96 results about "Community structure" patented technology

The invention relates to the technical field of electric power engineering information processing, and discloses a power grid power transformation engineering knowledge graph construction and retrieval method and system, and the method comprises the steps: carrying out the dynamic adaptive partitioning of a power grid power transformation engineering related document, and obtaining semantic coherent and independent text blocks; extracting entities and relationships based on the text blocks, and complementing implicit entities and relationships through a multi-round refining mode; performing fusion and disambiguation on the extracted and complemented entities and relationships to form a unified knowledge graph; performing hierarchical clustering on the formed knowledge graph to generate a multi-granularity community structure and a corresponding community report; intention resolution and pre-judgment guidance are carried out aiming at fuzzy questions of the user, and a retrieval strategy is optimized; and executing multi-hop semantic retrieval based on the optimized retrieval strategy, recalling related knowledge and generating answers. According to the method, automation, precision and intelligentization of power grid power transformation engineering knowledge graph construction and full-link retrieval can be realized.

The invention relates to the technical field of urban geography, in particular to a crowd movement flow-based multi-scale community structure importance identification method, which comprises the following steps of: constructing a crowd movement space-time network according to a preset geographic space unit and a time interval on the basis of crowd movement starting place and destination data; identifying a dynamic community structure based on a crowd mobile space-time network; performing multi-scale community structure object role and importance identification; and performing pattern classification by using the time sequence importance of the multi-scale community structure object. According to the method, the problems of inaccurate dynamic community structure recognition and lack of multi-scale object contribution decoupling mechanisms are solved.

A method and system for attribute community search based on a relation tree are provided. In the method, a data relation of a target domain is obtained, and an attribute information network of the target domain is constructed in the form of a graph. The attribute information network is loaded over a unified access interface, and data processing, subgraph construction, and algorithm operation and maintenance search are performed on the attribute information network. A community structure parameter k value and a search node q are received through an interface for attribute community search provided for a user, and a community that contains the search node q and satisfies structural cohesion and optimal attribute cohesion is returned. Rendering and interaction are performed on a user interface, which renders the community search result returned into a dynamic community network diagram and presents the dynamic community network diagram to the user.

The invention relates to the technical field of attribute graph collection and release, in particular to an attribute graph collection and release method, system and device based on local differential privacy and a medium. Receiving disturbed data from each client; based on all disturbed data, estimating community division of the original graph structure and triangular counting of each node; obtaining a target graph structure based on community division and triangular counting; and on the basis of the target graph structure and the disturbed data, node attributes are optimized, so that node attribute distribution and community division are kept consistent, and a final publishable target attribute graph is obtained. The method has the advantages that strict requirements of local differential privacy are met on the privacy protection level, and multi-dimensional features such as a community structure, triangular counting and node attribute homogeneity are reserved in the published attribute graph at the same time; and the practical value of the synthetic data in applications such as downstream graph analysis, community discovery and personalized service is greatly improved.

The invention discloses an arid region small watershed vegetation ecological environment monitoring method comprising the following steps: obtaining an arid region small watershed earth surface image, processing the arid region small watershed earth surface image, and obtaining a first vegetation distribution image; acquiring a second vegetation distribution image according to matching of the first vegetation distribution image and a spectral feature library; extracting spatial distribution characteristics of a vegetation community from the second vegetation distribution image, calculating a dynamic change trend of the spatial distribution characteristics, and obtaining a community structure change data set; based on the community structure change data set, predicting a short-term evolution trend of the vegetation community structure by adopting a long-short-term memory network, and obtaining a dynamic change prediction result of the vegetation community structure; according to the dynamic change prediction result, combining the community structure change data set to quantify the biodiversity level to obtain a biodiversity evaluation result; key indexes are extracted from the biodiversity evaluation result, and vegetation ecological environment state data are monitored in real time according to the key indexes.

