105 results about "Social network analysis" patented technology

The invention relates to the technical field of social network analysis and generative artificial intelligence, and discloses a multi-agent-based complex social behavior simulation and public opinion deduction method, which comprises the following steps of: dividing social groups, and dividing users in a social network into key opinion leader communities and common user communities; respectively constructing a key opinion leader agent and a common user agent which are driven by the large language model; constructing a dynamic weighted directed network, taking each key opinion leader agent model as a node, calculating a network node neighborhood through opinion index and node influence weighting, calculating an emotion score based on a text emotion index, dynamically adjusting the network node neighborhood based on the emotion score, and performing loop iteration for several times to complete deduction and simulation of network public opinions; the problem that public opinion propagation modeling is difficult under the cross-domain dynamic background is solved, the difference of public opinion individuals and the complex social interaction relation can be simulated more accurately, and the method is feasible.

The invention relates to the technical field of social network analysis and influence prediction, and discloses a large-scale social network influence prediction system and method.The large-scale social network influence prediction method comprises the steps that a multi-language knowledge graph alignment system is constructed, and accurate mapping of cross-language concept nodes is achieved; constructing a culture vector space representation system, and extracting culture features from the social network user behavior data; the resonance intensity calculation between the content and the culture vector is realized, and the resonance intensity of the content in a specific culture environment is quantified; realizing culture gene transmission dynamics simulation, decomposing the content into transmissible culture gene units, and simulating the transmission process of the culture gene units; fusing prediction results to realize accurate influence evaluation; the technical problems that an existing social network influence prediction technology is inaccurate in prediction in a cross-language environment and neglects a culture resonance effect and culture dynamics are solved, and more accurate prediction support is provided for applications such as social media marketing and public opinion analysis.

The invention discloses a public opinion event multi-mode semantic fusion modeling and abstract generation method and system, and relates to the field of natural language processing and social network analysis. Through the multi-mode semantic fusion technology, the short text understanding ability is improved, and the problems of semantic fuzziness and network language diversification are solved. Meanwhile, through a cross-window event cluster matching technology, an event evolution path with time continuity is constructed, and comprehensive capture of event dynamic characteristics is realized. Besides, the structured event abstract is automatically generated by utilizing the generative model, so that the consistency and the information density of the abstract are improved, and the actual application requirements are met. Through the innovations, the defects in the aspects of semantic comprehension, dynamic modeling and abstract generation in the prior art can be effectively overcome, a more efficient and accurate solution is provided for monitoring and analysis of public opinion events, and the method has wide application prospects in the fields of public opinion monitoring, emergency early warning, social media data analysis and the like.

The invention relates to a financial case fund flow traceability analysis method, and belongs to the technical field of computer networks. According to the method, the social network analysis technology and the graph neural network technology are combined, and the advantages of the social network analysis technology and the graph neural network technology are comprehensively utilized to analyze and track the fund flow direction in the financial transaction network. According to social network analysis, key nodes and group structures in a transaction network are identified by calculating node centrality and community discovery; the graph neural network analysis further extracts complex transaction features and modes through a deep learning model, and identifies abnormal transaction behaviors. Besides, advanced graph neural network models such as a graph convolutional network and a graph attention network are utilized, parallel computing and distributed training technologies are combined, the model training process is efficient and has good expansibility, and the method adapts to processing requirements of large-scale financial transaction data.

According to the privacy enhancement type (k, d)-truss community search method for the social network, a novel KTG tree structure is constructed by fusing k-truss and G-tree indexes. According to the index structure, hierarchical community decomposition of a social graph and social distance information are fused, a refined boundary vector coding scheme is designed to support efficient distance calculation, and a double-cloud-server non-collusion architecture integrating improved homomorphic encryption and matrix encryption technologies is constructed. Through a two-stage security query process of first structure filtering and then distance verification, on the premise of protecting full-process privacy of a graph structure, a query intention, a distance matrix and an intermediate calculation result, efficient and accurate search of a close community in a large-scale social network is realized. The method is suitable for various scenes such as social recommendation, risk control, public opinion analysis and anti-fraud, and the problems of privacy disclosure and calculation efficiency in social network analysis are effectively solved.

