102 results about "Social network" patented technology

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 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 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 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 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 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 invention discloses a proactive depolarization method for online discourse based on evolutionary multi-agent collaboration, belonging to the interdisciplinary field of social network content governance and artificial intelligence technology. The process is divided into three stages in chronological order: perception and decision-making, execution and amplification, and feedback and iteration. By introducing a multi-agent collaboration and evolutionary mechanism, and using artificial intelligence to proactively intervene in online interactive discourse, it reduces the spread of opposing opinions in online communities. This enables the governance system to possess decision-making and action capabilities similar to an organized team, achieving real-time governance of the online discourse environment, proactively guiding dialogue towards de-escalation, and reducing the spread of extreme emotions and biased viewpoints.

The invention discloses a marketing risk rehearsal method and system based on dynamic simulation, and the method comprises the steps: building a multi-agent interaction environment model based on historical market environment data and multi-source social network data, and configuring a parameterized decision model for a competitor entity, a user circle layer entity and a media opinion entity; through a Monte Carlo simulation and multi-agent reinforcement learning hybrid deduction engine, performing multiple times of dynamic game simulation on an initial action sequence of a local entity in a multi-agent interaction environment model obtained through marketing scheme data conversion, and generating simulation trajectory data; and aggregating and analyzing a plurality of pieces of simulated trajectory data to identify and quantify a competitive vulnerability mode and a word-of-mouth propagation detonation point mode, generating a rehearsal insight report, and iteratively optimizing a marketing scheme through anti-factual reasoning according to the rehearsal insight report until a strategy robustness condition is met. According to the method, prospective quantitative evaluation and active optimization of potential linkage risks of a marketing scheme in a dynamic game environment are realized.

The application discloses a social network-based insurance fraud gang identification method, system, device and medium, and relates to the technical field of computers, wherein the method comprises the following steps: constructing a social network relationship graph based on the role information and subject information in a newly added claim event and using insurance business relationships; obtaining traversal starting points for constructing a closed-loop relationship graph according to the social network relationship graph; constructing a plurality of closed-loop relationship graphs for a plurality of traversal starting points by using a first adjacent point acquisition method based on claim event relationships; and obtaining a target closed-loop relationship graph by merging the plurality of closed-loop relationship graphs by using a second adjacent point acquisition method based on claim event relationships, wherein the node information in the target closed-loop relationship graph is used to represent a target insurance fraud gang. The application is suitable for efficient investigation of fraud case identification and improves the accuracy and efficiency of fraud case identification.

This invention belongs to the field of data processing and group decision-making technology, specifically relating to a social network group decision-making method, method, and apparatus for selecting government services. The method involves acquiring initial preference information of multiple users for multiple government service options, initial trust relationships between users, and attribute information of the government service options; reconstructing a complete global trust matrix through a dynamic trust decay mechanism; dynamically identifying user behavior states into four multi-granularity states; adjusting the aggregation weights of user preferences; obtaining consensus preference values ​​for each government service option; and generating a recommendation ranking or selection result for the government service options.

The invention relates to an information interaction method and device, a storage medium and electronic equipment. The method comprises the steps that a target message and corresponding relation chain information are displayed on a target interface corresponding to a first account, the relation chain information indicates a common attention account of a second account and the first account, the second account is an account corresponding to the target message, and the second account does not belong to the attention account of the first account; under the condition that the relation chain information is triggered, an interaction area is displayed, and the interaction area is used for guiding the first account to pay attention to the second account by interacting with the common attention account. The relation chain information is displayed while the target message is displayed, interaction between the first account and the common attention account can be guided through the relation chain information to quickly know the second account, the attention efficiency of the first account to the second account is improved by taking the relation chain information as a medium, the attention path is shortened, and the user experience is improved. Therefore, the social network of the user can be quickly established and extended.

