591 results about "Community structure" patented technology

The invention relates to the technical field of a social network and particularly relates to a multi-label propagation discovery method of overlapping communities in the social network. The multi-label propagation discovery method comprises the following steps: reading data of the social network, constructing a social network diagram which adopts social network users as nodes and user relationship as edges; according to the social network diagram, carrying out preliminary community division of the social network, and carry outing community discovery by adopting a label propagation method of comprehensively considering the node centrality and label-degree distribution constraint to obtain a non-overlapping community structure; marking the levels of the nodes according to the obtained non-overlapping community structure and the centrality value of the nodes in the communities; and according to the levels of the nodes, calculating label propagation gain among the nodes with different levels, and carrying out overlapping node mining by utilizing the multi-label propagation to obtain the overlapping community structure of the social network. The multi-label propagation discovery method has the advantages that the overlapping community structure in the social network can be effectively mined, the accuracy and the efficiency of community detection are favorably improved, and the method can be applicable to the fields of target group mining, precision marketing and the like.

The invention discloses a method for removing water eutrophication, which comprises the following steps of: investigating a community structure of aquatic organisms in water body, and finding a missing link or broken link in a food chain according to an investigation result; stocking predacious fishes in the water body, removing small trash fishes and herbivorous fishes from the water body by adopting a medicament mode or a manual fishing mode, and regulating the community structure of the fishes in the water body; planting submerged plants, floating plants and emergent aquatic plants in the water with the depth of 0 to 1.5m; and stocking filter-feeding fishes, filter-feeding shellfishes, detritus feeding benthonic animals and herbivorous fishes in the water body. The method for removing water eutrophication can realize the removal of the water eutrophication and ecological restoration for large-scale water body of which the parameters cannot be manually controlled, effectively inhibit the outburst of algal bloom and keep the stable quality of the water body.

The invention discloses a method for partitioning communities in a complex dynamic network by virtue of multi-objective local search based on decomposition, and the method is mainly used for solving the problem of poor community partitioning accuracy in the course of processing the complex dynamic network in the prior art. The method is implemented through the following steps: (1) determining objective functions; (2) constructing an initial solution population, and initializing individuals in the solution population by a neighborhood real-number encoding method; (3) sequentially selecting the individuals from the solution population and then carrying out cross variation on the individuals to obtain progeny individuals; (4) updating the solution population by virtue of the progeny individuals; (5) locally searching and updating the solution population; (6) judging whether the population evolution process is terminated: if iterations reach the preset times, executing a step (7), otherwise, transferring to the step (3); and (7) selecting the optimum community partition according to the maximum module density principle. The method disclosed by the invention has the beneficial effects that two objective functions can be optimized at the same time, synchronous analysis of community partition and community evolution is realized, the community partitioning accuracy is improved, and the problem of detection of a community structure in the complex dynamic network can be solved.

A method and an apparatus for identifying sub-graphs similar to a sample sub-graph from a network, in particular for identifying similar community structures from a network, are disclosed. Specifically, when a sample sub-graph is a community structure, it usually contains a plurality of nodes densely associated with each other. The method proposes first screening the network via key nodes of community structures so as to find sub-graph candidates that are potentially similar to the sample sub-graph, then strictly comparing the sub-graph candidates with the sample sub-graph to find similar sub-graphs of the sample sub-graph.

The present invention discloses a weighted network community clustering method based on hybrid measure to dig the clustering relationship of nodes in a large complex network. The method comprises a step of introducing a new node intimacy definition for measuring the association intensity between the nodes in a directed weighted network, a step of carrying out weighted processing on the side of a directed / undirected network through newly defined node intimacy, and a step of providing a modular new definition based on the node intimacy and using the hybrid measure to carry out hierarchical community structure detection on the directed / undirected network. Compared with the traditional community structure detection method, node relation information which can be referred in the communication division is increased by the hybrid measure, the quality of the communication division is improved, and the scale of an ultra large community is reduced. At the same time, the method provides a unified analysis method for the communication division of undirected unweighted, directed unweighted, undirected weighted and directed unweighted networks.