The invention discloses an intelligent decision-making method and system based on deep learning, and relates to the field of data processing. The method comprises the steps of obtaining place data and corresponding attribute data of a to-be-decided project; establishing a fuzzy relation matrix between the places and the attributes; generating a network model reflecting trust relationship strength among users through trust propagation operation of the graph neural network; identifying a community structure containing a community overlapping degree through a community discovery algorithm; and according to the trust relationship strength between the users and the community overlapping degree, calculating an influence weight of a decision maker, forming a group consensus through a robust optimization method, and generating a decision result of the project to be decided. Aiming at low network relation modeling precision caused by multi-source heterogeneous data in bus station layout decision making in the prior art, the method and the device have the advantages that the network relation modeling precision is low through accurate modeling of an information propagation path in a complex trusted network, effective dimension reduction representation of a high-dimensional feature space and robust optimization solution in an uncertain environment; therefore, the calculation precision and robustness of the bus station layout intelligent decision-making system are improved.

The invention relates to the technical field of complex network analysis, and provides a social network overlapping community discovery method, which comprises the following steps: S1, calculating a potential influence value of each node, and screening out a set of core nodes in a network according to the influence values; s2, calculating the viewpoint convergence speed between the nodes by using the viewpoint value change in the DeGroot viewpoint dynamical model, and calculating the propagation weight between the nodes according to the average accumulated viewpoint difference value for measuring the viewpoint convergence speed; s3, constructing a membership degree vector for each node, and updating the membership degree vector in combination with the propagation weight in each round of propagation; s4, adaptively adjusting a community membership degree threshold value of label propagation according to the community network scale and the community characteristics, and allocating the community network attribution of the nodes according to the community membership degree threshold value; and S5, according to the propagation result of the initial label, carrying out community structure correction and redistribution of undistributed nodes. The defects of randomness, overlapping modeling and calculation efficiency of a traditional method are overcome.

The invention relates to a community structure discovery-oriented large-scale complex network visualization method. The method comprises the following steps: acquiring a large-scale complex network; extracting topological structure features of the large-scale complex network by using a representation learning technology to obtain a node representation vector set; performing dimension reduction processing on the node representation vector set by using PCA and t-SNE algorithms to obtain an output vector set; recursive clustering is carried out on the output vector set, each level in the hypergraph is divided, a community formed after clustering of each level is constructed as a super node, the relation between nodes in the community is combined as a hyperedge, and a hypergraph structure is constructed; and visualizing the hypergraph structure by using a diversified visualization algorithm. By adopting the method, the visualization effect of the complex social network can be improved.

The invention is suitable for the technical field of data detection, and provides an anomaly detection method and device, terminal equipment and a computer readable storage medium, and the method comprises the steps: constructing an original structure attribute graph according to interaction data of a solid object in an import and export process, carrying out the structural enhancement of the original structure graph based on a data enhancement strategy, and obtaining a plurality of high-order sub-graphs, acquiring an implicit interaction relationship among a plurality of import and export solid objects, and modeling local feature information of the import and export solid objects by using the trained multi-order contrast learning model; dividing the original structure attribute graph into a plurality of communities according to a topological connection relationship between entities, acquiring global feature information of the import and export solid articles by using a plurality of community structures, and performing anomaly detection on a target entity according to the local feature information and the global feature information of the import and export solid articles. According to the method, the attribute and structure information of each dimension of the solid article are utilized, and the anomaly detection precision of the import and export solid articles is improved.

The invention relates to a post personnel recommendation interpretation method and device based on gradient features and a community structure. The method comprises the steps of obtaining a pre-trained and converged graph neural network and original embedded features, carrying out subsequent operation according to a knowledge graph in which entities such as personnel, skills, posts and services are stored, and recommending a network for on-duty personnel by using the graph neural network, and combining the original embedded features with gradient vectors obtained by the graph neural network through a back propagation algorithm to form gradient features. And performing community division on the knowledge graph nodes, and generating node community tags by maximizing modularity. Finally, the gradient features, the node community labels and the graph neural network model are input into an interpretation framework together, the graph masks and the feature masks are optimized to maximize information quantitative indexes, and post personnel recommendation results containing the key sub-graphs and the feature masks are generated. By adopting the method, the on-duty personnel recommendation interpretability in different scenes can be continuously improved.

