20 results about "Complex network analysis" patented technology

The invention relates to the technical field of complex network analysis and data mining, and provides a scientific research cooperation analysis method for edge crossing attribute network clustering, and the method comprises the steps: obtaining original data, and carrying out the preprocessing of the original data; an undirected empowerment cooperative network is constructed and processed to obtain a core sub-graph, and the maximum connected component LCC of the core sub-graph is extracted; constructing a node multi-dimensional feature vector by using a natural language processing technology; using an ECV algorithm to determine an optimal community number; constructing a topological feature matrix and an attribute similarity matrix by adopting an ANCA algorithm, and fusing the topological feature matrix and the attribute similarity matrix; hyper-parameters of the ANCA algorithm are adjusted and optimized in a grid search mode, and community division and analysis are carried out. The method has an extremely important application value and a wide social prospect in optimizing resource configuration, evaluating team influence, promoting academic communication and cooperation and guiding development of a scientific research direction.

This invention discloses a safety analysis method for ship SOFC systems, belonging to the field of risk prevention and control technology. It includes: identifying the causative factors of the ship SOFC system; constructing a failure fault tree and event tree model; constructing a Bow-Tie model based on the failure fault tree and event tree model; quantitatively analyzing potential causative factors to identify key factors affecting the SOFC system; combining the analyzed ship SOFC system accident causation data and collected ship fuel cell standard and specification data to form a data system and construct a ship SOFC system accident risk prevention knowledge graph; and conducting SOFC system safety analysis based on the ship SOFC system accident risk prevention knowledge graph. This invention clarifies the criticality, hierarchical relationship, and action path of each causative factor; constructs the causative factors into a complex accident causation network; and, combining the semantic information of the knowledge graph and the capabilities of complex network analysis, deeply reveals the accident causation propagation mechanism, providing a comprehensive perspective for understanding the occurrence and development of accidents.

The invention provides a self-adaptive routing method and system based on Motif statistics and reinforcement learning, an on-chip routing network (Network-on-Chip, NoC) provides a communication basis for a decentralized many-core chip architecture, and the performance of the NoC is closely related to the routing method; according to the adaptive routing method and system, the data communication path is dynamically adjusted according to the actual operation scene of the chip, and it can be guaranteed that an on-chip routing network can express efficient communication performance in different complex communication scenes. Real-time global communication information is processed based on a complex network analysis method Motif, a Motif analysis result and other routing related information are combined, and a path selection strategy is trained through a deep reinforcement learning algorithm. The adaptive routing algorithm based on global communication analysis has stronger adaptive ability, and can provide better data communication time delay and higher data throughput in a complex and dynamically changing scene.

The invention discloses a mathematical description method of a cascade reservoir group joint scheduling system based on a complex network. The mathematical description method comprises the following steps: step 1, determining a network type; 2, defining nodes of the cascade reservoir group complex network; 3, defining edges of the cascade reservoir group complex network; 4, defining an adjacent matrix A of the cascade reservoir group complex network; step 5, defining node attributes of the cascade reservoir group complex network and a constraint set of the node attributes; 6, defining natural incoming water as an input variable of the reservoir group system; 7, describing a hydraulic coupling relationship between the cascade reservoirs; 8, describing a state transition equation between the cascade reservoirs; according to the method, the mutual relation between the reservoirs can be more comprehensively captured, the network property between the reservoirs in a cascade reservoir group combined dispatching system is visually reflected, more analysis angles are provided for reservoir optimization dispatching, and a systematic mathematical description method is provided for complex network analysis of the reservoir groups.

The invention is suitable for the technical field of complex network analysis, and provides a community discovery method based on high-order enhancement, which comprises the following steps: formalizing a literature reference network into an undirected graph, and constructing an adjacent matrix and a node attribute matrix based on the undirected graph; constructing a DNMF core loss based on the adjacency matrix and the node attribute matrix; constructing a first-order Laplacian regular term objective function and a high-order Laplacian regular term objective function based on the adjacent matrix; constructing comparative learning loss, and fusing the DNMF core loss, the first-order Laplacian regular term objective function, the high-order Laplacian regular term objective function and the comparative learning loss to obtain a total objective function; performing optimization solution on the total objective function to obtain a topology embedding matrix and an attribute embedding matrix; and obtaining a community discovery result based on the topology embedding matrix and the attribute embedding matrix obtained by solving. According to the invention, the accuracy of document community division can be improved.