The invention discloses a social network influence maximization method based on a community structure. The method comprises the following specific processes that: (1) dividing a community, forming a candidate node set, dividing a network to identify a kernel node and a boundary node in the network, and forming the candidate node set; (2) heuristically selecting nodes, and for each node in the candidate node set, verifying potential influence through a node degree, a community scale, a connected community number and influence weight so as to heuristically select and add the node with the highest influence into a seed set; and (3) executing a greedy algorithm, and utilizing the greedy algorithm to select and add the node with the highest marginal income into the seed set. By use of the method, the function of the community structure in influence spreading is analyzed to further improve accuracy and operation efficiency for mining initial seed nodes, and the problem of social network influence maximization is effectively solved.

The invention relates to an environment DNA identification method for fish community structure researching. The method is characterized by comprising the steps that 1, water samples are collected according to the size of the water area where a fish community is located; 2, the water samples are processed, and total environment DNA of the processed water samples is extracted; 3, a 16s r DNA sequence with nucleotide sequences being SEQ ID NO:1 and SEQ ID NO:2 is adopted as a universal primer to carry out PCR amplification on the total environment DNA, and a PCR product is obtained; 4, gel electrophoresis is carried out on the PCR product, and gel with DNA is obtained; 5, gel cutting and clone sequencing are carried out on the gel with the DNA, and then blast comparison is carried out through GENBANK so as to determine whether the DNA is a fish sequence or not; 6, after it is determined that the DNA is the fish sequence, the next-generation sequencing technology is adopted for analyzing composition conditions of the PCR product on a large scale, and therefore the environment DNA fish composition and community structure are determined. The environment DNA identification method is quite easy, convenient and efficient and has the practical value.

The invention provides a method for discovering a community structure oriented to a directed-weighting network. The method comprises the following steps: constructing the directed-weighting network based on the inter-node social relationship weight and direction; converting the directed-weighting network into an undirected-weighting network; initializing a network topology, and constructing a weight matrix; normalizing the matrix and calculating community discovery decision factors which specifically comprise node liveness, community relationship intensity, community density and community coupling degree; partitioning the network community based on the community discovery decision factors. The method can be used for rapidly discovering and construction the network community under a mobile, hetergeneous and dynamic environment, providing credible service links for users, realizing mobile sensing service credibility interaction, further improving the mobile sensing service and finally providing high-quality, diversified and customized group intelligent sensing service for the user.

The invention relates to the technical field of social networks, in particular to a dynamic community detection method in the social network. The method comprises the steps that a snapshot of a certain time slice (moment) of the social network is obtained for the social network changing dynamically, and then a social network chart is constructed; community division is carried out on the social network chart of the initial moment, and the snapshot of a subsequent certain time slice (moment) of the social network is compared with the snapshot of a former moment to find an increment node set; the proportion occupied by increment nodes is calculated, if the proportion of the increment nodes exceeds a specified threshold value, community division is carried out on a complete snapshot network, and otherwise, community division is carried out on the increment node set, and then the community structure of the snapshot of a certain time slice (moment) of the social network is obtained. The method can effectively explore the community structure in the social network and can be applied to the fields of target group mining and precision marketing.

The invention provides an activeness and cluster structure analyzing system based on network topics. The activeness and cluster structure analyzing system based on the network topics comprises a data acquiring and normalizing module, a data storage module, an application analysis module and an user interaction and display module; the user interaction and display module provides an interface for realizing user interaction and displaying data analysis results; the data acquiring and normalizing module is used for receiving an URL (Uniform Resource Locator) specified by a user and acquiring and normalizing the network data by virtue of a network data crawling subunit and a webpage data normalizing subunit; the data storage module is used for storing the normalized webpage data and providing analysis data to the application analysis module; and the application analysis module is used for deeply mining the activeness of the topics and a community structure based on the webpage clustering and hot spot mining, and showing the results to the user through the user interaction and display unit. By adopting the activeness and cluster analyzing system and the activeness and cluster analyzing method based on the network topics, the limitation due to that single detection approach is adopted in the internet public opinion system field, and the webpage content mining cannot be carried out, is solved; and the problems due to cluster structure mining in webpage information analysis and detailed information mining in state evaluation can be solved well.