The invention relates to the technical field of digital health management, in particular to a health behavior intervention and incentive method based on social network analysis, which comprises the following steps: acquiring health behavior data, social network data and interaction data of a user through a social platform and a health management application, preprocessing the acquired data, ensuring the accuracy and consistency of the data, and improving the user experience. Modeling by adopting a social network analysis technology, extracting social nodes and structural features, identifying a user behavior mode through clustering analysis, and evaluating health risks; on the basis of the analysis results, a personalized intervention strategy is generated, and through multi-dimensional incentive mechanisms such as material reward, social reward and psychological incentive, the user is pushed to continuously keep good health behaviors; according to the invention, accurate and long-term health management can be realized, and the problems of low efficiency and singleness in a traditional health intervention scheme are solved.

The invention relates to the technical field of classroom interaction, and discloses a classroom interaction evaluation method and system based on voice data, and the method comprises the steps: collecting classroom voice data, and converting an audio signal in the voice data into a digital signal; the collected audio signals are preprocessed, the audio is converted into characters, and voice data of different speakers are separated; analyzing the voice data of different speakers to obtain a speaker interaction sequence; carrying out statistics on the speaker interaction sequence to obtain interaction frequency characteristics among different speakers; constructing a classroom interaction network based on the speakers based on the obtained interaction durations and interaction frequencies among the different speakers; a social network analysis technology is utilized to perform deep analysis on a classroom interaction network, and classroom interaction behaviors are evaluated through quantitative evaluation parameters. According to the invention, the accuracy and efficiency of classroom interaction analysis are improved, and targeted classroom interaction organization and improvement suggestions are provided for teachers through deep analysis of interaction behaviors.

The invention relates to the technical field of industrial chain analysis, and discloses an industrial chain key hub identification method based on social network analysis, comprising the following steps: collecting multi-source heterogeneous data and constructing an industrial chain node incidence matrix, the data including enterprise industrial and commercial information, transaction records, patent cooperation data and social interaction records; based on a directed weighted heterogeneous network model, mapping industrial chain nodes into network vertexes, mapping association among the nodes into directed edges with weights, and constructing an industrial chain network; calculating a node global influence score by using an improved PageRank algorithm, and identifying a community core node in combination with a Louvain community discovery algorithm; and fusing the global influence score and a community core node result, and determining an industrial chain key hub. The industrial chain key hub identification method based on social network analysis aims to accurately identify key hub nodes by constructing an industrial chain network model and combining a network topology structure and dynamic interaction data.

The invention relates to the technical field of computer technologies, and discloses an old people social network analysis and recommendation system based on a graph neural network, which comprises a data acquisition module, a graph construction module, a graph neural network model module, a social network analysis module, a personalized recommendation module and a user interface module, the data acquisition module is used for acquiring and cleaning social related data of old people; and the graph construction module is used for constructing static and dynamic social network graphs. According to the old people social network analysis and recommendation system based on the graph neural network, a dynamic social network graph is constructed through the graph construction module, sliding time window updating is adopted, dynamic changes of the social relation of old people along with time can be captured, meanwhile, edge weight calculation is combined with the interaction frequency and the interaction depth, and the recommendation efficiency is improved. The interaction depth fuses semantic similarity and emotion scores, the interaction depth is accurately quantified, and a social network analysis module introduces a time decay factor prediction relation.

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 family education content recommendation method and system based on big data, and relates to the technical field of big data, and the method comprises the steps: collecting family member multi-source data for preprocessing, carrying out the real-time analysis of the preprocessed data, updating a user file, setting a personalized recommendation target according to an analysis result, and carrying out the recommendation of family education content through the updated user file. The learning progress of a user is predicted by using an LSTM model, and a social relation and an interaction mode among family members are established through a social network analysis algorithm in combination with a robust algorithm and a Pearch ranking algorithm. According to the method, the family member social relation graph is constructed and optimized through social network analysis, the robust algorithm and the Pearch ranking algorithm, the social sub-groups are identified, family education content recommendation is dynamically adjusted in combination with the reinforcement learning algorithm, and the social adaptability and interest matching ability of personalized content recommendation are improved.