The invention discloses a social network community division method based on a graph attention mechanism, and belongs to the technical field of social network analysis. According to the method, firstly, nodes and edges of the nodes in the social network are preprocessed, and then a graph attention mechanism is introduced through a graph neural network to capture local features and global structure information of the nodes. After node feature embedding is completed through multi-layer information aggregation, a clustering algorithm is improved by comprehensively considering the node attribute feature similarity degree and the proximity degree of nodes in a network topology structure, and community division is conducted on the nodes through the improved clustering algorithm. Experimental results show that compared with an existing community division method, the method has the advantages that the accuracy and the efficiency are remarkably improved, the potential community structure in the social network can be better recognized, and the method can be widely applied to the fields of social network analysis, user behavior prediction, recommendation systems and the like.

The invention discloses a lake and reservoir dominant algae community structure prediction method and system based on machine learning and a storage medium, and relates to the technical field of water environment monitoring and ecological prediction. Comprising the following steps that multi-source heterogeneous spatio-temporal data are obtained and fused, and the multi-source heterogeneous spatio-temporal data are historical time sequence data of a target water area; generating a feature vector from the fused multi-source heterogeneous spatio-temporal data; constructing a multi-output regression machine learning model, and inputting the feature vector into the multi-output regression machine learning model for training to obtain a multi-output prediction model; and inputting real-time monitoring data into the multi-output prediction model to generate a prediction result of a future dominant algae community structure. According to the method, whether cyanobacterial bloom or diatom bloom exists in the future can be clearly early warned, so that a manager can take targeted measures, for example, a specific algicide or ultrasonic equipment is used for the cyanobacteria, blind pesticide application is avoided, and the cost and the secondary pollution risk are reduced.

The invention discloses a structure entropy model for detecting a social robot in real time, particularly relates to the technical field of big data mining, and is used for solving the problem that an existing social robot detection method is difficult to balance between detection precision and processing efficiency. User data is processed through a feature selection weighting module to screen key features and distribute weights, a feature stability evaluation module analyzes the contribution degree of the key features to a community structure to screen features with the most distinguishing power, and a multi-relation graph construction module constructs a user similarity graph based on the key features and the weights thereof. The coding tree community division module initializes a coding tree on the user similarity graph and generates a hierarchical community division structure through iterative calculation of structure entropy change; and the community level classification module forms a comprehensive score for each community fusion structure entropy and behavior statistical characteristics and judges a social robot community through comparison with an optimization threshold.

The invention provides a method and a device for identifying multi-trophic-level aquatic biocenosis stability influence factors, and relates to the technical field of aquatic ecosystem analysis. The method comprises the following steps: acquiring a water body physicochemical parameter data set and a multi-trophic-level aquatic biocenosis data set of a plurality of sampling points of a target water area; constructing a species association network system for stability calculation based on the multi-trophic-level aquatic organism community data set; calculating a stability index representing the overall stability of the community based on the network system; and finally, analyzing the contribution degree of the water physical and chemical parameter data set to the stability index by utilizing a machine learning model, and identifying the key influence factor according to the contribution degree. According to the method, the key physical and chemical factors influencing the stability of the ecological system can be systematically and accurately identified by integrating the multi-nutrition-level community structure and machine learning, and the targeting and management efficiency of the restoration of the water ecological system are improved.

