two-phase flow; and the multivariate time series frequency complex network information fusion method based on the multi-electrode distributed conductance sensor can realize good effect of phase content measurement.

The present invention discloses a group degree based sorting method and model evolution method for important nodes on complex network. The method comprises: first, obtaining each order of group degree of each node on a complex network; then calculating each order of overall group degree of the complex network; normalizing each order of overall group degree; calculating a weight of each order of group degree according to a normalization result; finally, each node performing weighting on each order of group degree of the node according to the weight, wherein a result is an importance value of the node; and sorting each node according to the importance value. During model evolution, each time a new node is added, a connecting node of the new node is selected according to the importance values of existing nodes. According to the sorting method and model evolution method provided by the present invention, the importance value of the node is calculated based on the group degree, the obtained node importance sequence is better in line with the actual situation of the network, and the average path length and cluster coefficient property obtained through model evolution both have obvious advantages.

The invention discloses a complex network theory-based water route vulnerability research method. The complex network theory-based water route vulnerability research method comprises an implementationscheme and a research method, wherein the research method comprises a complex network theory and numerical calculation; and the complex network theory comprises a basic conception and feature parameters. According to the method, a ship route network model is established; and MATLAB software is utilized to calculate feature parameters of a complex network, wherein feature parameters comprise degree distribution, a clustering coefficient, a relative size of a maximal connected subgraph, an average efficiency of the network and a tightness index. The numerical calculation pays attention to the stability of calculation, is capable of controlling the growth momentums of errors and ensuring the stability of the calculation process, and pays attention to high calculation speed and high calculation precision, so that the numerical calculation is capable of correctly judging the property of the network and has reliability for the researches.

The invention provides a software defect measuring method based on a complex network. The software defect measuring method based on the complex network can predict defects existing in a software system, so that influences in the future are prevented. The software defect measuring method based on the complex network comprises the following steps that first, a system class diagram is generated reversely according to a system executable file; second, the obtained system class diagram is converted into a network diagram of a software structure, wherein classes represent nodes, and relations between the classes represent edges; third, analysis at the level of the complex network is conducted according to the obtained network diagram, and the average shortest distance, an access degree and a clustering coefficient in complex network parameters are calculated by means of complex parameters, and a complex characteristic measuring value of software is obtained; fourth, a hierarchy measuring system is imported according to an object-oriented level, and an object-oriented characteristic measuring value of the software is obtained; fifth, the complex characteristic measuring value obtained in the third step and the object-oriented characteristic measuring value obtained in the fourth step are contrasted with known standard values, an assessment is conducted, and a defect measuring result prediction about the analyzed software is obtained finally.

The invention discloses a clustering method, a system and a medium for automatically confirming the number of clusters based on a coefficient of variation, wherein, the density value of each data point in a data set is calculated, the density index is calculated according to the density value, and the data point with the largest density index is selected as a first clustering center; Calculating the shortest distance between each data point and the existing clustering center, calculating the probability that each data point is selected as the clustering center according to the shortest distance, and preselecting the clustering center according to the roulette disc method; Until the set cluster centers are selected, the initial cluster centers selected are used for k-means clustering to generate a corresponding number of clusters; Calculate the average intra-cluster coefficient of variation and the minimum inter-cluster coefficient of variation, then calculate the difference between theaverage intra-cluster coefficient of variation and the minimum inter-cluster coefficient of variation, compare the difference with the set value, and if the difference is less than the set value, merge the two clusters with the minimum inter-cluster coefficient of variation; Until the difference is greater than or equal to the set value, the clustering result is output.

The invention provides an energy consumption calculating and scheduling method of an urban rail traffic system. The method includes the steps of establishing a multi-layer network model of the urban rail traffic energy consumption system on the basis of the urban rail traffic system; calculating topological properties and data properties of nodes as input, wherein the topological properties include the degrees, betweenness and clustering coefficients of the nodes, and the data properties include the energy consumption and strength of the nodes; fusing the topological properties and data properties of the nodes through an OWA operator to obtain the weights of the nodes in the urban rail traffic system; calculating the energy efficiency emergence state, namely the energy efficiency representation parameter, of the urban rail traffic system in combination with the weights of the nodes through Choquet integrals on the basis of the emerging theory; regulating and controlling the energy efficiency of the urban rail operation system on the basis of the energy efficiency representation parameter of the urban rail system. For the complex-structure urban rail traffic system, the energy efficiency of the urban rail system can be calculated by means of the method, and the reasonable regulation and control of the energy efficiency of the urban rail operation system are realized in combination with the calculated energy efficiency.

The invention provides a behavior prediction method for a network attack knowledge graph generated based on honeypot actual combat, and the method comprises the steps: deploying a certain number of network nodes and binding honeypots, and then collecting and recording the information of a network attack behavior, an attack path and an attack object; and then, according to the attack information, establishing an actual combat knowledge graph of network attack and defense through Neo4j. On the basis of the built knowledge graph, the relevance of attack events is analyzed through a clustering algorithm, then the attack events are analyzed and predicted through the obtained average clustering coefficient, effective classification display and behavior prediction can be conducted on network attack and defense, and certain guiding significance is achieved for network attack and defense.

The invention discloses a multivariable causal-driven complex electromechanical system service security situation assessment method, and belongs to the field of complex electromechanical system service process state evaluation. The method comprises the following steps: firstly, extracting state monitoring data in a complex electromechanical system service process, preprocessing the state monitoring data, calculating a causal measure value between monitoring variables by using a GPDC method, and constructing a causal network topology model capable of reflecting a system state evolution process;extracting features of three dimensions of an average path length, a clustering coefficient and a network structure entropy based on the established causal network model; and reconstructing an abnormal state space of the system according to the extracted abnormal fluctuation information of the three features, normalizing abnormal over-limit indexes on the three dimensions, and effectively evaluating the service state of the complex electromechanical system by using the normalized abnormal indexes.