The invention discloses a graph neural network fairness improvement method and system, and relates to the technical field of graph neural networks. The method comprises the following steps: analyzing graph data to construct a node set and dividing sensitive attribute subgroups; estimating probability distribution of subgroup prediction results by adopting a Bayesian smoothing technology; calculating a mutual information difference between a node prediction result and the sensitive subgroup as a node-level prejudice value, and further generating a global average prejudice; global prejudice is fused into a loss function, task loss and fairness constraint are balanced through a dynamic weight strategy, and model parameters are optimized. The system comprises a node set construction module, a sensitive attribute subgroup analysis module, a node prejudice calculation module, a global constraint generation module and a joint optimization training module. According to the invention, the limitation of macroscopic statistics is broken through, and node-level prejudice accurate positioning is realized; fusing graph structure information to improve fairness optimization efficiency; and the dynamic weight strategy balances the performance and fairness. The method is suitable for scenes such as social network analysis and financial risk control.

The invention relates to the technical field of user layering methods, in particular to a user layering method and device and a storage medium, and the method specifically comprises the following steps: 1, collecting basic information data of users, including names, ages, genders, contact information and registration time, collecting behavior data of the users, and collecting social relation data of the users, cleaning the basic data and the behavior data according to the social relation data; 2, modeling a user behavior sequence; step 3, analyzing the user social network; 4, establishing a user value evaluation model; 5, layering the users based on deep learning; step 6, carrying out dynamic optimization on a layering result; step 7, applying and feeding back a layering result; according to the method, the problem of single traditional data is solved through multi-source data acquisition and processing, and accurate features are comprehensively extracted; behavior sequence modeling and social network analysis break through the limitation that only individual behaviors are concerned, and users with similar behaviors and social contact are deeply mined.

The invention discloses a social group member dynamic management method and system, and relates to the field of social network analysis, and the method comprises the steps: obtaining online interaction data and offline co-occurrence data of social group members, cleaning the online interaction data and the offline co-occurrence data, and converting the cleaned online interaction data and offline co-occurrence data into a structured data set; importing the structured data set into a graph database to obtain an initial weight, calculating a dynamic attenuation weight, and constructing a basic relation network; and converting an edge weight matrix in the basic relation network into a quantum bit entanglement state, measuring the quantum bit entanglement state to form a quantum probability amplitude, setting a quantum entanglement judgment threshold, and generating a quantum relation thermodynamic diagram through a Grover algorithm. Through the technical scheme of combining quantum calculation and dynamic entropy analysis, accurate management and risk prevention and control of the social group membership are realized, an edge weight matrix of a basic relation network is converted into a quantum bit entanglement state, and a quantum relation thermodynamic diagram is generated by using quantum measurement and a Grover algorithm.

The invention discloses a dynamic social network user behavior prediction method based on a core edge model and a graph neural network, and relates to the technical field of artificial intelligence and social network analysis. Comprising the steps of constructing a model; loading historical data of the dynamic social network, performing core-edge structure detection, identifying core nodes and edge nodes, and generating a core-edge structure matrix; embedding and coding the nodes by adopting a graph convolutional network to generate a first node representation; fusing the first node representations of the same node at different time steps by using a time memory fusion module to obtain a second node representation; constructing an objective function, wherein the objective function comprises random walk loss and core-edge structure loss; optimizing the parameters of the model by using the objective function; and predicting the future interaction behavior of the user by using the model. Finally, the problems that in the prior art, a core-edge structure is ignored, node attributes and a network structure are not effectively combined, and time dynamic modeling is insufficient are solved.