The invention provides an abnormal user identification method and device, electronic equipment, a medium and a program product, and can be applied to the technical field of big data and the technical field of artificial intelligence. The method comprises the following steps: acquiring multi-dimensional discrete feature data of a to-be-detected user set, and converting the multi-dimensional discrete feature data into corresponding number information; generating a continuous embedding vector set based on the number information, and outputting a target distance function; taking the continuous embedded vector set as input, and executing K-means clustering based on a bisection strategy based on a target distance function to obtain a cluster set and a corresponding centroid set; for at least one cluster in the cluster set, constructing a transaction network graph, and obtaining a community structure based on the transaction network graph; executing edge weight analysis based on the community structure and the edge weight information in the transaction network graph; target nodes in the transaction network diagram are removed based on an edge weight analysis result, and a user subset is obtained; and outputting an abnormal user identification result based on the user subset.

This application discloses a comprehensive assessment method and system for multi-source data management and correction of watershed fish resources. The method includes: extracting multi-dimensional features from a first data matrix to obtain a first feature matrix; classifying species data based on anomaly scores, outputting verification results, and correcting the first feature matrix to obtain a second feature matrix; acquiring environmental DNA sequencing data and corresponding environmental data; obtaining a first eDNA dataset from the environmental DNA sequencing data through ecological screening; correcting the species in the first eDNA dataset to obtain a second eDNA dataset; fusing the second eDNA dataset with the second feature matrix; calculating biodiversity indices and analyzing community structure; analyzing long-term trends in population and community structure; and generating a comprehensive assessment report. This application achieves full-process automation and intelligence from data quality control and fusion to assessment, significantly improving the accuracy and efficiency of watershed fish resource assessment.

The invention discloses a method for realizing influence maximization of information propagation based on node feature fusion and community detection. The method comprises the following steps: step 1, dividing a network into relatively independent communities, and defining features and boundaries of different communities; selecting candidate nodes based on a community structure; step 2, candidate node selection: screening out a part of nodes with potential influence from numerous nodes to serve as an alternative set of seed nodes; by analyzing and evaluating various attributes of the nodes, candidate nodes connected with more other nodes in the network are selected; and step 3, community detection and seed node selection: selecting different types of nodes as seed nodes in each community according to characteristics and requirements to realize diversified propagation of information. The method has the advantage of being low in time consumption, and the purposes of increasing the information spreading range and improving the information spreading efficiency can be achieved.

The invention discloses a system recommendation method based on Lanczos algorithm orthogonality. The system recommendation method comprises the following steps: firstly, modeling a problem of searching a community structure in a network structure into a problem of solving first k minimum feature pairs of a network Laplacian matrix; secondly, projecting a Laplacian matrix in a high-dimensional Euclidean space into a symmetric three-diagonal matrix in a low-dimensional Krylov subspace; then, using a feature value convergence criterion to screen out converged feature pairs of the symmetric tridiagonal matrix, and calculating feature vectors corresponding to the converged feature pairs; thirdly, calculating a feature vector of a Laplacian matrix according to the feature vector of the symmetric matrix; and finally, carrying out de-orthogonalization on the convergent feature vector of the Laplacian matrix and the Lancozs vector, storing the convergent feature vector of the Laplacian, and using the convergent feature vector of the Laplacian to detect the community structure through a standard k-means algorithm. According to the method, the real community detection data set is used as a drive, the Lanczos algorithm is used as a model, and the community structure contained in the graph network can be accurately detected, so that the recommendation quality of the system is improved.

The application discloses a kind of long-distance water transfer project water demand probability prediction method, comprising: collecting basic information, calculating the historical water demand sequence of water transfer project water receiving area, generating node level dynamic water demand characteristic vector;Based on dynamic water demand characteristic vector, through multiple directed dependence measure, identify the time-space dependence structure between nodes changing with time, dynamically divide high correlation, weak correlation and independent community, and track the evolution event of community;Based on community structure, fuse the geographical distance between community and water supply relationship, impose cross-domain cost factor, construct hierarchical water transfer area;Based on time-space dependence structure and hierarchical water transfer area, adopt differentiated probability prediction model and cascade time propagation mechanism, construct system level joint probability distribution.The application can accurately depict the dynamic dependence and hierarchical structure of system, improve the accuracy and reliability of prediction.

