520 results about "Social web" patented technology

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.

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 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.

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 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.

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 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.

Embodiments provide a social networking platform offering various services, such as, facilitating aggregation and management of a user's interaction on one or more social networking platforms, offering enhanced control over the level of privacy associated with the flow of user data, offering tools to customize the user's exposure to advertisement-related content on the social networking platform(s), integrating features to control aspects of how data / content is presented to and visualized by the user, empowering the user to multicast direct messages to other users without the other users having to meet certain constraints, empowering the user to create and / or join a group based on messaging threads, and the like. One or more of these enhanced services / features are associated with a powerful framework of authentication / permission model for access control.

The invention discloses a mobile social network resource allocation method based on cloud side-end cooperation, and the method comprises the steps: screening out a malicious ES through building a trust evaluation mechanism, guaranteeing the safety of a task unloading process, improving the data transmission efficiency between an MU and the ES through semantic transmission, constructing a two-layer task unloading frame and an incentive mechanism, and achieving the resource allocation of the MU and the ES. And optimizing an unloading strategy of the MU and the ES and a pricing strategy between the ES and the cloud. In the aspect of system composition, a mobile user, an edge server and a cloud server form a cloud edge-end cooperative mobile social network, and a trust evaluation machine comprehensively considers direct trust degree, indirect trust degree, intimacy degree, contribution history and the like, so that trust evaluation based on historical interaction records of the mobile user and the edge server is realized; for task unloading and excitation, an excitation-oriented two-layer task unloading scheme is designed, a Stackelberg game model is constructed to optimize strategies of all parties, and the problems of limited computing resources, high data transmission delay, low communication efficiency, security threat and unreasonable resource allocation of mobile equipment are solved.

The invention relates to the technical field of social network information propagation prediction, in particular to an event propagation prediction method based on group influence. The method comprises the following steps: constructing a social network graph of target event participating users, calculating an activation probability matrix among the users by using historical propagation data, and performing social graph structure enhancement based on a threshold value to obtain a de-noised social graph; applying a graph neural network on the de-noised social graph and introducing information bottleneck constrained self-supervised contrast learning optimization node embedding, and reserving features useful for group division and propagation prediction; dynamically dividing user groups related to propagation according to the similarity and propagation context of de-noising node embedding to form a group-level propagation sequence; based on the group representation, utilizing an attention mechanism and a group relation graph to carry out modeling on propagation influence among the groups to obtain global group representation of the event; global group representation is combined with candidate user embedding, propagation probability distribution is calculated, and future participating users of corresponding events are predicted.

The invention discloses a cross-hotel guest social network construction method based on artificial intelligence, and relates to the technical field of network construction, and the method constructs a multi-level and three-dimensional social portrait network structure, can comprehensively capture the behavior pattern and emotional state change of guests in each functional region of a hotel, and improves the social network construction efficiency. And meanwhile, by constructing a cross-hotel social relation network map and introducing a node influence index evaluation mechanism, the system can identify the potential value and the connection capability of the guests in the social network, construct a complete social network containing a multi-level relation, and improve the matching accuracy and the satisfaction degree of the social experience of the guests. And visual matching of cross-hotel guest behavior modes is realized based on a vectorization analysis technology of behavior characteristic graphs.

The invention discloses a social network malicious propagation node identification method and apparatus, a medium, a device and a product. The method comprises the steps of obtaining propagation data of a target topic in a target social platform; constructing a social network propagation graph of the target topic and generating a node embedding vector by adopting a graph attention network; extracting a node embedding vector and a historical text abstract of the target node, and fusing to obtain a structured natural language cue word; taking a structured natural language cue word as input, and adopting a function fusing classification loss and interpretation generation loss to supervise and finely adjust the initial large language model; evaluating the output quality of the fine-tuned model by using a reward model, and optimizing the parameters of the fine-tuned model through reinforcement learning by taking an evaluation result as a reward signal to obtain an optimized model; and deploying the optimized model to carry out malicious propagation node identification on the social network of the real-time propagation event. According to the invention, malicious propagation nodes in the social network can be accurately identified and decision explanation can be provided.

