242 results about "Social network" patented technology

The invention provides a private domain traffic scheduling and content distribution method and system based on group behavior analysis, and relates to the technical field of private domain traffic operation, and the method comprises the steps: constructing a user social network through analyzing the historical behavior data of a user, calculating the influence weight of the user, and generating a user group portrait in combination with the long-term and short-term interest distribution of the user. And performing semantic analysis on the candidate content, constructing a content propagation network, calculating a content propagation coefficient and a matching coefficient of the content and the user group, and generating a target content sorting table. User active time period distribution is calculated according to the user activeness prediction model, a content delivery strategy is generated in combination with the target content sorting table and the matching coefficient, and the delivery strategy is dynamically adjusted according to the delivery effect index, so that accurate content distribution is realized, and the private domain traffic operation efficiency is improved.

The invention provides a network security risk assessment system and method, and relates to the technical field of network security. The network security risk assessment system comprises a data acquisition layer, the data acquisition layer is connected with an analysis processing layer, the analysis processing layer is connected with an assessment decision-making layer, the assessment decision-making layer is connected with a display interaction layer, and the data acquisition layer acquires business scene data, network topology data, user behavior logs and social network conditions. According to the system, through highly integrated module design, 'information isolated island 'between traditional security tools is broken, comprehensive security situation awareness is realized, novel and unknown threats are dealt with by utilizing situational analysis and dynamic adjustment capability, false alarms and missing alarms are remarkably reduced, and the system is based on machine learning and behavior analysis. Intelligent priority ordering and automatic response can be realized, and the speed and accuracy of risk identification and response are improved.

The invention discloses a user social behavior-based public opinion propagation prediction method and system. The method comprises the following steps of S1, perfecting and dividing data set categories according to data set forms and contents; s2, performing sentiment analysis according to the content of the comments forwarded by the user, and performing preliminary clustering on themes of the tweets forwarded by the user; s3, fusing with user theme features, and performing feature construction; s4, constructing a feature module of the user node, and pruning the whole social network according to the propagation condition; s5, calculating the credibility degree and the susceptibility degree of the user according to the historical forwarding data to construct a mask matrix, and performing similarity evaluation on the propagation network of the specified event; and S6, constructing a propagation prediction result evaluation index. According to the method, the dependency and independence of the user are combined, the user topic features are combined, the user node features with more practical significance are obtained, and compared with traditional public opinion analysis and prediction, the method has higher accuracy.

The invention discloses a multivariate heterogeneous data talent evaluation system and method based on an intelligent atlas, and the system comprises a multi-source heterogeneous data collection module, a heterogeneous knowledge atlas construction module and a personalized recommendation module, and forms a heterogeneous atlas neural network through obtaining and determining a heterogeneous network data source. Based on the knowledge distillation technology, complex social network entity links and figure portraits of figure relations are established, talent data after knowledge distillation are generated, and in combination with skill talent evaluation and intelligent recommendation of the knowledge maps, knowledge complementation among multiple heterogeneous knowledge maps is achieved; according to the method, the integrity of knowledge is effectively guaranteed by utilizing a distributed data storage mechanism, the figure portrait is good in accuracy and high in credibility, meanwhile, accurate semantic understanding is achieved, the talent evaluation accuracy is high, and intelligent recommendation for different individuals is achieved.

The invention relates to the technical field of medical care, in particular to an AI-based personalized mental health assessment and intervention method, which comprises the following steps: S1, data acquisition: acquiring multi-dimensional data of a user; s2, data preprocessing: preprocessing the collected multi-dimensional data; s3, psychological health portrait construction: generating a personalized psychological health portrait of the user; s4, social relation psychological influence analysis: identifying an emotion change mode, a negative emotion propagation path and a social isolation risk of the user in social interaction, and dynamically analyzing emotion propagation characteristics in a social network structure and potential influence factors of a user psychological state; s5, making a personalized psychological health intervention scheme: generating the personalized psychological health intervention scheme; the method disclosed by the invention not only reveals the potential influence of social interaction on mental health, but also provides a data-driven scientific basis for further personalized mental health intervention.