Provided is a method for carrying out community detection on a heterogeneous social network on the basis of a clustering algorithm. The method comprises the steps that an adjacent matrix is built; the community structure is initialized; the local modularity is calculated; a set of mark numbers of communities participating in fusion is obtained; candidate fusion sets are obtained; differences of the modularity are calculated; whether the first modularity difference and the second modularity difference meet the fusion standard or not, if yes, the mark numbers of the communities participating in fusion and the mark numbers of candidate communities are unified, and if not, the step of calculation of the local modularity is executed again; a new community structure is recorded; if community merging does not exist in the current cycle, the optimal community structure is output. According to the method for carrying out community detection on the heterogeneous social network on the basis of the clustering algorithm, due to the fact that the clustering method, the similarity vector method and the local modularity method are adopted, the methods can be effectively applied to community detection of the heterogeneous social network, and accuracy of the detection result of the heterogeneous network community structure is improved.

The invention discloses a method for adjusting and controlling microbial enhanced oil recovery, which comprises the following steps: (1) analyzing the microbial community structure in produced fluid of an oil reservoir via molecular biological method and / or detecting the metabolites in the produced fluid; (2) adjusting the microorganism(s) to be injected into the oil reservoir and / or the nutrient system corresponding to the microorganism(s); (3) injecting the adjusted microorganism(s) and / or the nutrient system corresponding to the microorganism(s) into the oil reservoir through a water injection well; and (4) obtaining the crude oil from a corresponding beneficial oil producing well. Compared with the prior art, the method of the present invention adjusts microbial community structure in the oil reservoir to evolve toward the direction of facilitating oil production, and the performance of the functional microorganism(s) can be completely realized; the nutrient system is pertinently injected to avoid the blindness of using the nutrient system. Therefore, the method is scientific, economical and effective for the microbial enhanced oil recovery.

The invention discloses a method for improving the recovery ratio of indigenous microbial enhanced oil recovery. The method comprises the following steps: acquiring a fluid sample on a target reservoir site; analyzing an indigenous microbial community structure; determining an indigenous activator and the injection quantity thereof; selecting an on-site injection process of the indigenous activator; evaluating a physical analog enhanced oil recovery; performing on-site tests, and tracking and analyzing of on-site test effects. The method is reasonable, simple in process, easy to operate, safe and reliable, beneficial to on-site popularization and application, and low in investment and cost, greatly improves the on-site test effect, improves the recovery ratio of more than 12% during indigenous microbial enhanced oil recovery on-site tests, and thus can be widely applied to on-site tests for improving the recovery ratio of indigenous microbial enhanced oil recovery.

The invention provides a massive email forensic analyzing scheme based on community network analysis and file content analysis, which solves problems that a scheme capable of realizing analysis of a community structure by utilizing receiving and sending addresses and forensic analysis by utilizing text and attatchment contents does not exist at present. The method comprises the following steps of: automatically analyzing an email data file of an Outlook client, an OE (Outlook Express) client and a Foxmail client; extracting the receiving and sending addresses, the attachment, the text, a theme and a sending mode and other information contained in the email data file; extracting an email communication relationship network built based on the receiving and sending addresses of the email and receiving and sending times; extracting the community structure of the communication relationship network through a WCMN (weighing cepstral mean normalization) algorithm; and then analyzing and calculating a suspicious degree of a community through a suspicious degree computational algorithm based on contents of the email and the attachment. According to the scheme, address information and content information of the email are fully utilized; and the community structure contained among email addresses is extracted; and the suspicious degree is provided for personnel handling cases for reference.