The invention relates to a node injection attack method based on adaptive target selection, and the method comprises the following steps: S1, target node selection: calculating a comprehensive score of a node based on uncertainty and topology centrality, and dynamically selecting a target node set of a current attack round; s2, feature generation: using an adaptive feature generator to generate node features which are similar to target node distribution and have strong aggressiveness; and S3, disturbance edge construction: selecting an optimal disturbance edge connection mode for the injection node according to strategy network output in reinforcement learning. According to the method, the attack flexibility can be improved through dynamic target selection, the attack performance can be remarkably enhanced through combination of disturbance characteristics and structures, and the method has good concealment, expandability and generalization ability. The method is widely applied to security evaluation and defense research fields related to graph neural networks, such as social network analysis, recommendation systems, knowledge graphs and the like.

The invention provides an information cascading prediction method and system based on a subgraph, and belongs to the technical field of social network analysis and neural networks, and the method comprises the steps: S1, constructing a deep learning information cascading prediction model CasSubTS, enabling collected user published information to pass through an input layer, and constructing an information cascading graph G; s2, inputting the G into a sub-graph sampling layer, dividing the G into a plurality of information cascade sub-graphs according to different time steps, converting the information cascade sub-graphs into adjacency matrixes, and performing node feature aggregation on the adjacency matrixes to obtain a feature representation matrix B; s3, inputting the B into a feature learning layer to obtain a feature vector # imgabs0 # with a structural feature and a time sequence feature; s4, inputting the # imgabs1 # into a feature weighting layer, and performing weighted fusion on the # imgabs2 # by using a channel attention mechanism to obtain a weighted feature vector # imgabs3 #; and S5, inputting # imgabs4 # into a prediction layer to predict a final macroscopic cascade increment. According to the method, the information cascading in the social network is effectively predicted.

The application discloses a cross-department collaboration efficiency optimization method fused with social network analysis, comprising the following steps: multi-modal data acquisition and space-time labeling: collecting communication data, behavior data and physiological signal data in cross-department collaboration, adding time stamp and node identification to each data, and associating the node identification with department attributes and role characteristics in the social network; dynamic identification of cognitive bias: based on the social network node interaction data, adopting natural language processing and sentiment analysis technology to detect logical fallacy of the communication text, and generating bias type label and propagation intensity index in combination with physiological signal wave, the application improves collaboration efficiency, accurately identifies the cognitive bias propagation path in cross-department collaboration through multi-modal data fusion and social network analysis, dynamically intervenes to reduce invalid communication, shortens the task response and dispute resolution time, enhances collaboration resilience, and the space-time enhancement and critical state early warning mechanism of the propagation graph can avoid the risk of network structure imbalance in advance.

The present invention discloses a method and system for identifying key common technical entities, belonging to the technical field of text data recognition; the method comprises: step S1: obtaining a technical text data set in a required field, and performing data cleaning on the technical text data set; step S2: defining entities and semantic relationships, and annotating text summary contents to form a corpus; step S3: using the corpus to train an entity relationship extraction model introduced into a neural network; step S4: performing commonality measurement through universality, efficiency, and relevance, and screening common technical entities; step S5: measuring the importance of technical entities with the help of social network analysis, and combining leading indicators to measure technical criticality, so as to accurately and efficiently identify key common technical entities.

The invention discloses an edge graph neural network edge point modeling method based on dynamic graph optimization, and belongs to the technical field of graph neural networks and deep learning. According to the method, a graph structure containing task related nodes and auxiliary concept nodes is constructed, an iterative refining mechanism of edge point joint updating is introduced, and dynamic mutual optimization of node features and edge features is achieved. In the node updating stage, the edge features serve as weights to guide neighborhood information aggregation; in the edge updating stage, updated node features are used for recalculating edge features to form closed-loop optimization of edge guiding points and point updating edges. According to the method, the static problem of edges in a traditional graph neural network is solved, the utilization ability of the model to dynamic relations and prior knowledge is improved, and the method is suitable for complex reasoning tasks such as small sample learning, social network analysis and recommendation systems.