This invention relates to the field of internet information technology and discloses a method for evaluating the dissemination effectiveness of self-media promotion content. This invention constructs a directed weighted graph by collecting user interaction data and comprehensively calculates three core graph signal indicators: structural cohesion, interaction persistence, and influence anchoring degree. Finally, a comprehensive effectiveness score is obtained through a nonlinear fusion model. This method can effectively identify and filter invalid interactions, quantify the tightness of the community structure, the depth of dialogue, and the anchoring degree of core users. It overcomes the shortcomings of existing technologies, such as poor anti-interference capabilities and limited insight dimensions, achieving a multi-dimensional and interpretable accurate evaluation of community dissemination effectiveness, and providing partners with objective priority decision-making basis.

The application relates to the technical field of attack traceability data security, in particular to an information system APT attack process tracing method and system and a medium. The method comprises the following steps: acquiring log information of an information system, converting the log information into a traceability graph, identifying each community in the traceability graph, calculating community structure singularity of each community, clustering the community structure singularity of all communities, acquiring abnormal behavior significance of each community according to deviation and outlying conditions of the community structure singularity of each community in the cluster, identifying each abnormal community, obtaining behavior credit of each node in each abnormal community in combination with abnormal conditions of difference degrees of out-degree and in-degree of each node in each abnormal community, identifying abnormal nodes under APT network attack, and tracing paths under APT network attack by using an STP steiner tree algorithm. The application can improve the accuracy of APT attack process tracing.

The application discloses a stable anti-reversibility microbial community structure and a construction method and application thereof, and relates to the technical field of microbial community construction, and in particular to a stable anti-reversibility microbial community structure and a construction method and application thereof.

The application provides a power distribution network scheduling method and a scheduling device. The method comprises the following steps: constructing a power distribution network adjacency matrix and a power distribution network weight matrix according to a topology structure of the power distribution network; dividing the power distribution network into a plurality of power distribution network communities according to the power distribution network weight matrix by using a Louvain community finding algorithm to obtain a first community structure; establishing a constraint condition to optimize the first community structure to obtain a second community structure; constructing a communication network adjacency matrix of a communication network according to a hierarchical structure of the communication network; constructing a coupling network adjacency matrix according to the power distribution network adjacency matrix, the second community structure and the communication network adjacency matrix, the coupling network adjacency matrix being used to describe a coupling relationship between the power distribution network and the communication network; and performing power scheduling on the power distribution network according to the coupling network adjacency matrix to balance power supply and demand of the power distribution network. The method solves the problem of low power scheduling efficiency caused by a low coupling relationship between the power distribution network and the communication network in the prior art.

The application relates to a recommendation method based on social network user behavior information, which comprises the following steps: obtaining user behavior data and obtaining user activity holding location preference and holding time preference; constructing a preference similar network by using the obtained user preference, and then fusing a user online social relationship network to obtain a plurality of non-overlapping community structures reflecting the social connection and preference similarity between users; calculating the value of each hidden point representing the user's decision on a project by using a hybrid pairwise Markov random field model; calculating all node states of each project, and then selecting a recommendation list by sorting. The application aims to recommend the optimal activity holding location and holding time for an activity organizer, so as to maximize the number of participants in the activity and meet the decision planning needs of the activity organizer.

The invention relates to the technical field of intelligent security and monitoring, and discloses a security camera community relation analysis method, device and system based on minimum entropy, and the method comprises the following steps: collecting multi-dimensional data of each camera, and generating a feature vector; calculating the comprehensive similarity between the cameras, and constructing an association graph in combination with the position, orientation and naming information; performing clustering analysis based on the feature vector and a minimum entropy criterion, and constructing a point location clustering structure; community division based on minimum entropy is executed in combination with the clustering structure, and a community structure reflecting the space and behavior relation between the cameras is generated; the system comprises a data acquisition module, a preprocessing module, a similarity calculation module, a clustering analysis module and a community division module. According to the method, camera clustering and community division are optimized through the minimum entropy criterion, the behavior analysis precision of the security and protection system is improved, and the anomaly detection, resource scheduling and safety early warning capabilities are effectively enhanced.