The invention provides a method for restoring a slash type degraded wetland. The degraded wetland is restored by adopting one or more of a hydrological connection method, a microrelief transformation method, a matrix recovery method, a vegetation recovery method and a riparian zone slope protection method. According to the specific circumstance of the degraded wetland, the method can comprehensively utilize the whole technical system, and also can separately utilize certain technology. The method improves the landform, a water system, a wetland matrix and the riparian zone environment of the degraded wetland through ecological engineering measures so as to reasonably arrange wetland vegetation from land to open water, construct an optimal wetland vegetation community structure, and effectively improve the stability and the self-maintenance of the degraded wetland.

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 present invention discloses a privacy protection method for community detection application-oriented social network data publication. The method comprises: upon performing initialization and community detection for data, sequencing nodes in a network graph; forming the data into super nodes by means of K anonym, and generalizing edges into super edges having weights; and splitting the super nodes and the super edges; and publishing the anonymous social network data. According to the present invention, during data publication, while the anonym requirements are ensured, structural information of the community structure of the original image is better saved, which is favorable to related analysis on the community by the data analyzer.

The invention discloses a Binjiang touring type wetland revegetation mode structure. The Binjiang touring type wetland revegetation mode structure is characterized in that in a river bank, an aquatic plant purifying region is arranged below a high water level, a water edge soil fixation and erosion preventing region is arranged above the high water level, and an overwater forest recovery region is arranged on one far-water side outside the erosion preventing region. In the aquatic plant purifying region, aquatic floating-leaved plants are planted below a normal water level in a naturally scattered growth and close planting mode, and high-stalk emerged plants are planted above the normal water level and below the high water level in a naturally scattered growth and close planting mode. The water edge soil fixation and erosion preventing region is paved with slope protection bricks and the width of the water edge soil fixation and erosion preventing region ranges from 2m to 3m. The overwater forest recovery region is of an ecological simulation community structure formed by natural mixing of wetland trees and shrubs to achieve height matching, color combination, advantage and disadvantage complementation and calobiosis. Touring footpaths and seats for rest are naturally arranged in the river direction in communities. Through meticulous engineering design and scientific plant matching, the Binjiang touring type wetland revegetation mode structure achieves perfect combination of Binjiang hydraulic engineering and wetland revegetation, is important for river bank wetland soil fixation and erosion preventing and environment improvement, and has the advantages of being ecological, beautiful and multifunctional and increasing effect.

The invention discloses a method for determining absolute abundance of a microbial community structure in an environment. The method comprises the following steps: extracting DNA in an environmental sample, obtaining a DNA sample, and determining a total concentration of classification genes in the DNA sample through a real-time fluorescent quantitative PCR (Polymerase Chain Reaction) method; obtaining the relative abundance of the classification genes in microbes of different kingdoms, phyla, classes, orders, families, categories and species through a second-generation high-throughput sequencing method; and finally, multiplying the total concentration of the classification genes in the DNA sample by the relative abundance of the classification genes in the microbes of different kingdoms, phyla, classes, orders, families, categories and species, and calculating to obtain the absolute abundance of the classification genes in the microbes of different kingdoms, phyla, classes, orders, families, categories and species, so that rapid high-throughput determination of the absolute abundance of the microbial community structure is realized. Therefore, cross-group sample comparison is reliable, growth and decline of the species can be substantially reflected, and a technical support is provided for further exploration of the microbial community structure.

The present invention relates to a restoration method of a degraded ecosystem in hilly areas of Funiu Mountain in Western Henan Province in the technical field of ecological restoration. The restoration method comprises the following steps of selecting appropriate tree species, shrubs and grass species, reasonably performing structure configuration, and effectively restoring the ecosystem in a short time. According to the restoration method, regional vegetation coverage rate is improved by 20-40%, water and soil erosion is reduced by more than 30%, and soil nutrient accumulation is increased by 8-10%. The forest and grass ecosystem is reasonable in structure. Through screening and optimizing, a robinetinidin & medicago sativa and quercus variabilis & medicago sativa artificial forest and grass community structure mode which is high in water and soil conservation benefit is established, economic benefit per unit field area is increased by more than 25%, nitrogen fertilizer application mount is saved by 20%, and net income of farmers is improved by more than 15%. Ecological environment and climate in the experimental areas are largely improved.

