798 results about "Social web" patented technology

The invention relates to the technical field of artificial intelligence, and discloses a multi-agent social network simulation method and system based on a cognitive inference chain, and the method comprises the steps: initializing a multi-agent system comprising a social environment engine, a user portrait engine and a cognitive inference engine, executing a multi-agent social network simulation cycle of a preset round of iteration, in each iteration round, the social environment engine pushes social information to the intelligent agent as external stimulation and activates the intelligent agent to execute an independent decision, the cognitive state of each dimension in the cognitive reasoning chain is updated through large language model reasoning, corresponding social behaviors are generated, and the social behaviors and corresponding cognitive state tracks are recorded; and periodically analyzing historical records to optimize influence coefficients among all cognitive dimensions of the cognitive inference chain, and adjusting an inference strategy of a preset large language model. The simulation of the cognitive process of the intelligent agent is a transparent and traceable evolutionary process, and the complete and understandable simulation of the'observation-cognition-behavior 'cycle is realized.

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.

An apparatus, including a processor which provides an electronic forum which provides a video or audio broadcast of a sporting event and allows users to communicate with one another, post comments, place a bet or bets, receive information regarding bets available, betting odds, changes in betting odds, or analytics information, or report an instance of suspected game fixing, match fixing, or cheating, before, during, or after, the sporting event; a transmitter, which transmits the electronic forum to a user communication device; and a receiver which receives information transmitted from the user communication device. The processor processes an outcome of a bet on the sporting event and determines if the bet is a winning bet or losing bet. The apparatus generates a report and transmits the report to a computer associated with a sport governing body, governmental entity, gaming facility, analytics provider, social network, financial institution, or escrow agent.

The invention relates to the technical field of network information propagation prediction, and discloses a topic propagation prediction method and system based on multi-dimensional feature fusion, and the method comprises the steps: extracting multi-dimensional features, inputting the multi-dimensional features to a decoder layer of Transform for fusion, and constructing a prediction model for topic propagation prediction; the multi-dimensional features comprise user comprehensive influence, text emotion features, time dynamic features and user interaction behaviors. According to the method, through collaborative optimization of the dynamic user modeling module, the multi-modal feature fusion module and the intelligent time sequence analysis module, the social network propagation prediction accuracy is remarkably improved, and accurate prediction of the topic propagation trend in the social network is achieved.

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 provides an intelligent message pushing method and system, and the method comprises the steps: collecting the social behavior data and personal attribute data of a user in a social network; dividing the users into a plurality of groups by using the social behavior data and the personal attribute data based on an ant colony algorithm; analyzing a social relation and an interaction mode among users in the group; collecting message resources in the social network, and classifying and labeling messages; performing collaborative filtering processing on the to-be-pushed message, determining a target user group, and generating a message recommendation list; and pushing the recommended message to users in the target user group according to a preset pushing strategy. The user social behavior data and the personal attribute data are converted into the ant feature vectors based on the ant colony algorithm, similar feature vector ants are gathered through pheromone updating and path selection mechanisms, different user groups are formed, the user group division accuracy is improved, and user requirements are more accurately grasped.

The invention relates to the technical field of computer software, and discloses a large-scale social network simulation method and system based on a dynamic topological structure. The method comprises the following steps: distributing a user portrait and a memory to a corresponding agent, and constructing a social network for the agent; distinguishing a core agent from a conventional agent according to the influence measurement; the core agent is driven by a large language model and interacts with other core agents through a natural language, so that the attitude of a conventional agent to an event is influenced; the conventional agent updates the attitude through the trust weight, and adjusts the social network by using a dynamic link prediction engine; updating the memory of the intelligent agent, pushing a library and a social network; and continuously performing a dynamic evolution process at each time step until the deduction of the event is completed. According to the method, detailed description of individual behaviors is ensured, the expandability of the whole system is also considered, and the decision-making efficiency and the simulation precision of the super-large-scale social network simulator are remarkably improved.

