82 results about "Social network analysis" patented technology

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.

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 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 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 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 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 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 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.

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 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.

The application belongs to the field of social network analysis, and particularly relates to a dynamic social network alignment method based on longitudinal federation and canonical correlation analysis, comprising the following steps: simulating the spatio-temporal relationship of users through a dynamic spatio-temporal graph self-encoding memory model and an attention mechanism to obtain a user relationship matrix; constructing a user attribute matrix and fusing the user relationship matrix to obtain a user matrix; inputting the user feature matrices of platforms X and Y into a model for training through a training model based on federated learning to obtain a prediction result of cross-domain user alignment; and updating and modeling the dynamic relationship representation in combination with the time sequence characteristics of user relationship and fusing other non-time sequence characteristics for cross-platform user alignment prediction. Through the method, the problems of cross-domain data privacy leakage and social network dynamics can be effectively solved, and finally precise cross-platform network user alignment is realized.

The invention discloses a subgraph editing distance calculation method based on a graph neural network, and relates to the field of graph calculation and artificial intelligence, and the method comprises the following steps: obtaining a query graph and a target graph; generating node-level and edge-level representations for the query graph and the target graph through a unified graph isomorphism encoder to capture the influence of node and edge specific editing operation on graph topology; inputting the target graph into a self-adaptive graph mask module, generating mask scores of nodes and edges through a gating attention mechanism in combination with query graph representation, shielding irrelevant parts and dynamically balancing graph scale differences; and performing multi-head mask processing on the target map based on the mask score to generate a plurality of candidate substructures. According to the method, asymmetry can be effectively processed, fine-grained structure differences are captured, calculation precision and efficiency are improved, and the method is suitable for scenes such as drug discovery, social network analysis and recommendation systems.

The invention relates to the technical field of social network analysis, and discloses a method for identifying important nodes in social network data, a terminal and a storage medium. The method comprises the following steps: acquiring social network data and calculating semantic similarity between posters; the method comprises the following steps: mapping users, stickers and topics into nodes, mapping attention, interactive behaviors and similarity relationships into edges, and constructing a multi-relationship heterogeneous graph; secondly, defining a meta path to capture high-order topological dependence, performing multi-hop neighborhood aggregation on nodes, and fusing different path characteristics by using a self-attention mechanism to obtain high-order structure characteristics; meanwhile, a unified semantic space is constructed based on a pre-training language model, and semantic embedding representation of nodes is extracted. And finally, the high-order structure features and the semantic features are deeply fused to generate joint representation, and scores are calculated based on the joint representation so as to accurately identify important nodes. According to the method, the hierarchy and potential influence of the nodes in the network can be comprehensively described, so that the accuracy and robustness of important node identification are improved.

The invention discloses a small sample learning method suitable for single power grid project investment execution risk management evaluation, which relates to the technical field of power grid investment management, and comprises the steps of establishing an evaluation index system, screening and extracting key indexes, and establishing a small sample learning method based on a semi-supervised prototype network. According to the method, a semi-supervised form of a prototype network is further defined on the basis of the prototype network, then typical annotations are generated by using kernel density estimation, and finally empirical analysis is carried out. Through screening and defining of multi-stage key evaluation indexes, in combination with social network analysis and an interpretation structure model, process type, driving type and result type key indexes are comprehensively identified; the semi-supervised prototype network and kernel density estimation are innovatively applied, so that risk classification and management can be effectively carried out under the condition of small sample data, and the actual problem of lack of big data is solved.

The present invention discloses a public opinion evolution analysis model that integrates network game theory and opinion dynamics, as well as its establishment method and application, belonging to the field of social network analysis technology. The public opinion evolution analysis model in the present invention combines social network game theory and opinion dynamics theory, and constructs a mathematical model of public opinion evolution (POEM) based on relevant theories in social science while considering more comprehensive factors. It realizes the scientific simulation of the psychological activities of individuals in the process of public opinion dissemination, breaking through the two major limitations of single behavior statistics in traditional network game models and the lack of behavioral characterization in opinion dynamics models; and uses uncertainty mathematical theory to characterize the psychological trends and decision-making behaviors of social individuals in the process of public opinion evolution, more accurately depicting the impact of individual dissemination behavior on the evolution of social public opinion.

The application discloses a social network influence maximization privacy protection method based on zero-knowledge proof and belongs to the technical field of cryptography and social network analysis, and comprises the following steps: constructing a network topology structure of a social network; generating a network commitment based on a Commitment circuit according to the network topology structure; after topological sorting according to the network topology structure, obtaining a final seed node set and a final proof set by using a greedy algorithm; compressing the final proof set by using a tree-shaped proof recursive method to form a final proof; and taking a tuple formed by the final proof, the network commitment, the final seed node set and a final influence diffusion value as a social network influence maximization privacy protection result. The method can provide a verifiable influence maximization calculation result to a third party on the premise that the network structure of the social network is completely private, and ensures efficient operation on an actual social network.

