126 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 provides an influence maximization method and system based on deep reinforcement learning and a graph neural network, and belongs to the field of influence maximization in social network analysis. The method aims to optimize selection of seed nodes through a reinforcement learning technology so as to realize maximum propagation of information in a network, and the graph neural network can accurately capture an interaction relationship between nodes in a graph structure by virtue of a message transmission mechanism of the graph neural network, which is highly matched with an information propagation process in a cascade propagation model. Therefore, in the influence maximization model based on the deep reinforcement learning and the graph neural network, the graph neural network is combined with the deep reinforcement learning, the embedded representations of the nodes are learned by using the graph neural network, and the embedded representations are used as the input of the deep Q network so as to fully capture the complex structure and node relationship in the network. In addition, a multi-head attention mechanism is introduced to aggregate information of each round, expression of key features is enhanced, and information redundancy is effectively reduced.

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 discloses an organization internal communication efficiency improvement method based on social network analysis. The method comprises the following steps: S1, collecting internal communication data of an organization; s2, preprocessing and standardizing the collected data; s3, constructing a communication network graph, and identifying communication frequency and intensity; s4, identifying communication bottlenecks and key nodes in combination with a social physics model and a graph attention network; s5, introducing a network fluid dynamic model and a swarm intelligence method to optimize a communication strategy; s6, continuously optimizing the network structure based on an adaptive optimization method and a context awareness technology; and S7, generating personalized communication optimization suggestions, and dynamically displaying communication network changes. According to the invention, the communication efficiency and decision-making ability in the organization are effectively improved.

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.

The invention discloses a workshop data integrated control model optimization method based on a graph mining algorithm, which comprises the following steps of: firstly, acquiring workshop equipment data, preprocessing, defining a node for each piece of equipment, distributing a unique identifier for each node, determining attribute information of each node, and constructing a workshop equipment relation network model based on the attribute information; and finally, carrying out importance evaluation on nodes in the workshop equipment relation network model based on a PageRank algorithm, and optimizing the constructed workshop equipment relation network model by utilizing a social network analysis algorithm. According to the scheme, the graph mining algorithm is used for generating the ship intelligent cabin workshop relation social network model for the equipment of the whole ship intelligent cabin workshop, the ship intelligent cabin workshop relation social network model is optimized, the production process is deeply understood by deeply mining the relation between the equipment, evaluating the importance of the equipment and discovering the group relation, and the production efficiency is improved. The method has real-time performance and dynamic adaptability, can be updated at any time to adapt to the change of the workshop, and can enable workshop workers to conveniently analyze the importance degree of workshop equipment and the dynamic relationship between the workshop equipment.

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 is applicable to the technical field of knowledge migration, and provides a graph domain adaptive node classification method based on instance learning, which comprises the following steps of: acquiring a source domain data set with a mark and a target domain data set without a mark; the representation of the source domain data set and the representation of the target domain data set are learned through a GNN model, modeling is conducted on the source domain data set and the target domain data set, and embedded vectors of the source domain data set and the target domain data set are obtained; the embedded vectors of the target domain data set are classified through KNN, and target domain category pseudo labels are obtained; performing fine-grained instance contrast learning on embedded vectors of the source domain graph data set and the target domain graph data set; and after classification is finished, displaying a final classification result of the target domain graph data set. According to the method, the defects of an existing graph domain adaptation technology in cross-domain tasks are overcome, and the method has wide applicability and can be applied to graph data analysis tasks in various cross-domain scenes, such as social network analysis, recommendation systems, knowledge graphs and biological networks.

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 is suitable for the field of social network analysis, and provides a social public opinion hypernetwork key public opinion element identification method based on a structural hole, and the method comprises the steps: collecting public opinion event related data; constructing a social public opinion hypernetwork model of five layers of subnets; constructing a conversion hypergraph, and constructing a conversion line graph according to the conversion hypergraph; and calculating the sum of scores of all hyperedges formed by node participation, and calculating the final score of the node. According to the method, the development situation of public opinion propagation is researched by utilizing a multilayer super network model, a structural hole theory is introduced into a method for identifying public opinion elements, and a conventional hyperedge sorting method is improved to a certain extent; compared with the traditional method, the method provided by the invention can effectively and accurately identify various key public opinion elements in the social public opinion super network, and can analyze by combining with the node importance index of the super network, so that the key public opinion information can be interpreted from multiple dimensions and multiple perspectives.

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 belongs to the field of social network analysis, and particularly relates to an early guide type topic detection method based on emotion accumulation and dynamic graph representation, which comprises the following steps: obtaining topic data generated before and after topic outbreak, including basic information, comment information and friend relationship of comment making users in topics; establishing a relation weight matrix between the users according to theme distribution similarity of historical comments made by the users and emotional influence of the users; converting the basic information of the user into a vector representation; and inputting the vector representation of each user under the topic and the relation weight matrix between the users into a graph attention network based on time coding, and judging whether the current topic belongs to a guide type topic or not. The guiding type topic can be effectively recognized, and the method is crucial for timely controlling further evolution of guiding type topics such as rumors and correctly guiding network public opinions.

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 relates to a complex network node influence sorting method and system based on structure entropy, and belongs to the technical field of social network analysis. The method comprises the following steps: constructing a social network graph according to a data set, and carrying out community discovery by utilizing two-dimensional structure entropy to obtain stable overlapping communities; calculating the influence of the nodes in the community and the influence of the nodes across the community, and performing linear combination on the influence of the nodes in the community and the influence of the nodes across the community to obtain a comprehensive influence value of each node; and sorting all the nodes according to the comprehensive influence value, and identifying the key node with the maximum influence in the social network. According to the method, the importance of the nodes in the social network is evaluated by measuring the two-dimensional structure entropy change of the nodes in the communities and among the communities, so that the key nodes are identified and sorted, and the method for effectively identifying and sorting the key nodes is provided for obtaining the important nodes in the social network.

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 relates to the technical field of social network analysis, and discloses an anti-social network user identity association method based on a greedy strategy. In order to effectively model and solve an anti-social network user identity association problem and balance cost and income, an account injection scheme based on a greedy strategy is provided, and an attack problem on a user identity association model is modeled as a problem of maximizing a determined error pairing number. The similarity score of the pairs is certainly greater than the similarity score between the existing account pairs, so that the attack problem associated with the user identity of the social network can be solved; on the basis of a greedy strategy, a vulnerability evaluation method based on an account network role is adopted to identify vulnerability, and progressive attacks from easiness to difficulty are realized; meanwhile, an injection strategy searching method based on a greedy strategy is designed and used for determining which existing accounts should initiate friend connection between the injection account and the existing accounts, and therefore efficient attacks are achieved at low cost.

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.