The invention discloses a whitish soil improvement method. The method includes the steps that (1) rice straw returning or wheat straw returning is conducted on carclazyte soil; (2) after straw returning is conducted, organic fertilizer is applied in a combined mode, and nitrogenous fertilizer is added to adjust the C / N ratio of straw to range from 20:1 to 30:1; (3) deep tillage is conducted on the soil to enable silt on the surface of the soil and heavy loess at the bottom to be blended fully; (4) after deep tillage is conducted on the soil, irrigation field retting is conducted; (5) after deep tillage and field retting are conducted on the soil, rice base fertilizer is applied; (6) the steps are repeated many times, and the depth of deep tillage is increased year by year every time deep tillage is conducted. According to the method, the rice per unit yield and soil fertility are improved, the soil structure is improved, the cation exchange capacity of the soil is improved, and the community structure of soil microorganisms and the soil enzyme activity are affected.

The invention relates to a method for discovering topics of communities in an on-line social network. The method includes the specific steps that data acquisition is carried out on the object social network based on a web crawler; the relevancy of each user object in an interactive network topological structure is worked out based on an acquired interactive relationship between the user objects in the social network; a static interactive network of the user objects is constructed; a compact user community structure is obtained through hierarchical clustering according to the relevancy of each user object; for each community obtained through division, a database is searched to acquire text messages corresponding to the community, the text messages are input as documents and classified through an SVM, and the hot topics of the community are worked out. Compared with an existing method for discovering topics in an on-line social network, the method for discovering the topics of the communities based on community division has the advantages that noise data can be effectively removed, the more compact topics between the communities can be obtained, and a deeper understanding of information spreading laws of the social networks is facilitated.

The invention discloses an ecological community structure layout method of healthcare gardens. The ecological community structure layout method of the healthcare gardens comprises performing strict classification, precision matching and reasonable application according to growth characteristics, functions and usages of all kinds of healthcare plants, and respectively mass-planting the plants in healthcare gardens of different forms according to needs through scientific design, consummate layout and corresponding compatibility; meanwhile, selecting relevant arbors which are vigorous, good in stress resistance and have pharmacological actions as a frame and utilizing healthcare plants of different varieties, clear functions and high ornamental values as a main body to build an ecological plant community. According to the ecological community structure layout method of the healthcare gardens, aesthetics, medicine pharmacology and environmental psychology are used as guidance, sightseeing gardens with healthcare effects are built through the application of biological measures, the healthcare plants, gardening architecture articles and the like, positive significance on public health maintenance, recreation and body-building, disease prevention and cure, wisdom development, tumor suppression and cancer prevention, body health and longevity, physical and mental health and the like can be obtained, and also social significance of energy saving and emission reduction, low carbon and environmental protection and ecological civilization promotion can be achieved.

The invention relates to a culture-free biochemical method for analyzing the microbial community structure of yeast for traditional brewage, in particular to a method for analyzing the microbial community structure of yeast for traditional brewage by using the phospholipid fatty acid spectrogram analysis technique. The method comprises the following steps: (1) weighting a certain amount of yeast sample which is pulverized and evenly mixed; (2) adding a buffer solution and an organic reagent to obtain an organic phase containing lipids; (3) separating the organic phase by small silica gel columns, eluting and collecting organic components containing polar lipids; (4) carrying out methyl esterification under mild condition, and adding an extracting reagent to obtain corresponding fatty acid methyl ester; (5) carrying out quantitative analysis on the fatty acid methyl ester by using a gas chromatogram-mass spectrogram combined instrument; and (6) determining the characteristics of the microbial community structure in the sample according to characteristics of different phospholipid fatty acid compositions. The invention provides an accurate and quick culture-free detection means for researching the composition of the microbial community structure of yeast for brewage and knowing the law of growth and decline of different microbial communities in the yeast preparation process.