The invention provides a key user identification method and system in a social network, and relates to the technical field of user identification, and the method comprises the steps: constructing a social network graph model according to social network data; calculating a basic centrality index through a graph theory algorithm; dividing the social network data into a plurality of communities, and identifying local roles of user nodes in the plurality of communities; calculating a local centrality index of the local role; generating a global score and a local score; and performing score ranking on the global scores and the local scores, and screening key user nodes. According to the method, through two-dimensional analysis fusing global centrality indexes and local role features, the problem that cross-community bridging nodes and local core users are insufficient in recognition in a traditional method is effectively solved, and the comprehensiveness and accuracy of key user recognition are remarkably improved; and through dual verification of time sequence feature analysis and rule engine verification, the abnormal state of the disguised high-influence user is effectively identified, and the result reliability is remarkably improved.

A modular system designed for privacy-preserving content recognition and supplemental content delivery across web and mobile environments. The system employs lightweight character sampling and vision-based recognition to generate unique content fingerprints without storing or replicating original data. It features a hybrid processing architecture, using local computing resources for intensive tasks while optimizing performance on resource-constrained devices. Core functionalities include multi-method content fingerprinting, real-time monitoring with adaptive sampling, and secure supplemental content association. Operating entirely on the client-side, it complies with website terms of service and privacy regulations. Advanced features include AI-driven content recognition, blockchain-based verification, and granular content targeting through resizable selection interfaces. This technology enables seamless delivery of supplemental content while preserving privacy, reducing resource usage, and ensuring scalability across browsers, mobile applications, and edge devices. It is particularly applicable in industries such as education, retail, and secure data sharing.

The invention provides a knowledge graph link reasoning method, and belongs to the field of artificial intelligence and knowledge graphs. The method comprises the steps that a local structure is reserved through a social triple integrity hypergraph, global association is captured through a social relation semantic hypergraph, and complementary social network double hypergraph representation is constructed; based on composite feature fusion of heterogeneous interaction, homogeneous parallel interaction and overall interaction, and in combination with a hypergraph attention network, relationship-guided dynamic semantic propagation is realized; designing multi-channel comparative learning based on social data; the social triple score is optimized based on a marginal sorting loss function, and a knowledge graph link prediction task and a self-supervision comparison task are fused through a joint learning framework. The problems of insufficient local structure modeling, global semantic information splitting, weak representation generalization ability, high dependence on labeled data and the like existing in knowledge graph link prediction in an existing social network are solved, and link reasoning tasks in a dynamic, sparse and high-noise-disturbance social scene are difficult to effectively deal with.

The invention provides a social network key node identification method and system fusing propagation characteristics, and relates to the technical field of node identification, and the method comprises the steps: collecting various data in real time; the method comprises the following steps: identifying network propagation data to obtain key information data, constructing an enhanced recurrent neural network optimized based on a sodat swarm optimization algorithm, inputting the key information data, outputting to obtain propagation network characteristics, and analyzing the key information data to obtain node propagation performance data. And extracting a multi-factor influence coefficient from the external factor data, constructing a node evaluation system, and screening the preliminary nodes according to the multi-factor influence coefficient to obtain key nodes. According to the method, the recurrent neural network is enhanced, the propagation network features are extracted, the node evaluation system is constructed, the topological structure data are classified, and the key nodes are determined in combination with the multi-factor influence coefficient, so that the understanding and prediction capabilities of the propagation process are improved, and the key nodes are identified more accurately.

The invention discloses a social network denoising method based on a graph neural network, which belongs to the technical field of social networks and comprises the following steps: acquiring social network data; a dual trainable denoising module is constructed, the dual trainable denoising module comprises a structural noise corrector, a feature noise filter and a fusion layer, the structural noise corrector is used for generating mixed features, the feature noise filter is used for generating graph convolution output, and the fusion layer is used for fusing and obtaining a purified user node feature matrix; constructing a loss function L and training to obtain a dual denoising model; obtaining social network data to be denoised, and obtaining a corresponding purified user node feature matrix through the dual denoising model; according to the method, feature-topology non-unification can be effectively reconciled, so that the model can neglect inconsistent noise in features and structures, and the characterization quality in a social network with complex noise is remarkably improved. The purified user node feature matrix has good performance in the classification task.