This disclosure belongs to the field of nuclear power technology, specifically relating to a social recommendation method and apparatus based on heterogeneous hypergraphs and metapaths. The social recommendation method based on heterogeneous hypergraphs and metapaths provided in this disclosure mines implicit social relationships to enrich the original social network, attempting to further improve the performance of the social recommendation system. However, these implicit social relationships are often unreliable and can introduce a lot of noise. Introducing user credibility can solve this problem to some extent. It utilizes hypergraphs to learn complex high-order relationships in the graph, including user-item interaction relationships and social relationships; user in-degree features and user out-degree features are respectively used. User in-degree features characterize the influence of implicit feedback, while user out-degree features characterize the influence of trust propagation. Thus, by mining implicit social relationships, constructing hypergraphs to learn high-order relationships, and characterizing fine-grained user feature representations, the recommendation performance of the social recommendation system is further improved.

The application provides a point of interest recommendation method and device based on heterogeneous multi-graph representation learning, equipment and medium, the method comprises the following steps: obtaining a user and a POI set containing multi-dimensional context information in a location social network, and establishing a user-POI interaction graph; using a neighborhood similarity measurement method to extract feature vector representations of the user and the POI in the interaction space; based on user check-in time information and text description information, calculating semantic information similarity distance and time pattern similarity distance, constructing a user-user perception graph, and learning feature vector representations of the user in the social space; using POI location information to construct a distance hypergraph, obtaining feature vector representations of the POI in the location space after dimension reduction clustering processing; fusing the feature vector representations in the social space and the interaction space, and the location space and the interaction space, extracting user representation vectors and POI representation vectors, and using a neural network model to predict user preferences to realize POI recommendation.

The present application provides a social user implicit relationship recommendation method and device, the method comprises the following steps: obtaining user data of multiple social platforms; according to the user data, a plurality of groups of heterogeneous information graphs based on user social information are constructed; according to the heterogeneous information graph, clustering processing is carried out respectively through a double information based multi-view attribute graph clustering method, a rule based clustering method and an attention mechanism based graph neural network recommendation clustering method, three kinds of clustering results are obtained; the three kinds of clustering results are fused to obtain a target recommendation result, which can improve the efficiency of recommendation in the user social network.

In accordance with the present invention, an interactive user display application is provided. The application displays and refreshes images that are intended to represent users or members of a social network or other web-based service. Using these images, the interactive user display application allows a user of the application to interact with other users or their profiles while viewing their images or while interacting with or consuming media.

The invention relates to the technical field of information technology and intelligent analysis, in particular to a policy diffusion network intelligent simulation analysis system which comprises a subject behavior modeling module, a propagation path analysis module, a social network embedding module and a dynamic environment adaptation module. Through combination of multi-subject behavior modeling and social network embedding, node influence is quantified, an instruction execution time sequence is dynamically recombined, and the simulation optimization problem in a complex policy environment is solved. According to the method, the precision and applicability of the policy diffusion process can be improved, collaborative optimization of the environment variable recovery speed, policy adjustment offset suppression and external disturbance control is realized, and diversified policy research requirements are met.

The application belongs to the technical field of robot control, and discloses a mobile robot dynamic obstacle avoidance planning system and method based on visual navigation, real-time motion parameters such as obstacle position, speed and acceleration are extracted based on pose semantic perception results, which are input into a social LSTM network together with obstacle semantic types and robot motion parameters, interaction behaviors such as avoidance and following among multiple obstacles are captured through a social pooling layer, the influence of robot motion on obstacle trajectory is quantified through an interaction modeling layer, and a trajectory probability distribution and a prediction confidence of 3-5 time steps are output, while a collision probability, a minimum collision time and a minimum control amount required for collision avoidance are accurately calculated; through a lightweight Transformer double-branch shared network, a pose solution and obstacle semantic segmentation share feature extraction backbones, and robot motion constraints are integrated to realize multi-task parallel output of single-frame data, and perception link delay is shortened.

The application discloses a kind of social hot content semantic deduction intelligent agent construction method, system, medium and product, comprising: obtaining historical hot search list data and carrying out clustering and processing, obtain the effective content set of multiple hot data categories;Respectively for each hot data category, construct intelligent agent and give preset person set collection;For each hot data category, according to the effective content set of the hot data category, obtain the comment reply strategy prompt word of the intelligent agent of the hot data category under each preset person set in its preset person set collection.The intelligent agent constructed by the application makes social hot content semantic deduction more in line with real social network.