The invention relates to the technical field of complex network analysis and data mining, and provides a symbol network link prediction method based on node behavior similarity, and the method comprises the steps: 1, carrying out the preprocessing of behavior data, carrying out the time slice division of original node behavior data, and forming a discrete time sequence behavior matrix; 2, behavior joint modeling: carrying out statistics on behavior combinations of node pairs in different time slices, and generating a joint behavior probability matrix and an edge distribution probability; thirdly, a weight distribution mechanism is adopted, and a Softmax weighting strategy is introduced; fourthly, symbol mutual information is calculated, and a final behavior similarity value is calculated by combining node pair symbol labels; and 5, outputting behavior characteristics, and inputting results as characteristics into the link prediction model. According to the method, high efficiency and accuracy of link prediction are realized, and more reliable technical support is provided for applications such as a social recommendation system, online trust evaluation and user relationship mining.

The invention discloses a vehicle pile network data aggregation method, and belongs to the technical field of multi-source data processing, and the method comprises the steps: carrying out the initial grid division of an urban road network, and optimizing the boundary of a grid through the combination of complex network analysis and a random walk algorithm; acquiring data in each grid and preprocessing the data; performing feature extraction on the preprocessed data by using a feature extraction network, and fusing the extracted features into grid features by using a grid feature fusion model; using a feature fusion algorithm to aggregate each grid feature and the corresponding predicted target value, and using the aggregated grid features and the corresponding predicted target values to perform joint training on the feature extraction network and the grid feature fusion model; and performing feature extraction and feature fusion by using the trained feature extraction network and the grid feature fusion model to complete the iterative aggregation of the vehicle pile data. According to the method, vehicle network collaborative interaction under multi-source data aggregation can be realized, and construction of an intelligent energy traffic network is supported.

The invention discloses an innovation path recommendation method based on complex network analysis and scientific and technological innovation force. The method comprises the following steps: acquiring multi-source technical achievement data; performing vectorization processing on text information of the result record, and constructing a technical map based on node vectors and node attributes; community detection and bridge node identification are carried out on the technical atlas to determine potential association relationships among different technical fields; generating a candidate path set containing cross-domain connection based on the community detection result and the bridging node information; scoring and sorting paths in the candidate path set, and comprehensively considering path weights, cross-domain connection degrees and length constraints; and screening out an optimal innovation path in the sorting result and outputting a recommendation result. The method can effectively reveal the potential relation between the technical fields, assists in discovering high-value innovation paths, provides decision support for scientific research planning and technical innovation, and solves the problems that in the prior art, the innovation path mining efficiency is low, and cross-field correlation is difficult to quantify.

The invention provides a financing risk monitoring method and system. The method belongs to the technical field of risk monitoring. The method comprises the following steps: acquiring multi-modal data related to financing activities, and preprocessing the acquired multi-modal data; based on the preprocessed multi-modal data, constructing a financing risk dynamic conduction network by using a graph theory and a complex network analysis technology; performing deep feature extraction on the multi-modal data in the financing risk dynamic conduction network to obtain a financing risk feature data set; performing risk type classification on the financing risk feature data set by applying a machine learning algorithm, and identifying different types of risks; a dynamic conduction network is constructed by utilizing a graph theory and a complex network technology, so that not only are association and propagation paths among various risk factors clearly presented, but also a visual analysis basis is provided for supervision and decision making; and the visualization and interpretability of the risk conduction path are enhanced.