Disclosed is a fraud-related gang incident recognition system and method based on intelligence sharing and graph calculation, and a related device. The solution mainly comprises an intelligence sharing platform and an incident model based on graph calculation. The intelligence sharing platform integrates a plurality of trained recognition models, establishes a model intelligence sharing system, and summarizes multivariate information of each model in real time. The incident model based on graph calculation is configured to perform data exchange with the intelligence sharing platform. The incident model based on graph calculation is configured to classify nodes based on a graph neural network, and perform analysis on same on the basis of economic characteristics and social characteristics, to construct a social network graph of the incident, further identify overlapping gang structures on the basis of the social network graph, and transmit the overlapping gang structures to the intelligence sharing platform for recognition processing. In the solution of the present invention, gang organizations in social networks can be effectively discovered and identified by combining a graph calculation method of node attributes and a network topology structure and by using an intelligence sharing platform.

The invention provides a rumor simulation analysis method and system based on a large language model agent, and the method comprises the steps: determining the feature information of an initialized agent according to a preset propagation network topology structure, and constructing a set number of role agents; based on the to-be-processed speech of the role agent at the current time step, simulating a human thinking activity through a large language model to obtain a variant rumor generated by language propagation of the role agent at the current time step; completing rumor propagation simulation based on the propagation network topology structure and the variant rumors of each time step; in each round of time step, determining the propagation probability between the role agents according to the propagation distance and the credibility of the role agents; and after multiple rounds of iteration, determining the role influence corresponding to each role agent according to the propagation probability and the viewpoint intensity of the role agent. The rumor propagation simulation method and device achieve the technical effect of performing high-precision simulation on the rumor propagation process in the complex social network.

Embodiments extract a key phrase from an online profile of a user of a professional social network (PSN) and identify a set of skills associated with the key phrase. A set of skill embeddings corresponding to the identified set of skills may be retrieved from a store of skill embeddings. The skill embeddings may be pre-created by a large language model (LLM). The retrieved skill embeddings may be aggregated to create a skill-centric digital representation of the user. Based on the skill-centric digital representation of the user, a subset of digital documents may be identified to present to the user via the PSN.

The invention relates to the technical field of path planning, in particular to a personalized path planning method and system based on federated learning and edge calculation. Comprising a model training stage and a path planning stage. In a model training stage, a client quantifies travel experience of a user in an access region into a credibility score of a path, optimizes the credibility score of the path according to a path selection frequency in a user social network, and locally stores the credibility score of the path in a known region; the clients upload the path trust degree scores of the clients to the central server, global aggregation of the trust degree scores is carried out, and then the central server issues the aggregated trust degree scores to the clients; the invention discloses a personalized path planning method and system based on federal learning and edge calculation, provides an evaluation dimension of path trust degree for the personalized travel problem of a user, and can provide personalized path planning according to the personal preference of the user.

The invention relates to an abnormal data identification method and device, computer equipment and a storage medium. The method comprises the following steps: constructing a user social network graph according to user communication information; dividing risk groups according to the user login information and the user transaction information to obtain high-risk groups; performing association marking in the user social network graph according to the high-risk group to obtain a potential abnormal group; marking a target user to be identified for the user login information and the user account information of the potential abnormal group; and obtaining the logout times of the to-be-identified target user, and marking the to-be-identified target user as target abnormal data according to the logout times. The method and the device can be applied to financial anti-fraud application scenes, target abnormal data with fraud risks in users can be effectively and accurately identified, and the identification reliability is improved.

The invention belongs to the technical field of character pattern recognition, and particularly provides an oracle structure recognition method of a font pattern isomorphic network, which comprises three stages of font pattern structure feature extraction, font pattern isomorphic network model and oracle font structure recognition. Through oracle font skeleton extraction, oracle font skeleton singular point detection, skeleton burr and bifurcation optimization, and key point extraction as a node communication path as an edge, an oracle font graph structure is established, and graph structure nodes, edges and graph level features are calculated. The font graph isomorphic network integrates the advantages of a graph isomorphic network and dynamic edge and graph embedding, and learns vector representation of multi-level features such as nodes, edges and graphs of the oracle font graph structure so as to train and identify the font structure of the oracle. The method can be used for structure matching recognition of oracle character pattern images, has the advantage of being high in recognition accuracy, and is suitable for recognition of character pattern structures such as gold texts and seal scripts and recognition of social networks, biological information and molecular structures.

The invention relates to a community structure discovery-oriented large-scale complex network visualization method. The method comprises the following steps: acquiring a large-scale complex network; extracting topological structure features of the large-scale complex network by using a representation learning technology to obtain a node representation vector set; performing dimension reduction processing on the node representation vector set by using PCA and t-SNE algorithms to obtain an output vector set; recursive clustering is carried out on the output vector set, each level in the hypergraph is divided, a community formed after clustering of each level is constructed as a super node, the relation between nodes in the community is combined as a hyperedge, and a hypergraph structure is constructed; and visualizing the hypergraph structure by using a diversified visualization algorithm. By adopting the method, the visualization effect of the complex social network can be improved.