The invention discloses a graph neural network anomaly detection method based on a high-order topological structure and a personalized PageRank. According to the method, high-order topological information is integrated into a personalized PageRank (PPR) algorithm, a novel high-order personalized PageRank (HiPPR) matrix is constructed, and in combination with high-order adaptive spectral convolution (HiASC), effective capture of a multi-scale node relation in a complex graph structure is achieved. For an abnormal detection scene, the noise interference is reduced by utilizing HiPPR, and the feature expression capability of abnormal nodes is enhanced through HiASC, so that the detection precision and robustness are improved. Experiments show that the method has excellent performance on the same illustration image and the different illustration image, and is particularly suitable for the fields of network security, social network anomaly detection and the like.

The invention discloses an SQL statement intelligent auditing and risk early warning method for financial services, and relates to the technical field of financial service intelligent auditing, and the method comprises the following steps: 1, defining a financial SQL core risk type and a quantitative auditing dimension; 2, a full-dimensional sensing module is constructed, multi-source data related to the SQL to be audited are collected, and the multi-source data at least comprise SQL statement data, database operation environment data, financial service rule data, supervision compliance data and historical SQL execution log data. According to the method, financial SQL auditing scene elements are mapped into four types of element individuals of SQL, risk, compliance and business, association strength among the individuals is quantified in combination with a connection weight algorithm, a virtual social network is constructed, four types of core risks of multi-risk-dimension collaborative judgment, sensitive data coverage, compliance authorization, business logic and performance overload are realized, and the risk level of the financial SQL auditing scene is improved. And the one-sidedness of a traditional static rule is avoided.

The invention discloses an organization implicit cooperation relation identification and optimization method based on multi-modal social network mining, which comprises the following steps of: multi-source cooperation data acquisition and preprocessing: acquiring mail exchange records, conference participation logs, instant messaging interaction data and project management system data in an organization, and performing anonymization processing on the data; reserving department and role labels; a node set comprises department-level nodes and employee-level nodes, and the two layers of nodes are connected through a membership relationship; according to the method, multi-source heterogeneous data (mail communication, conference logs, instant messaging records, project management data and the like) are collected and subjected to anonymization and cleaning processing, the department data dispersion barrier is broken, a complete data basis is provided for collaborative analysis, and the collaborative interaction behavior between the nodes is represented by the edge set and comprises communication frequency, task association and an information transmission path. And the analysis one-sidedness caused by the fact that the prior art only depends on single structured data is avoided.

The invention relates to the technical field of health management and digital asset interaction, and discloses an online and offline linked intelligent calorie exchange system, in which an online and offline linked intelligent calorie exchange method comprises the following steps: acquiring a multi-source sensor data stream of group sports participants, and generating a spatio-temporal data matrix; using a conditional random field model to fuse individual mode recognition results, and outputting a corrected motion mode tag sequence; calculating a calorie consumption value of each participant based on the corrected motion mode label sequence, and generating a calorie integral result; obtaining a social relation graph of the group exercise participants, and generating a social network topological graph; member data of different trust levels are integrated based on Bayesian inference, and calorie calculation parameters are dynamically adjusted. According to the invention, the credibility of the whole transaction ecology is improved, and a high-quality value voucher is provided for offline commodity exchange.

A multi-unit building communication network method, and organization of users based on verified user role in the building, property address, privileges, and restrictions to facilitate targeted user communications for more transparent information, effective communication and transparent procurements related to property maintenance, management, and other issues. The invention method provides open effective communication of verified users without needs of disclosing personal contact information. The network collects and analyzes information on property management problems to identify opportunities for financial and non- financial improvements of multi-unit residential, commercial, and industrial properties.