The invention discloses a key node identification and network robustness collaborative optimization method, and belongs to the field of complex network analysis and network robustness guarantee. The method comprises the following steps: firstly, extracting a multi-scale structure and topological characteristics of nodes, constructing a supervision label fusing structural vulnerability and propagation potential, training a gated graph neural network to obtain node importance scores, and dividing the network into a core layer, a standby layer and a peripheral layer according to the scores to form layered topology; on this basis, a plurality of differentiated edge adding strategies are designed, a Q learning framework is introduced, the edge adding strategies are adaptively selected according to the network state, the network structure is iteratively optimized under the constraint of edge budget, and integrated collaboration of key node identification and topology reinforcement is realized. Compared with the prior art, the method has the advantages that the connectivity and service availability of the network under deliberate attacks are enhanced while the key node recognition precision is improved, and the method is suitable for distributed scenes such as a micro-service system, edge computing and the Internet of Things.

This invention relates to a method for modeling and blocking urban flooding disaster chains based on large language models and complex network analysis, belonging to the field of disaster prevention and mitigation. Using multi-source media text as input data, a large language model is used to automatically extract disaster events and their causal relationships. Through association rule analysis, support, confidence, and lift between events are calculated to construct a weighted directed disaster chain network, which characterizes the cascading propagation relationships of urban flooding events. Based on complex network theory, indicators such as the centrality, connectivity, and network efficiency of nodes and edges are calculated to identify key nodes and vulnerable propagation paths. A progressive node and edge removal experiment is designed to evaluate various intervention strategies and determine the optimal disaster chain blocking scheme. This invention enables automated modeling and quantitative analysis of disaster chain networks from unstructured text data, rapidly identifying key propagation links and system vulnerabilities in urban flooding, significantly improving the accuracy of urban flooding disaster chain identification, analysis efficiency, and the scientific nature of prevention and control decisions.

The invention discloses a dynamic community detection method and device, a medium and equipment, and relates to the technical field of artificial intelligence and complex network analysis. According to the method, neighbor view angle information and structure view angle information of each user node are fused, and network denoising and feature enhancement are realized through dual-channel graph convolution according to the similarity between neighbor nodes of each user node in a graph structure of each time step and the similarity between topological structures of each user node; according to the method and the device, the user nodes are extracted, then node representation and global graph representation of the user nodes are generated, and when the two-channel graph convolution process is trained, joint modeling structure consistency loss, potential implicit conflict loss and local-global mutual information loss are constructed, so that the node representation accuracy is improved, the utilization rate of multi-view information is improved, and the method and the device are suitable for popularization and application. And clustering is carried out based on the trained node representation, so that the division accuracy of communities in the dynamic social network is improved, and the calculation efficiency is considered.

A node centrality measurement method for multidimensional network models in the Internet of Things (IoT) is disclosed, relating to the fields of IoT and complex network analysis. The steps are as follows: 1. Define the concept of a local multidimensional network, simulate the decision-making process of individuals based on local information, and combine relative entropy to quantify local differences between dimensions, calculate the local migration tendency between dimensions, and construct a dimension migration probability matrix; 2. Based on the difference between the target multidimensional network and extreme networks, dynamically adjust the trap probability to correct the node centrality value; 3. Utilize matrix symmetry and the transitivity of dimension differences to quickly screen a very small number of extreme networks for approximate calculation of trap probabilities; 4. Combining dimension migration tendency and dynamic trap probability, obtain the final node centrality ranking through iterative calculation. This invention solves the problems of unreasonable dimensional difference quantification, random walk dependence on global information, and lack of adaptive adjustment of trap probabilities in existing methods, improving the rationality and accuracy of node ranking.

The application discloses a kind of spatiotemporal factor-driven complex network anomaly detection method, device and medium, it is related to complex network analysis and network security technical field.The method comprises: constructing network security industry chain network, based on time attenuation function to the time characteristic and space neighborhood characteristic are encoded, obtain spatiotemporal factor, according to spatiotemporal factor calculation dynamic relationship weight, based on time sequence sliding window sampling and contrast window learning, obtain the characteristic vector of each node, based on the characteristic vector of each node determine the node level feature deviation of each node, and based on the node level feature deviation of each node, structure evolution rate and cross-network alignment abnormality, the score of each node is calculated;Based on the score threshold value set dynamically, if the score of node exceeds the score threshold value set, then determine that the node exists abnormal behavior.The application can improve the accuracy of complex network anomaly detection.