The invention discloses a telecommunication fraud detection method based on high-order information enhancement and partition information aggregation, and the method comprises the steps: constructing a telecommunication user social network graph, and taking the graph as a telecommunication fraud detection data set; performing data enhancement on the data set according to the characteristics of the social network graph of the telecommunication users; training a telecommunication fraud high-order information enhancement model, and generating embedded representations of nodes; training a partition information aggregation telecommunication fraud detection model; and performing fraud detection by using the trained partition information aggregation telecommunication fraud detection model to obtain a node detection result. According to the method, the collaborative fraud detection capability is enhanced, the adaptability of the graph neural network to the heteromorphic neighbors is improved, the expression capability of the graph neural network is improved, and the performance of telecommunication fraud detection is further improved.

The invention provides an interest point recommendation method and device for heterogeneous multi-graph representation learning, equipment and a medium, and the method comprises the steps: obtaining a set of users and POIs containing multi-dimensional situation information in a location social network, building a user-POI interaction graph, and extracting feature vector representation of the users and the POIs in an interaction space by adopting a neighborhood similarity measurement method; based on user sign-in time information and text description information, calculating a semantic information similarity distance and a time pattern similarity distance, constructing a user-user perception graph, and learning feature vector representation of the user in a social space; constructing a distance hypergraph by utilizing POI position information, and obtaining feature vector representation of the POI in a position space after dimension reduction clustering processing; and fusing each feature vector representation in the social space and the interaction space as well as the position space and the interaction space, extracting a user representation vector and a POI representation vector, and predicting user preferences by using a neural network model to realize POI recommendation.

The invention provides a fraud detection method based on a side information enhanced graph neural network, and the method comprises the steps: S1, constructing a training set which comprises entities in a social network, features of the entities, and relation types between the entities; s2, constructing a detection model; s3, constructing a model loss function; s4, training a detection model by using the training set in the S1; and S5, performing fraud detection by using the trained detection model, and outputting a detection result of the entity. The problems that an existing fraud detection method is insufficient in precision and low in detection accuracy are solved.

The invention discloses a robot detection method and system based on a sample equalization strategy and a heterogeneous graph, belongs to the technical field of robot detection, effectively relieves the problem of unbalanced data distribution in robot detection through the sample equalization strategy, and combines the multi-relation modeling capability of the heterogeneous graph to improve the robot detection efficiency. And attention behaviors and semantic similarity among the users are fully mined, and the recognition precision of the social robot is remarkably improved. And feature information in different relational graphs is layered and aggregated by using a graph neural network, and multi-modal data is dynamically fused through a semantic attention mechanism, so that the analysis capability of the model on a complex social network relationship is enhanced. Meanwhile, noise interference is reduced through cooperative application of oversampling and undersampling, the generalization performance of the system on the camouflage behavior of the novel robot is further improved, and the robustness and reliability of a detection result in different application scenes are ensured.

The invention provides a psychological health abnormity reason tracing method and system based on a social behavior complex network, and the method comprises the following steps: obtaining user social network data, and carrying out the preprocessing of the user social network data; constructing a user social behavior complex network based on the interaction relationship between the users; evaluating the current mental health state of the user based on the social network data of the user; performing anomaly detection according to an evaluation result, and performing reason tracing through the user social behavior complex network; and providing mental health intervention suggestions according to the abnormal detection result and the traceability result. According to the method, information can be effectively extracted from massive, complex and multi-dimensional social data, so that accurate evaluation of individual mental health states and tracing of abnormal reasons are realized.

The invention relates to the technical field of information technology, in particular to a decentration-based peer-to-peer group social network system and a building method thereof, and the method comprises the following steps: setting network nodes, each network node having local data collection, primary processing and decision-making capabilities, the network nodes have self-organization and dynamic participation capabilities, the system adopts social communication protocol design, realizes data transmission among the network nodes through a wireless communication technology, supports flexible network node arrangement and communication coverage, adopts distributed data processing and storage, and is high in reliability and high in reliability. And the network nodes share data and carry out cooperative processing. Through autonomous interaction and data sharing among network nodes, the system can realize efficient resource utilization, real-time environment perception and dynamic decision adjustment, improve the resource allocation efficiency, realize real-time data acquisition and optimize dynamic environment response, and significantly improve the management efficiency and the user experience.