The invention discloses a social blog management system and method based on Vue and SpringBoot, belongs to the technical field of social networks, and solves the problems that an existing system only focuses on user information collection, activity registration and basic data storage and cannot meet the complex requirements of a blog platform for content creation, personalized recommendation and multi-dimensional social interaction, and the social blog management efficiency is improved. The system comprises a front-end module, a rear-end module and a database module, and the front-end module and the rear-end module carry out data interaction through AJAX and RESTful API (Application Program Interface); according to the system, a front-end and back-end separation framework of Vue.js and SpringBoot is adopted, and a relational database MySQL, a memory database Redis and OSS storage are combined, so that efficient storage and cache management of data are realized, the system performance is optimized, a back-end module can generate personalized recommendation information by analyzing user blog information, the user experience is improved, and the user experience is improved. Rich user management and social contact functions are provided, and interaction between users is enhanced.

A system and method whereby a group of people can privately, or in an online location with a singular purpose, share a website, social network, or other communications for a particular event. A personalized social network can be generated for the group to enhance privacy, security, and ease of interaction. The personalized social network can be associated with a place, time, or other conditions, such that the social network can effectively address a singular purpose, such as a wedding, and all group members can easily communicate information relative to the event.

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 aims to provide a social network advertisement propagation and user behavior analysis integrated system, which belongs to the technical field of marketing data analysis and comprises a data acquisition module, a user portrait module, a propagation path module, a prediction engine module and an optimization regulation module. The data acquisition module acquires social data, advertisement interaction data and user behavior data. The user portrait module constructs a social influence model and generates a user feature tag and an interest map. The propagation path module identifies key propagation nodes, constructs a social network propagation model, and generates network diffusion vectors. The prediction engine module establishes a prediction model, calculates an advertisement interaction probability index, generates a resource allocation and optimal push strategy, generates a performance evaluation signal, and is bidirectionally connected with the optimization regulation and control module. The optimization regulation and control module monitors and predicts the operation state of the engine module in real time and sends out an optimization instruction according to the performance evaluation signal. The system can accurately analyze the user behavior, predict the advertisement propagation path, optimize the advertisement putting effect and improve the advertisement interaction possibility.

Systems, devices, and methods are described herein for calculating a trust score. The trust score may be calculated between entities including, but not limited to, human users, groups of users, organizations, or businesses / corporations. A system trust score may be calculated for an entity by combining a variety of factors, including verification data, a network connectivity score, publicly available information, and / or ratings data. A peer trust score targeted from a first entity to a second entity may also be calculated based on the above factors. In some embodiments, the peer trust score may be derived from the system trust score for the target entity and may take into account additional factors, including social network connections, group / demographic info, and location data. Finally, a contextual trust score may be calculated between the first and second entities based on a type of transaction or activity to be performed between the two entities.

The invention discloses a multi-modal social network public opinion hidden danger troubleshooting method and system, and the method comprises the steps: collecting the multi-modal data of a social platform in real time through a distributed edge node, and the multi-modal data comprise text, image and audio data; dividing a plurality of social network communities based on user interaction relationships, wherein the user interaction relationships include but are not limited to topic circles, friend relationships, comment interaction and forwarding likes; community propagation dynamic features, group topological features and content features are extracted from the multiple social network communities one by one based on the multi-modal data; screening out a plurality of suspected hidden danger communities from the plurality of social network communities based on the community propagation dynamic characteristics, the group topological characteristics, the content characteristics and a preset screening strategy; and after carrying out hidden danger detection and hidden danger classification on the plurality of suspected hidden danger communities based on the multi-modal data, outputting a plurality of hidden danger classifications corresponding to the plurality of suspected hidden danger communities. According to the method and the system, the comprehensiveness and the accuracy of public opinion hidden danger recognition are remarkably improved.