The invention belongs to the field of overlapping community detection, discloses an overlapping community detection method based on gradient guide graph contrast learning, and provides a gradient guide dynamic capturer to eliminate message contrast conflicts by sensing gradient changes of positive and negative sample pairs. Key negative samples inducing message comparison conflicts can be accurately captured, and a model is promoted to accurately identify communities to which nodes belong. Besides, aiming at false negative samples and LCE problems, the invention respectively provides a new false negative strategy and a message passing mechanism based on pheromones so as to enhance the modeling capability of the model for a complex community structure.

The invention belongs to the technical field of computers, and discloses a query method for a closely associated transaction account group, which comprises the following steps of: 1, extracting all connected k-trus communities in a global time interval of an account transaction network, and calculating a real time interval; step 2, carrying out forward evolution refinement; step 3, carrying out backward evolution refinement; step 4, constructing a DAG index structure of the time evolution community; step 5, querying the maximum connected k-trus community of the given account in a specified time interval by using the index; and step 6, returning a query result. According to the method, by constructing the account community structure diagram evolved in the time interval, the repeatedly stored community information is effectively compressed, and the expression ability and query efficiency of account community evolution in a cross-time range are improved. Through recursive stripping and sharing of edge sets among communities, minimum redundant community representation across multiple time windows is realized, and the storage overhead is remarkably reduced while the historical integrity of the communities is ensured.

The application relates to the technical field of network analysis, and discloses a network community discovery system and method through matrix analysis, which comprises the following modules: a network module, which converts a static network topology into an information propagation model, and establishes a node state time sequence dynamic model by defining a node information processing rule and a multi-round propagation mechanism; a phase space reconstruction module, which maps high-dimensional time sequence data to low-dimensional phase space through a nonlinear dimension reduction method to form node trajectory distribution data; a community feature module, which analyzes the convergence, oscillation mode and attractor feature of the node trajectory, and generates community structure feature data through trajectory similarity; and a community division module, which identifies a community boundary through density clustering, and constructs a hierarchical community organization through multi-scale analysis. The application can deeply mine the internal community structure of a network from the perspective of dynamic information propagation, and overcomes the limitation that traditional methods only consider static topology.

Multi-tenancy system to perform tactical and permanent database and communication operations including secure handling of personally identifiable and / or health information (PII / PHI), data collection, data management, reporting, data analytics, secure communications, document sharing and microservices (e.g., capturing biomarkers, determining presence of certain conditions by comparing captured biomarkers to biometrics associated with condition). System includes security platform meeting stringent data protection mandates including firewall with extensive security protocols, encrypting communications between components of system (in transit) and information within each of the components (at rest). PII / PHI information is further encrypted and only visible with appropriate decryption key. System utilizes low code / no code database platform to address increasing demand for rapid, iterative and collaborative application development. System includes form builder to easily add dynamic fields. System includes a collaborative database development with a community structure approach of “who” data is needed from rather than “what” data is needed. Community structure controls operation thereof.

The application discloses a voltage partition method based on a community structure theory and regional reactive power supply and demand balance, and is characterized by comprising the following steps: step S1: according to the topological structure of a power system network, lossless processing is performed on the power system network, and reactive power loss on a line of the power system network is equivalent to reactive power load at nodes at two ends of the line; step S2: the community structure theory is used to perform reactive power partition on the power system network, a penalty function is set according to a difference between reactive power demand and reactive power supply in a community, and a reactive power imbalance degree is set according to a ratio between the reactive power demand and the reactive power supply in the community; a community structure quantitative evaluation index modularization function Q' is established in combination with the penalty function and the reactive power imbalance degree; step S3: arrangement and combination are performed on community partition, the community structure quantitative evaluation index modularization function Q' is calculated and updated, and the community partition with the maximum modularization function Q' value is selected as a final reactive power partition result. Therefore, the best reactive power partition result is obtained.