The application provides a symbol graph-oriented influence maximization method and system, and the method comprises the following steps: acquiring symbol graph data, and performing representation learning on nodes by using a symbol-aware attention layer to generate symbol-aware node embedding; a seed set representation module based on a variational autoencoder is constructed, a conflict-aware regularization term is introduced to punish negative relationships in the seed set and encourage positive cooperation; a double-path diffusion model is used to simulate the propagation dynamics of positive and negative influences to obtain node net influence; the modeling capability of a complex teacher model is transferred to a lightweight student model through knowledge distillation; finally, a hybrid reasoning strategy is used to output a seed node set with high net influence and minimum internal conflict. The application can effectively process positive and negative edge heterogeneity information in the symbol graph, accurately simulate the inhibition effect in the influence propagation, generate a high-quality seed set, and has important application value in the fields of social network analysis, viral marketing and the like.

The invention discloses a microscopic cascade prediction method and system based on user portrait decoupling and multi-source signal fusion, and belongs to the field of artificial intelligence and social network analysis. The method comprises the following steps: firstly, learning user static representations in a social network and a historical cascade graph by using a graph neural network, and obtaining community-level representations through clustering; secondly, the cascade data is constructed into a multi-time granularity hypergraph sequence, and a hypergraph attention network is used for learning time sequence dynamic representation of a user; then, through a double-branch parallel architecture, individual features and common features of the user are decoupled from the static and dynamic representations respectively, and constraints are introduced to optimize feature learning; then, adaptive gating fusion is carried out on the two types of features, and unified user representation is formed; and finally, predicting a next user most likely to participate in the information cascade based on the unified representation. According to the method, the accuracy and the interpretability of micro cascade prediction are improved by decoupling the heterogeneous behavior mode of the user and fusing the multi-source information.

The present invention relates to the technical field of social network analysis and generative artificial intelligence, and discloses a method for simulating complex social behavior and deducing public public opinion based on multiple agents. Social groups are divided, and users in a social network are divided into key opinion leader communities and ordinary user communities; key opinion leader agents and ordinary user agents driven by a large language model are respectively constructed; a dynamic weighted directed network is constructed, and each key opinion leader agent is modeled as a node. The neighborhood of the network node is weightedly calculated by the opinion index and the node influence, and the sentiment score is calculated based on the text sentiment index. The neighborhood of the network node is dynamically adjusted based on the sentiment score, and the method is iterated several times to complete the deduction and simulation of network public opinion. The method solves the problem of difficulty in modeling the dissemination of public opinion in a cross-domain dynamic context, and can more accurately simulate the differences among individual public opinion users and complex social interaction relationships. The method is practical and feasible.

A method of streamlined selection and application of a makeup look involving personalized makeup look recommendations with respective step-by-step makeup routine instructions for executing a selected makeup look. A predictive model identifies one or more makeup look recommendations from a database of makeup looks to present to a user. Recommendations are generated from a combination of user demographic data, user-input preferences, social networking analytics (e.g., frequently used makeup looks among users such as “trending” makeup looks), and user behavioral history data obtained from a smart device application and a smart cosmetic device system. Each respective makeup look can be executed from a respective detailed tutorial guide comprising step-by-step instructions on how to apply cosmetic substances to achieve the respective makeup look.

The invention relates to a graph enhancement method and system based on core subgraph retention and structure compression and a storage medium, and relates to the technical field of graph contrast learning. According to the method, a graph augmentation strategy combining core subgraph extraction and information compression is provided for solving the problems that in existing graph contrast learning, random augmentation is prone to damaging a semantic structure, and redundant information is introduced. The method comprises the following steps: encoding an input graph by using a flexible connection GNN, and identifying and retaining a core sub-graph with a stable structure and dense semantics through adaptive frequency spectrum filtering; and an enhanced view is generated on the basis of the core subgraph, semantic consistency is kept by using comparison consistency loss, and compression loss based on an information bottleneck principle is introduced to remove redundant information. According to the method, noise can be effectively suppressed while the semantic core of the graph is reserved, the discrimination and generalization performance of representation are improved, and the method is suitable for various graph representation learning tasks such as molecular modeling, social network analysis and recommendation systems.

The invention relates to a dynamic authority allocation method and device for cross-department knowledge sharing and security isolation, computer equipment, a storage medium and a computer program product. The method comprises the steps that user access logs are collected and preprocessed, modeling is conducted on a user behavior sequence through a recurrent neural network model for the preprocessed user access logs, and a behavior analysis result is obtained; constructing an enterprise knowledge graph in combination with natural language processing and social network analysis technologies, and generating a knowledge demand evaluation result according to the enterprise knowledge graph; determining an optimal permission scheme in combination with a behavior analysis result and a knowledge demand evaluation result; according to the user access context and the behavior analysis result, matching an adaptive security isolation strategy from a preset strategy library; and executing real-time permission updating according to the optimal permission scheme and the security isolation strategy. By adopting the method, the management cost and the compliance risk can be reduced, and the method has high expandability and scene adaptability.