The invention discloses a deep geothermal resource potential evaluation method, a deep geothermal resource potential evaluation device, deep geothermal resource potential evaluation equipment and a medium, and relates to the technical field of deep geothermal resource evaluation.The deep geothermal resource potential evaluation method comprises the steps that a research area is divided into five levels of structural units, and the deep geothermal resource potential is evaluated on the basis of deep thermal backgrounds of I-III level structural units and storage cover combination characteristics of IV level structural units; the method comprises the following steps: constructing indexes for characterizing geothermal occurrence conditions of a foundation of a research area, and constructing indexes for characterizing geothermal resource aggregation conditions of the research area on the basis of fracture distribution and protrusions and recesses of a V-level structural unit, so as to carry out step-by-step optimization from a whole area to a local favorable area, thereby emphatically considering a heat enrichment area in the research area; and then a social network analysis method is adopted to establish the influence weight of each index based on the relevance of the influence factors of each index, so that high-accuracy evaluation is carried out on the deep terrestrial heat of the research area.

The invention relates to a smart home dynamic influence maximization method based on time sequence perception, and belongs to the technical field of social network analysis. The method comprises the following steps: inputting a dynamic social network data set into a time sequence snapshot generator to generate a dynamic network sequence; pre-training labeling is carried out based on an influence capacity scoring method of time perception, and dynamic influence capacity scores, static features and dynamic features of nodes in the dynamic network sequence are calculated; the dynamic network sequence passes through a space-time network model to obtain a remodeled space-time feature vector; the remodeled spatio-temporal feature vector passes through a time sequence perception enhancement module to obtain fusion features; inputting the fusion features into a candidate seed node predictor, and predicting candidate seed nodes in each snapshot; and based on the candidate seed nodes in each snapshot, selecting an optimal seed node from the snapshots through an enhanced greedy algorithm to obtain an optimal seed node set. According to the invention, the accuracy of candidate seed prediction can be improved.

The invention belongs to the technical field of data processing, and particularly relates to a heritage community protection updating method and device based on social network analysis. The method comprises the steps of obtaining survey data of a target heritage community; constructing a relation network matrix among the actors according to the survey data; if it is determined that the relation network matrix meets a preset heritage community stability standard, development degree data of the target heritage community is obtained based on the network structure parameters, and core actors of the target heritage community are obtained based on the network position parameters; and determining an updated action plan of the target heritage community. The social network theory and method are introduced, a quantitative method is provided for protection and updating of human habitation type heritage places (especially determining the development degree of heritage communities and key actors thereof), the action logic of a single subject can be broken, a multi-element treatment structure in which governments, communities, enterprises and other promoters jointly participate is established, and the development degree of the heritage communities and the key actors are determined. And more participation channels and resource supports are provided for heritage protection and sustainable development.

The application discloses a kind of atmospheric pollution source positioning method and device based on social network analysis.The method comprises: obtaining the pollutant data of each monitoring station in a certain period in region and pre-processing;The pollutant data after pre-processing is filtered, and air pollution scene is established;Social network analysis is carried out on air pollution scene data to obtain the correlation strength between each station;According to the correlation strength between each station and the distance between each station, the intermediate centrality value of each station is calculated;According to the intermediate centrality value of each station and geographic coordinates, spatial interpolation is carried out using interpolation method, and the pollution source is positioned according to the plane data obtained after spatial interpolation.The positioning method of atmospheric pollution source proposed in the application can not only find the corresponding pollution area and key station corresponding to pollution event in complex environment, but also facilitate targeted management, and can further accurately locate the positioning range of pollution source to the vicinity of specific monitoring point, so as to efficiently and accurately locate the atmospheric pollution source.

The invention discloses a multi-target influence maximization method based on cost constraint in a hypergraph, and belongs to the technical field of social network analysis, and the method comprises the following steps: S1, selecting an independent cascade IC model as a basic model; s2, evaluating double targets by adopting a multi-target optimization function; s3, according to a three-mode initialization strategy, diversified initial individuals are generated in different areas of a Pareto frontier (PF) through an HCI-based initialization module, a unit collective influence-based initialization module and a random initialization module; and S4, operator selection: a fast non-dominated sorting algorithm, a two-point crossover operator, a mutation operator, and a self-adaptive crossover rate and mutation rate mechanism are selected. Through the mode, the core technical problems that the influence on diffusion and seed cost cannot be considered in a hypergraph scene, and the evaluation and search efficiency is low are solved, and an efficient and extensible evolutionary multi-target solution is provided.