The invention provides an e-commerce brand promotion system based on social network influence propagation, and belongs to the technical field of e-commerce brand promotion systems. The system comprises a data acquisition module, a node identification module, a propagation dynamics model construction module, a promotion strategy generation module and an execution and feedback module. The data acquisition module obtains social and e-commerce data in a compliance manner and carries out standardization processing. The node identification module screens and classifies KOL through a multi-dimensional index, and determines potential propagation nodes in combination with K-means clustering; the propagation model module optimizes a'social network-SEIR 'model based on SEIR, and selects an optimal path by using a Dijkstrar + maximum flow algorithm; the strategy module generates a hierarchical KOL cooperation and user incentive scheme; the feedback module collects data per hour, dynamic adjustment is performed within 24 hours, and the method is executed in five steps corresponding to five modules. 30 days after application, the brand popularity is improved by more than or equal to 60%, the cost waste rate of small and medium-sized brands is reduced to 15% or below, the market sinking reach rate is improved to 38%, and popularization requirements of industries such as beauty makeup and the like are met.

Systems, devices, and methods for identifying a person's interests based at least in part on their interactions with social networks, where “interests” may refer to a person's hobbies, opinions, behaviors, goals, memberships, or activities, as non-limiting examples. The person's interactions with social networks may include postings of comments, articles, or images, indicating a like or dislike for a posting of another person, “following” an account, performing a search, or the type of networks or social media they engage with (which may be associated with a social or political goal, as a non-limiting example).

Computer-implemented methods, systems, and computer-readable media for a categorized favorite food user generated content / social network are described.

The invention discloses a social network construction method for viewpoint propagation, and the method comprises the steps: firstly determining the network characteristics of a real social network, and providing a basis for the construction of an individual network; secondly, an individual network conforming to the network characteristics is constructed, and viewpoints can be spread on the individual network; then necessary individual attributes and classification tags are added to individual nodes in the individual network so as to reflect the diversity of the individuals in the social network; constructing a topic network according to the logic relationship of the topics, wherein the logic relationship represents the correlation between the topics; finally, the individual nodes are affected by surrounding neighbor individual nodes to make contact with the topics at a certain probability, and therefore connection between the individual nodes and the topics is established. According to the method, the individual network is constructed based on real network characteristics, individual attributes are endowed with differences, topics are taken as communication carriers, and the dynamic process of topic viewpoint propagation can be simulated more truly.

The application discloses a social network relationship prediction method based on a multi-curvature hyperbolic graph neural network, and belongs to the technical field of social networks, and comprises the following steps: constructing a social network graph; constructing a category prediction network comprising a random deletion layer, a multi-curvature hyperbolic space mapping layer, an improved hyperbolic graph convolutional neural network, a dynamic curvature fusion layer and a classifier; training the category prediction network to obtain a category prediction model; obtaining a social network graph to be identified, and obtaining a corresponding fusion feature matrix Zr through the category prediction model; processing Zr into a binary matrix, and determining an edge relationship according to element values in the binary matrix. The application introduces a multi-curvature learning mechanism, so that the model is more flexible and more accurate in matching the real geometric characteristics of substructures in the social network graph, a scaling aggregation factor is introduced during hyperbolic graph neural network feature extraction to adjust the contribution of neighbors, so as to adjust node features, maintain the hierarchical relationship during multi-layer feature extraction, and finally improve the accuracy of relationship prediction.