The invention discloses a community discovery method based on label propagation with local influence. The method comprises the following steps that given complex network data is represented in the form of a graph model; a node sequence table is generated according to the degree centrality, and each node in the node sequence table is distributed with a unique label; local influence of each node inthe network on neighbor nodes is calculated; according to a made label update strategy, the labels of all the nodes are updated in a traversal way according to the sequence of the node sequence table;and when an iteration termination condition is satisfied, the nodes with the same labels are classified into the same community, and a network communication division result is obtained. A classic label propagation algorithm is improved in the aspects of the node label update sequence and the label update strategy, node labels are updated less randomly, a community division structure is more stable and accurate, and the method can be applied to the fields of network public opinion monitoring, information retrieval and e-commerce recommendation systems.

The invention discloses a large data intelligent analysis platform oriented to the field of social security, comprises a relational network analysis module, which is used for constructing one or morethematic databases according to multi-source data, constructing a network topology structure based on the thematic databases, obtaining labels of each node in the network topology structure, and mining key nodes and / or community structures in the network topology structure according to a set scene task; The trajectory behavior analysis module is used for acquiring different types of trajectory data, generating different types of trajectory models according to the trajectory data, and performing trajectory analysis based on various trajectory models; The video intelligent analysis module is used for obtaining the multi-scale features of pedestrians according to the set pedestrian detection and tracking algorithm, extracting the semantic features, gait features and visual features of pedestrians according to the multi-scale features, so as to identify and search the pedestrians. The invention can effectively improve the pre-warning capability and the pre-warning level, and provides powerful technical support for the emergency response.

The invention belongs to the field of biological agents and discloses triple probiotics viable bacteria powder obtained by whole oat mixed solid-state fermentation utilizing various probiotics direct-adding reagents. The triple probiotics viable bacteria powder contains bifidobacterium lactis V9, lactobacillus plantarum P-8 and lactobacillus casei Zhang, and is prepared by taking whole oat powder as a fermentation substrate, and has excellent gastrointestinal tract digestive juice endurance capacity, is capable of improving a bacterial community structure of human intestinal tracts, capable of regulating blood lipid metabolism and capable of improving pathogenic infection resistance of intestinal tracts, and effectively prevents and relieves irritable bowel syndromes such as diarrhea, constipation, stomachache, abdominal distension.

A method and an apparatus for identifying sub-graphs similar to a sample sub-graph from a network, in particular for identifying similar community structures from a network, are disclosed. Specifically, when a sample sub-graph is a community structure, it usually contains a plurality of nodes densely associated with each other. The method proposes first screening the network via key nodes of community structures so as to find sub-graph candidates that are potentially similar to the sample sub-graph, then strictly comparing the sub-graph candidates with the sample sub-graph to find similar sub-graphs of the sample sub-graph.

A label propagation community structure mining method based on a node membership degree comprises the steps that a unique label is given to each node in a network, and each label is used for representing a community to which the corresponding node belongs; row vectors in a complex network adjacent matrixes are seen as sampling samples of the nodes, and a weight coefficient between two nodes is used as edge weight value; the variance of the node connecting edge weight coefficients is utilized as the node membership degree; in each label updating iteration, only the node labels with the membership degrees larger than the label updating threshold value are updated, and the nodes with the membership degrees smaller than the label updating threshold value are used as overlapping nodes; if the labels are changed, or the label propagation frequency is smaller than the label iteration threshold value, the iteration process is repeatedly executed, and if not, updating is stopped. The label propagation community structure mining method can well detect the overlapping community structure of the complex network under the situation that the time complexity increase is not large, and has the good robustness and accuracy.