The invention relates to the technical field of e-commerce, in particular to a distributed data processing system based on big data, and the system comprises an internal data collection end which is used for collecting user transaction data, commodity data and behavior logs of an e-commerce platform in real time; the external data acquisition end is used for acquiring consumer credit data and social network behavior data of the third-party platform through an API (Application Program Interface); the data classification module is used for carrying out initial classification on the collected data by adopting a multi-level label system and generating classification labels with weight coefficients; the data screening module is used for dynamically filtering the classified data according to a preset threshold condition, and the threshold condition comprises data timeliness, integrity and confidence; the data error correction module is used for correcting the labels of the misclassification data set and feeding back the corrected data to the input end of the classification module; and the plurality of distributed data analysis centers generate 16 user portrait dimensions from A1 to D4, and the analysis centers execute data modeling of the corresponding dimensions in parallel.

The invention discloses a machine learning-based binary social contact mode and an implementation method, and relates to the technical field of social networks, the mode comprises an on-campus social contact domain module, an off-campus self-media domain module, a hybrid processing architecture and cross-domain information fusion module, and a social contact enhancement recommendation engine. Through the on-campus social domain module and the off-campus self-media domain module, on-campus and off-campus information can be comprehensively utilized, more comprehensive social portraits are further provided, and the fusion of the double-domain information is beneficial to more accurate understanding of social behaviors and interest preferences of users. Differentiated modeling of an intra-school strong relationship and an extra-school weak relationship is realized through a domain separation architecture, a cross-domain attention layer effectively captures feature interaction of different social scenes by adopting a domain specific parameter matrix, and semantic-level feature fusion is realized while the independence of a dual-domain topology is kept in cooperation with a feature migration mechanism of a bridging matrix. And the node characterization simultaneously comprises stable campus social features and dynamic cross-school interest features.

Systems, apparatuses, and methods for more effectively monitoring social network and social media posts to assist in identifying posted video, audio, or textual content that may be of interest or concern to a specific entity. This may comprise implementation of a process or technique to select a platform to extract content from, selecting one or more channels or sub-channels on that platform from which to extract content of interest or concern, identifying and subsequently extracting posts expected to contain content of interest or concern, post-processing the extracted content to place it into a form in which it can better be evaluated, and based on the extracted and processed content, causing one or more actions or events to occur.

The invention provides a high-order network vulnerability analysis method based on a hypergraph, relates to the technical field of systems and information, newly constructs a hypergraph network model with adjustable degree distribution as a bottom layer topology, and provides a novel vulnerability index suitable for a hypergraph network through theoretical analysis. And the relation between hypergraph degree distribution heterogeneity and vulnerability is disclosed. According to the method, high-order and randomness characteristics of interaction of an actual network system are fully considered, the constructed degree distribution adjustable hypergraph network model can be used for describing an ecological network, a social network and a traffic system, and the provided novel vulnerability analysis method can effectively evaluate the ability of the actual network system to deal with external interference. The method is wider in application range in practical application, and particularly has important technical value in the aspects of network security, survivability design, risk prediction and the like.

The invention relates to the technical field of artificial intelligence, and discloses a propagation trend deduction method and system based on multi-agent cooperation. The method comprises the following steps: constructing a social network environment with a plurality of agents, and defining portrait vectors for the agents; generating a social adjacency matrix; the internal cognitive module of each agent is responsible for processing the internal state of the agent, and the external behavior module defines parameterized actions of interaction between the agent and the social network environment and between the agent and other agents; taking an embedded vector obtained based on the internal state and the external information of the agent as the input of a decision model, and fusing a social context bias matrix into an attention mechanism of a decision encoder of the decision model to reflect a social local atmosphere; and generating an internal vertical field based on an embedded vector, and outputting an external action in combination with a social environment to drive propagation simulation and trend deduction. The method has remarkable advantages in the aspects of processing multi-modal information, depicting individual heterogeneity and realizing large-scale prospective prediction.

The invention discloses a graph-enhanced social robot detection method and device based on reinforcement learning, and the method comprises the steps: 1, extracting metadata information and text information of a user in a social network through a multi-layer perceptron and a pre-training language model, carrying out the feature fusion of the information through a multi-head self-attention mechanism, and generating user representation; 2, performing oversampling on minority class nodes in a potential feature space by using a linear interpolation method based on neighborhood perception, and constructing a balanced training sample set; 3, dynamically adjusting an edge retention threshold value based on the feature similarity and a reinforcement learning strategy, and executing an edge filtering operation to eliminate unreliable connection and optimize a graph structure; and 4, inputting the enhanced node features and the purified graph structure into a graph neural network classifier to realize accurate identification of the social robot nodes. According to the method, the problem of misjudgment caused by class imbalance can be effectively relieved, and the detection accuracy and stability are remarkably improved.