A system and method for displaying portions of a social network. The method can include: receiving, from a client device, a first transmission including a first selection of a first channel including at least a portion of a map, where the first transmission includes a first identification of a selecting user account of the social network, and where the first channel is configured to access a first layer, and where the first layer includes a first set of social media posts; receiving, from the client device, a second transmission including a second selection of a second channel including at least a portion of a map, where the second transmission includes a second identification of the selecting user account of the social network, and where the second channel is configured to access a second layer, and where the second layer includes a second set of social media posts; applying, by a computer processor, a linking operation to link the first channel and the second channel to generate an aggregated channel configured to access the first layer and the second layer; receiving, from the client device, a request for the aggregated channel; and providing, in response to the request, the aggregated channel for display by the client device.

A system includes a phased array antenna, a signal processing module configured to detect wireless signals received from one or more target devices, a conversion module configured to determine 3D spatial data for the one or more target devices based on the wireless signals, an integration module configured to receive visual data from at least one camera, perform object detection and classification on the visual data to detect and classify one or more objects, and tag the one or more objects with 3D coordinates based on the 3D spatial data for the one or more target devices; and a correlation module configured to synchronize the 3D spatial data from the conversion module with the visual data from the integration module including object detection and classification information for use in a cloud-based augmented reality (AR) content to augment social networking and targeted advertising.

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 application provides a kind of multi-modal graph denoising false news detection method and system based on Transformer, belongs to network data processing technical field, including: obtaining the metadata of source news, including news text, image, video and the historical tweet of forwarding user;Using the detection model trained in advance to process the metadata of the source news obtained, to obtain the result whether the source news is false news.The application can more accurately represent news by multi-modal features;Using the time sequence and hierarchical structure in news dissemination process, effectively modeling the real user dissemination behavior pattern, improves the adaptability of detection result to real scene;Reduce the deviation between graph structure and actual social network, more accurately reflect the information influence relationship between users;The representation method of user and news node is various, by modeling the user interest preference or historical behavior, it is easier to cope with the social network environment with highly personalized user behavior.

The invention discloses a social network community discovery method based on high-order topological information and graph embedding, and the method comprises the steps: obtaining an unauthorized social network, constructing a weight matrix according to a k-core structure and a triangular motif structure of the unauthorized social network, and obtaining a weighted social network; adopting a Node2vec algorithm to obtain embedded feature vectors of nodes in the weighted social network, and forming an embedded feature matrix Z; and k-means clustering is carried out on the embedded feature matrix Z so as to discover the network social community. Through the above mode, the weight matrix is constructed in combination with the k-core structure and the triangular motif structure of the unweighted social network, core node information and common neighbor information of the network are fully utilized, the obtained weighted social network is processed by using the Node2vec algorithm, and the community division effect on a data set can be improved.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for presenting user profiles on a user interface. In some implementations, a server receives a request to view profiles for each of one or more other users from a client device associated with a requesting user. The server applies matching criteria to a profile of the requesting user and profiles for respective users to identify profiles for a sequence of profiles. The server enables sequential display of information representative of the identified profiles on a user interface of the client device, including enabling display of first information representative of a first profile of the identified profiles, determining a user review attribute for the first profile, dynamically adjusting the sequence of profiles based on the user review attribute, and enabling display of second information representative of a second profile of the identified profiles in the adjusted sequence of profiles.

[0009] Methods and apparatuses are provided for assigning users to virtual world servers based on social connectivity. One exemplary method includes receiving a request to connect a client device to one of a plurality of virtual world servers and accessing social network connectivity data of a user account associated with the client device. The method further includes identifying a plurality of other user accounts based on the social network connectivity data, ranking the plurality of virtual world servers based on connections with devices associated with the plurality of other user accounts, and connecting the client device to a virtual world server of the plurality of virtual world servers based on the ranking.

The application provides a social network opinion leader mining method based on GraphSAGE, relates to the technical field of social networks, and comprises the following steps: constructing a social network data set to obtain social network user information; pre-classifying social network users according to the social network user information to obtain a pre-classification result; extracting features from the pre-classification result to obtain feature data and setting initial node features; training a node binary classification model based on GraphSAGE on the feature data through the initial node features to obtain a binary classification model; and classifying social network users that do not participate in training into opinion leaders by using the binary classification model to obtain a mining result of the opinion leaders; and the social network opinion leader mining method based on GraphSAGE fully utilizes user information and topological structure information between users, effectively solves the problem of poor model transferability of other models, and greatly improves the accuracy of the mining result.