The invention discloses an intelligent decision-making method and system based on ecological-social dual-network coupling and functional character conduction, and relates to the technical field of intelligent ecological management, and the method comprises the steps: constructing an ecological network module and a social network module, and superposing the output of the ecological network module and the output of the social network module into an ecological-social dual-network coupling model; based on the model, ecological and social agents are created, then the agents are utilized to form a double-agent coordinator to optimize the model, and a final decision scheme is generated. According to the invention, ecological function evaluation, social benefit accounting and dynamic optimization decision making are split into independent agent modules, and cross-system collaboration is realized; the states of ecological and social indexes are balanced through a coordinator, scheme re-optimization is automatically triggered, and an evaluation-decision-calibration continuous improvement mechanism is formed; and the ecological driving module and the social driving module output an optimal scheme which simultaneously meets ecological protection requirements and social feasibility constraints through an intelligent negotiation mechanism.

The present application belongs to the technical field of data analysis, and specifically relates to a method for detecting an adversarial social robot based on a graph and structure perception. The method comprises the following steps: constructing a user relationship network by taking social platform users as nodes and the forwarding relationship between the users as edges; performing representation learning on different structures in the network by using a structure feature extractor; performing structure similarity calculation between nodes by using a structure self-attention mechanism; training user representation vectors by using a graph Transformer algorithm; and inputting the user representation vectors into a category generative adversarial network for training to obtain a discrimination result of whether the user is a social robot. The method has strong generalization and high robustness by using adversarial thinking and unsupervised training, and can be used for detecting social robots in a large social network platform, thereby providing commercial value for user management, advertisement placement, public opinion safety control and the like of the social platform.

The present application relates to the technical field of enterprise team time management, in particular, the present application relates to a virtual team management method based on enterprise information sharing, the present application sets a time stamp for key task cooperation link and collects multiple types of data on the information sharing platform, a dynamic reminding threshold model is constructed by using the method of combining transfer learning and adversarial generative network, the social network factor of the task is considered, the quantum annealing algorithm is used to optimize the model parameters, in the task reminding stage, the model is used to monitor the task processing time in real time, the reminding frequency is dynamically adjusted based on cognitive computing, the busy state of the members is obtained by using blockchain and edge computing, the reminding time is postponed by using time series prediction and multi-agent negotiation, emotion computing and virtual reality technology are also used to trigger the reminder, so as to avoid interfering with the work of the members, improve the acceptance and attention of the members to the reminder, and significantly improve the cooperation efficiency of the key task of the virtual team, and optimize the virtual team management effect.

The present invention relates to a platform and system that uses advanced artificial intelligence (AI) and comprehensive multimodal data analysis to create dynamic, personalized digital doubles that authentically replicate an individual's personality, emotions, and behaviors. The system comprises multiple modules, including user registration, data collection, social network integration, chat, data analysis, personality and emotion simulation, digital double creation and improvement, banking and telecommunication services integration, user interaction and evaluation, progress tracking, security and privacy control, and a comprehensive algorithmic framework. The hierarchical memory structure with short-, mid-, and long-term layers manages data with promotion and purge mechanisms. A Parallel AI Supervisor checks and refines responses, while ethical and moral filtering mechanisms prevent outputs that violate moral norms. The Security and Privacy Control Center ensures data integrity, with potential blockchain-based logging of major changes, and an IA Watchdog detects suspicious modifications. The invention aims to enrich virtual interactions through dynamic, learning digital duplicates that offer deeply immersive and genuinely personal digital experiences.

The invention discloses a target influence maximization method and device based on influence probability joint modeling, and the method comprises the steps: removing non-key user nodes which have small influence on a target user in a social network, and obtaining a simplified target community network; calculating the influence of the candidate seed nodes on the target user in the target community network by using a target influence probability algorithm CATIP; calculating the influence of the candidate seed nodes on the non-target users by using a weighted collective influence algorithm WCI; and comprehensively evaluating the influence of the candidate seed nodes on the target and non-target user groups by using a joint influence probability CIP algorithm, and identifying and obtaining key seed nodes. According to the method, under the condition of equal resource consumption, the influence effect on the target user can be improved, the interference on the non-target user can be reduced, and the accurate access of the information can be realized. In addition, the method is low in time complexity, and is suitable for a target influence maximization task of a large-scale network.