The invention discloses an emotion recognition system based on multi-modal physiological and social information, and the system achieves the modeling of a local-to-global central nervous system network through the EEG, obtains a local brain neural network through the EEG in different brain regions, obtains a global brain neural network through the spatial information between different brain regions, and achieves the recognition of the local brain neural network and the global brain neural network. Modeling from a local central nervous system to a global nervous system network is realized; on the basis of constructing a central nervous system network, physiological signals of other modes are utilized to perform information supplement on the central nervous system network, and signals such as ECG and EMG are utilized to perform modeling of a peripheral nervous system network, so that modeling from the central nervous system network to a human body global nervous system network is realized; and after the human body nervous system network is obtained, the social network is constructed by using personal social information, so that network model construction from a local nervous system network to a global nervous system network and from individuals to the society is realized.

The invention discloses a real-time topic tweet classification method and device based on small samples, and the method comprises the steps: S1, obtaining original tweet sample data, and marking the data; s2, dividing the original tweet data marked with the data into a training set and a verification set, and performing preprocessing; s3, establishing a word embedding model, inputting the preprocessed training set data into the model for training, and converting the labeled data into word vector representation; s4, establishing a classifier model, transmitting the training set data into the classifier model, and performing pre-training by taking a mask language modeling task as a target task; s5, obtaining tweet data containing each target topic specific keyword on each large social network site through a web crawler, and performing incremental training on the model; s6, inputting the verification set data into the text classifier to obtain a final topic tweet classification result; s7, repeatedly executing the incremental training process in the step S5 in real time by taking day as a unit; the problem that a traditional text classification method depends on large-scale marked data is solved.

The invention provides a symbolic social network-oriented positive information maximum propagation method, a program, equipment and a storage medium. According to the method, link prediction is carried out by using a link prediction model based on a symbol potential factor, and the missing of a network link relationship is complemented. On the basis, seed node propagation information is selected by adopting a symbol-based PageRank algorithm, so that the positive information influence is maximized. According to the method, the problems of user relation missing and relation complexity in a current network are considered, the network structure can be improved through a link prediction technology, and the seed node with the most influence is selected through a seed node selection algorithm based on the complete network structure to spread information, so that the positive information influence of the information is maximized.

A method for automatically categorizing groups of users in a social network, and using the categories to propose matches between groups or users and groups includes accessing content shared within a group in a social network application, processing the shared content with at least one machine learning model to determine labels for the content, determining categories for the group based on the labels for the content, accessing matching criteria for a user of the social network application, generating a match between the user and the group based on the matching criteria for the user and the categories for the group, supplying data indicative of the match to a client device of the user, receiving a match acceptance from the client device, and in response to receiving the match acceptance, providing the user access to the group in the social network application.

The application provides a rumor simulation analysis method and system based on a large language model agent, comprising determining initialization agent feature information and constructing a certain number of role agents according to a preset propagation network topology structure; based on the unprocessed speeches of the role agents at the current time step, a large language model is used to simulate human thinking activities to obtain the mutant rumors generated by the role agents at the current time step for language propagation; rumor propagation simulation is completed based on the propagation network topology structure and the mutant rumors at each time step; in each time step, the propagation probability between the role agents is determined according to the propagation distance and the credibility of each role agent; after multiple iterations, the role influence of each role agent is determined according to the propagation probability and the view intensity of the role agent. The application achieves the technical effect of high-precision simulation of the rumor propagation process in a complex social network.

This specification provides a communication product processing method, a user emotion processing method and a device. The method includes: determining the avatar image sequence used historically by batch users and the ordering information sequence of historically ordered communication products; quantifying the emotion value reflected by the avatar image in the avatar image sequence to obtain the avatar emotion value sequence of each user, and the avatar emotion value sequence is used to describe the change of the user's emotion value within a preset historical time period; based on the avatar image sequence, the avatar emotion value sequence and the ordering information sequence, generating the user abstract features of each user; using the user abstract features and the communication product abstract features of each user as input features, training the communication product processing model, and obtaining the preference relationship between the user and the communication product, and the communication product abstract features are the abstract features of the product information of the full amount of communication products. In this way, the visual information of the user's social network avatar can be fully mined, so that the characterization of consumer preferences under the recommendation system is more comprehensive.