The invention relates to the technical field of social network application, in particular to a social media viewpoint evolution simulation method and device based on coupling dynamics, and the method comprises the steps: extracting event topic interaction network data based on social network public information, carrying out the statistics of user historical interaction behaviors, and determining an information transmission network and an internal association network according to the data, the two forms a multi-layer association network; determining a transmission state of forwarding and a text viewpoint baseline for calculating a transmission state and a viewpoint value of a user in the social network data set; and inputting the propagation state and the viewpoint value into a coupling dynamics simulation model, carrying out iterative calculation until the viewpoint value converges so as to obtain a final propagation state and a final viewpoint value of the user, and generating forwarding situation distribution and user viewpoint distribution. Therefore, the problems that errors are generated, public opinion monitoring and early warning and public opinion guide strategy construction are affected and the like due to the fact that a viewpoint evolution model adopts a single-layer propagation structure and coupling modeling is not carried out on propagation and an internal correlation structure in the related technology are solved.

The application discloses a false news detection method and system based on uncertain semantic fusion, relates to the technical field of data mining, and comprises the following steps: acquiring multi-modal data of news in a social network; extracting high-order semantic features of a specific mode from each mode of data by a mode coding module based on uncertainty; modeling uncertainty between modes by a variational attention fusion module, and fusing multi-modal features; and obtaining a category label of target news by using a false news classifier based on the fused multi-modal features. The application can extract more robust high-order features from different modal information of news, further effectively integrate more reliable multi-modal features, and effectively improve the accuracy of multi-modal false news detection.

A rumor detection method based on time sequence emotion features, comprising: processing data obtained from a social network to obtain source posts, reply posts and propagation paths of an event; performing text feature extraction on the texts of the source and the reply to convert them into vector form; performing emotion feature extraction on the texts of the source and the reply to obtain emotion scores of the texts; performing time sequence feature extraction on the texts of the posts to obtain one-hot time sequence vectors; multiplying the extracted emotion features and the extracted time sequence features to obtain emotion time sequence feature vectors of the posts; performing vector splicing on the emotion time sequence feature vectors and the text feature vectors to obtain post representation vectors; inputting the post representation vectors and the propagation paths of the posts into an RvNN neural network to obtain a comprehensive representation of the event; inputting the obtained comprehensive representation of the event into a neural network in a rumor classification module to judge the event and output a rumor classification result.

The invention relates to the technical field of data matching, in particular to a picture and video matching method and system based on multi-modal fusion, and the method comprises the steps: when a matching demander recognizes a matching tag on a to-be-matched picture, obtaining a matched video set according to the to-be-matched picture matching effect data associated with the matching tag; according to the matching video set, guiding the matching demander to update the matching effect of the to-be-matched picture; wherein the matching effect recommendation scene of the to-be-matched picture needs to be predicted according to the social network interest set of the matching demander; further obtaining a recommended target scene; customizing and adjusting the matched video set according to the recommended target scene; and guiding the matching demander to preview the customized and adjusted matching video set. According to the method, the reality sense of the scene is stronger, the phenomenon of scene distortion occurs as much as possible, and the matching accuracy can be improved in a huge and complex virtual scene.

In one embodiment, a method includes, by one or more computing devices, receiving, from a client system at a first location, an audio input from an unknown user; identifying a first user of the online social network who is proximate to the first location; identifying the unknown user as a second user of the online social network based on a comparison of the audio input to one or more voiceprints of one or more candidate users stored by the online social network, respectively, wherein each voiceprint comprises audio data for auditory identification of a unique user of the online social network, and wherein each candidate user is within a threshold degree of separation of the first user within the online social network; and sending customized content to one or more of the first user or the second user based on their social-networking information.