The invention relates to the technical field of computer software, and discloses a large-scale multi-agent collaborative reasoning method and a social network simulation system. The method comprises the steps that users in a social network are simulated through intelligent agents, figure portraits are distributed to all the intelligent agents, and an intelligent agent social network topology is constructed; calculating entropy values of the vertical field numerical values of all the intelligent agents in the previous time step; selecting an intelligent agent with a preset proportion as a core intelligent agent of the next time step; the core agent is driven by a large language model; the conventional agent is driven in parallel by adopting a graph attention situation deduction model based on a graph attention network, and a standing numerical value of a next time step is recursively predicted by aggregating static features and dynamic features of neighbor agents; mapping the standing text of the core agent into a standing value through a scoring device, and replacing the predicted standing value of the current core agent; according to the method, an agent interaction mechanism conforming to social laws can be more effectively simulated.

The invention provides a link prediction method and system based on a double-branch fusion network, and belongs to the technical field of link prediction. The core of the method comprises the following steps: 1) a KAN network is adopted by a graph attention branch to replace a traditional edge weight, and the fitting ability to a complex relationship is enhanced through a learnable nonlinear basis function; and 2) fusing attention branches and connecting SE and CAM channel attention modules in parallel, capturing a long-term dependency relationship through global average pooling, and improving the sensitivity to high-frequency emergence association in combination with double pooling so as to realize local and global feature complementation. And after the two branch features are fused, calculating a node interaction score through a decoder, and outputting a link existence probability. Experiments show that the AP and AUC indexes of the method on data sets such as Yeast are superior to those of a mainstream method. The system executes the processes through a memory and a processor, is suitable for scenes such as social networks and recommendation systems, and has remarkable application value.

The invention relates to the technical field of social network data mining, and discloses a social network data mining method and system based on a graph neural network, and the method comprises the steps: collecting user interaction data in a social network and a timestamp thereof, employing a time perception graph segmentation algorithm, dividing time slices based on a sliding window, and constructing a dynamic heterogeneous graph sequence. A Transform time decay attention mechanism is adopted, influence weights of different time slices are calculated, and weighted aggregation is carried out on node features of all the time slices; and inputting the weighted time slices into the D-GNN model, generating a time-perceived user embedding vector, and analyzing the influence of the social network. According to the method, the model better conforming to the actual dynamic change of the social network is constructed, and the accuracy and adaptability of data mining are improved. Calculation efficiency is improved on large-scale social network data, and availability of the model in industrial application is ensured. And social influence analysis is carried out, and technical support is provided for practical applications such as precision marketing, public opinion monitoring, social recommendation and the like.

The invention relates to a financial science and technology multi-dimensional trust evaluation method and system based on a block chain, and the method comprises the steps: collecting on-chain transaction data of a financial subject and encrypted and verified off-chain multi-source data through a smart contract disposed in a block chain network; constructing a credit model comprising at least four orthogonal evaluation dimensions, wherein the dimensions comprise on-chain behavior credibility, off-chain asset verification degree, social network influence and historical performance volatility; dynamically adjusting the weight coefficient of each dimension by adopting a time sequence prediction model, and writing a weight adjustment result into the block chain after the weight adjustment result is verified by a consensus mechanism; and generating a final trust evaluation result containing the Merkle proof, and storing a result hash value to a block chain non-tampering database. According to the financial science and technology multi-dimensional trust evaluation method and system based on the block chain, by fusing on-chain behaviors, off-chain assets, social networks and historical performance four-dimensional orthogonal data, the default prediction accuracy is improved, and the long-tail user coverage rate is improved.

The invention relates to the technical field of network security situation awareness and early warning, and discloses a photovoltaic power station network security situation awareness and early warning method and system, and the method comprises the steps: carrying out the multi-source heterogeneous data collection of a photovoltaic power station and a social network, and carrying out the distributed preprocessing and feature extraction of the collected data, carrying out weighted fusion analysis on the comprehensive feature data through a situation awareness model, predicting potential attacks through an attack prediction model, calculating a network security situation in real time through a deep belief network, and carrying out automatic attack blocking by a centralized control center and a plant station operation and maintenance terminal; encryption protection is carried out through a forward isolation device and a VPDN tunnel technology. According to the invention, situation awareness and risk prediction are carried out through a deep learning algorithm, automatic attack blocking and headquarters-plant-station intensive management and control are realized in combination with a dynamic early warning mechanism, a trapping system and a block chain technology, and the monitoring, early warning and protection capabilities of the network security of the photovoltaic power station are significantly improved.