The invention provides a common neighbor relation mining method and system based on graph calculation and mass data, and the method comprises the steps: 1, constructing a concept metadata system; step 2, performing data acquisition from the information system to be mined, and obtaining an instance of the concept according to the concept metadata system; step 3, unified coding: coding attributes in the instances; 4, the codes and the corresponding examples are stored in a graph database in a distributed storage mode; 5, relation mining is carried out, instance extraction is carried out from distributed graph storage, common neighbor discovery among instances is carried out according to coding characteristics, rule filtering is carried out according to preset rules, and a recommendation relation is generated; the system is used for implementing the method. The method can be widely applied to the fields of social network analysis, recommendation system optimization and the like, and an efficient solution is provided for relation mining of mass data.

The present application relates to a dynamic network representation learning method for node attribute preservation. The method includes: obtaining a time-dependent social network sample; constructing an inductive graph convolution model; determining the sampling order of neighbor nodes of a target node in the time-dependent social network sample according to the number of aggregate convolution modules of the inductive graph convolution model, and biased sampling from the highest sampling order downward layer by layer according to the order of interaction time between the neighbor nodes and the target node to obtain a sampling node set and a neighbor sequence queue for each layer; through #imgabs0# aggregate convolution modules, according to each layer's sampling node set and neighbor sequence queue, aggregate the neighbor node attribute vectors of the corresponding order layer by layer, and use the output of the last aggregate convolution module as the embedding vector of the target node; training the inductive graph convolution model, and using the trained inductive graph convolution model to perform representation learning to complete the social network analysis task. This method can learn more accurate node representations.

This invention relates to the field of social network analysis technology and proposes a social network sentiment evolution model that integrates user influence and activity. To address the shortcomings of existing technologies, such as inaccurate simulations and inflexible predictions in current network sentiment analysis and sentiment evolution processes, this invention provides a social network sentiment evolution model that integrates user influence and activity. The model analysis method involves: collecting text sentiment data from target users; collecting user influence data from each user; collecting the activity level of the target social network at time t; collecting the forgetting probability of each user at time t as a measure of sentiment evolution capability; establishing a dynamic model for the sentiment evolution of the social network; optimizing the evolution probability of the information propagation model based on text sentiment; and analyzing the evolution process of the dynamic model for the sentiment evolution of the social network based on the optimized information propagation model. This model is suitable for application in social network analysis.

The invention discloses a low-efficiency industrial land transformation strategy optimization method based on industrial chain analysis. The method comprises the following steps: acquiring related information data in a research area; calculating a target industrial land benefit index based on the preprocessed related information data, and identifying the low-efficiency industrial land according to the industrial land benefit index to obtain a low-efficiency industrial land identification result; the method comprises the following steps: constructing an enterprise association network based on a social network analysis method, calculating network feature values of enterprise nodes, identifying key enterprises in key industrial links from an industrial chain dimension, and carrying out land type division according to low-efficiency industrial lands in low-efficiency industrial land identification results related to the key enterprises, and carrying out transformation strategy optimization according to the division result. The method aims at overcoming the defect that in the prior art, a low-efficiency industrial land transformation strategy lacks consideration of the influence of an industrial chain on land benefits.

The present invention relates to the technical field of news hotspot monitoring, and provides a news hotspot monitoring method and system based on social network analysis, comprising: calculating the correlation between different news events, using the correlation between two news events as the weight of an edge, establishing a relationship network diagram, and calculating a metric for each news event to select hot news events, and obtaining effective hot news events by calculating the synergistic benefit weight results of the selected hot news events; sequentially removing the selected hot news events from the relationship network diagram according to the ranking of the synergistic benefit weight results to obtain a new relationship network diagram, and reselecting hot news events based on the new relationship network diagram to obtain differences from the first selected hot news events, and selecting the hot news event that causes the greatest difference as a key event. This helps to gain a deeper understanding of the connection and influence between hot news events and other news events in news reports.