The invention relates to the technical field of complex network analysis, in particular to a network key node identification method and system for technical network analysis, and the method comprises the steps: confirming a to-be-analyzed target patent data set, and taking target patent data in the target patent data set as nodes; the method comprises the following steps: establishing a patent topology model by taking an inter-patent reference relationship of target patent data in a target patent data set as an edge, obtaining a weight of each edge in the patent topology model based on the target patent data set to obtain a reference weight set, optimizing the patent topology model based on the reference weight set to obtain a target topology model, and outputting the target topology model. And training a pre-constructed graph convolutional network model to obtain an optimized graph convolutional network model, and identifying a preset target network key node in the target topology model by using the optimized graph convolutional network model to realize identification of the target network key node. According to the invention, the accuracy of identifying the network key node can be improved.

The invention relates to the technical field of complex network analysis and graph machine learning, in particular to a community detection method based on theme emotion and a weak edge optimization strategy. The method aims at solving the problems that an existing community detection technology is sensitive to weak connection edges and noise data in social network application, multi-dimensional joint optimization such as theme consistency cannot be achieved, and implicit semantic relations are difficult to capture. A technical framework comprising a data preprocessing module, a theme-emotion enhancement module, a weak edge optimization module and a multi-target community division module is constructed; through multi-modal data fusion, topic emotion collaborative modeling, weak edge dynamic optimization and multi-objective function optimization, double improvement of community structure compactness and semantic consistency is realized, noise interference is effectively suppressed, the accuracy, robustness and interpretability of community detection are remarkably improved, and the method is suitable for hidden community mining of a large-scale social network.

The invention discloses an urban inland inundation disaster chain modeling and blocking method based on a large language model and complex network analysis, and belongs to the field of disaster prevention and reduction. A multi-source media text is used as data input, and disaster events and causal relationships thereof are automatically extracted by using a large language model; the support degree, the confidence degree and the improvement degree among the events are analyzed and calculated through association rules, and a weighted directed disaster chain network is constructed to describe the cascade propagation relation of the urban waterlogging events; calculating indexes such as centrality, connectivity and network efficiency of nodes and edges based on a complex network theory, and identifying key nodes and fragile propagation paths; a progressive node and edge removal experiment is designed, multiple intervention strategies are evaluated, and an optimal disaster chain blocking scheme is determined. According to the method, automatic modeling and quantitative analysis from unstructured text data to a disaster chain network can be realized, key propagation links and system vulnerabilities in waterlogging can be quickly identified, and the identification precision, the analysis efficiency and the prevention and control decision scientificity of an urban waterlogging disaster chain are remarkably improved.

This invention relates to a big data processing method and system for integrating online public opinion into website construction, belonging to the field of data processing technology. It includes the following steps: Step S1: Constructing a multi-source heterogeneous public opinion dataset; Step S2: Performing semantic analysis and entity recognition on the multi-source heterogeneous public opinion dataset, generating a public opinion-user association matrix based on public opinion themes and user profile features; Step S3: Utilizing complex network analysis algorithms to perform community discovery and core node identification on the public opinion-user association matrix, and generating a public opinion development trend prediction curve by combining it with a public opinion heat prediction model; Step S4: Based on the public opinion development trend prediction curve and key user groups, dynamically adjusting the page content display priority through the website content management module, while simultaneously calling intelligent interactive components to generate targeted public opinion response content; The beneficial effects of this invention are: It achieves the integration of public opinion data and website construction data through community discovery and core node identification.

This invention relates to the fields of complex network analysis and social network data mining, and provides a community discovery method based on user values, comprising the following steps: S1: constructing user-topic dual-encoding representations; S2: decoupling representations based on value hierarchy structure; S3: value semantic alignment constraints; S4: user value representation generation and optimization; S5: hybrid similarity construction and node centrality calculation; S6: label update strategy based on hybrid centrality ranking; S7: label propagation driven by multi-level influence; S8: community attribute center construction and fuzzy node identification; S9: fuzzy node re-attribution correction based on community attribute centers to solve the problems of difficulty in obtaining stable user value representations under low / no corpus conditions, and difficulty in effectively introducing value orientation as node attributes into community partitioning, thereby achieving stable community partitioning based on user values.