A structure and passenger transport paradigm for accommodating passengers in a vehicle with particular attention paid to safety, utility and comfort, social networks including a technology configured to enable community engagement in personal protection and methodologies for virtual visualization and navigation in a remote physical space and provides a mechanism for verification and authentication of video and other evidence.

The invention provides a dynamic credit risk assessment system and method based on multi-source data, and relates to the technical field of financial science and technology, and the system comprises a multi-source data collection module which is used for integrating internal data of a financial institution, public credit investigation data and Internet behavior data, and achieving safety data fusion through a privacy calculation technology; and the feature engineering module is used for extracting statistical features (such as transaction frequency), text features (such as social media sentiment analysis) and graph features (such as social network centrality), and constructing a novel credit risk assessment model in combination with dynamic behavior data and static data. The model not only considers the historical credit record and financial condition of the borrower, but also incorporates the behavior pattern and social network information of the borrower, so that the credit risk of the borrower can be evaluated more comprehensively. Through the application of a machine learning algorithm, useful features can be automatically learned and extracted from massive data, and the accuracy and efficiency of credit rating are improved.

The invention belongs to the field of data detection, and provides a false news detection method and system based on fact-emotion dual uncertainty, and the method comprises the steps: obtaining a text and an image of each piece of news in a social network; the method comprises the following steps: performing feature extraction on a text and an image to obtain text embedding and image embedding, performing Gaussian reweighting on the text embedding and the image embedding respectively, then performing calculation to generate Gaussian uncertainty representations, and screening the Gaussian uncertainty representations based on a variational information bottleneck strategy to obtain multi-modal uncertainty representations; processing the multi-modal uncertainty representation to generate a fact inconsistent representation, and processing the multi-modal uncertainty representation based on an emotion manipulation graph convolutional network to generate an emotion inconsistent representation; and fusing the fact inconsistent representation and the emotion inconsistent representation to generate fusion features, and performing classification based on the fusion features to obtain a false news detection classification result. According to the method, the limitation of cross-modal complementarity caused by the uncertainty of multi-modal data is relieved.

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 relates to the field of influence measurement, and discloses a social media-based content influence measurement method, which comprises the following steps of: constructing a social network graph according to collected user social relation data; each user is a node, interaction between the users is an edge, and features in the social network are analyzed; modeling a propagation path of the information by adopting a propagation model, and weighting the weight of the information propagation according to the social relationship between the users; establishing a weight model, endowing each edge in the propagation path with different weights according to the strength of different social relationships, and strengthening the influence of the core social group on content propagation; evaluating the propagation breadth and propagation speed of the content in the social network, analyzing the participation behavior of the user, and evaluating the propagation effects of the content in different social circles; and training the data by using a machine learning algorithm, and optimizing the propagation effect prediction model. The method has the advantage of accurately evaluating the actual propagation effect and influence of the content.

The present disclosure is directed towards implementing a video monetization service into a social networking platform to form an information marketplace, enabling users to monetize information and digital content. A creator of a user generated content can configure the user generated content to his / her user preference and invite as many participants, e.g., all associations, a limited group of associations, including just one association, or other social media users in general. The associations may be followers, circles, friends, connections, or any other form of association.

The invention discloses a personalized federated graph learning method oriented to equipment resource isomerism, and aims to solve the defects in graph data isomerism, equipment resource adaptation and privacy protection in the prior art. According to the method, collaborative optimization is realized through a closed-loop process of local pre-training, embedding aggregation, personalized training, soft label collaboration and classifier distillation. Each client pre-trains a model based on a local graph data set and generates interlayer embedding, and uploads the model to a server after sampling and privacy enhancement; the server performs aggregation to form a public embedded data set and distributes the public embedded data set to the client to support distillation training and soft label generation; the soft labels are filtered and subjected to weighted aggregation to form global soft labels, and the client completes classifier knowledge distillation by combining the public embedded data set and the global soft labels, and iteratively optimizes the performance of the model. According to the method, the generalization ability and the resource utilization efficiency of the model are remarkably improved while the data privacy is guaranteed, and the method is suitable for distributed graph data training tasks of multiple scenes such as social networks.