57 results about "Social network" patented technology

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 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.

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 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 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 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 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 application discloses a point of interest recommendation method based on meta-path enhanced view contrast learning, and the method comprises the following steps: constructing an initial position social network and generating node initial features; determining and generating corresponding enhanced edges according to whether the geographical distance between node pairs based on different correlation relationships meets the corresponding preset threshold, so as to obtain an enhanced position social network; constructing a network mode view, a meta-path view and a meta-path enhanced view; performing meta-path level view contrast and graph level view contrast, and respectively calculating corresponding contrast losses; calculating the total loss of model training by linearly weighting the meta-path level contrast loss and the graph level contrast loss; and generating a user point of interest recommendation after optimizing model parameters. The application realizes the construction of enhanced edges by integrating time and space information to alleviate data sparsity, and improves the network representation learning quality and the point of interest recommendation accuracy by means of multi-level feature aggregation and multi-level view contrast.

The application discloses a kind of digital asset user analysis encryption method, system and readable storage medium.By using the multi-source heterogeneous data analysis for behavior data and scene data, important support is provided for the identification and selection of user information, accurate portrait, which can improve customer satisfaction.Through the analysis of the emotional characteristics of network individuals and groups, and the extraction of behavior and scene information characteristics such as text feedback, the application can locate core value customers.With customer behavior data, feature data, social interaction data, etc.as input dimensions, while analyzing the customer preference, the user's social influence and social network can also be analyzed, and then the user's social status, economic conditions, education level, etc.are studied.

This invention discloses a multi-source heterogeneous data talent evaluation system and method based on intelligent graphs, including a multi-source heterogeneous data acquisition module, a heterogeneous knowledge graph construction module, and a personalized recommendation module. After acquiring and determining the sources of heterogeneous network data, a heterogeneous graph neural network is formed. Based on knowledge distillation technology, complex social network entity links of relationships are established, and talent data after knowledge distillation is generated by creating a person profile. Combined with skill-based talent evaluation and intelligent recommendation using the knowledge graph, this invention achieves knowledge complementarity between multiple heterogeneous knowledge graphs, effectively ensures the integrity of knowledge through a distributed data storage mechanism, and provides accurate and trustworthy person profiles. It also achieves precise semantic understanding, resulting in high accuracy in talent evaluation and intelligent recommendations tailored to different individuals.

The invention belongs to the technical field of artificial intelligence, and provides a man-machine hybrid crowd intelligence knowledge driven decision-making method, device and equipment and a medium, and the method comprises the steps: obtaining machine data, and processing the machine data to obtain machine decision-making attributes; expert knowledge and public knowledge are obtained and combined to obtain human decision attributes; processing according to the machine decision attribute and the human decision attribute to obtain a man-machine mixed decision attribute weight and a human crowd intelligence decision opinion; constructing a man-machine hybrid social network, and determining a man-machine hybrid crowd intelligence knowledge value measure through the man-machine hybrid social network, the information entropy and the credibility; executing multi-attribute man-machine knowledge fusion and adjustment to obtain an optimized group knowledge value and an optimized man-machine mixed decision subject weight; and constructing a man-machine mixed decision subject weight feedback optimization model, and executing decision processing through the man-machine mixed decision subject weight feedback optimization model to obtain a decision result. The method has the beneficial effect that the scientific degree and fitness of decision making are improved.

The application provides a high-order network vulnerability analysis method based on a hypergraph, relates to the field of system and information technology, and newly constructs a hypergraph network model with adjustable degree distribution as a bottom topology, proposes a new vulnerability index suitable for the hypergraph network through theoretical analysis, and reveals the relationship between the heterogeneity of the hypergraph degree distribution and the vulnerability. The application fully considers the high-order and random characteristics of the interaction of actual network systems, the hypergraph network model with adjustable degree distribution can be used for describing ecological networks, social networks and traffic systems, the new vulnerability analysis method can effectively evaluate the ability of the actual network system to resist external interference, has a wider application range in practical application, and has important technical values in network security, invulnerability design and risk prediction and the like.

The application discloses an organization implicit cooperation relationship recognition and optimization method based on multi-modal social network mining, comprising the following steps: multi-source cooperation data acquisition and preprocessing: collecting organization internal mail correspondence records, meeting participation logs, instant messaging interaction data and project management system data, anonymizing the data, and retaining department and role labels; constructing a double-level dynamic weighted directed graph: the node set includes department-level nodes and employee-level nodes, and the two layers of nodes are connected through membership; the edge set represents the cooperation interaction behavior between nodes, including communication frequency, task association and information transmission path, the application, by collecting multi-source heterogeneous data (mail correspondence, meeting logs, instant messaging records, project management data, etc.), and through anonymization, cleaning and processing, breaks down the department data dispersion barriers, provides a complete data basis for cooperation analysis, and avoids the analysis one-sidedness caused by relying on single structured data in the prior art.

A method, implemented as computer code being executed by one or more processors, in which computer game graphics for a casual, social game are shown on a display of a computing device, where the casual, social game is downloaded as an app to a smartphone and / or tablet computer and can be accessed or played using a social network application or environment; and in which one or more of the processors are programmed such that: a notification is automatically generated if a player is stuck at a level for more than a defined time or after more than a defined number of attempts to pass that level, the notification alerting friends of the player so that they can assist him or her.

This invention relates to the field of big data analytics, specifically to a method and system for assessing the influence of social network organizations. The method includes: calculating the dissemination scope of organizational content through a dissemination power assessment module; quantifying interaction quality using a large language model and semantic similarity metrics through an interactivity assessment module; identifying content authenticity through a credibility assessment module using a multimodal model and adversarial training; predicting influence and providing action suggestions through a dynamic trend prediction module combining time-series analysis and reinforcement learning; and implementing collaborative assessment across the aforementioned modules. The core of this invention lies in multidimensional fusion, adversarial robustness, and dynamic prediction, solving the problems of existing technologies such as single-dimensionality, weak anti-deception capabilities, and passive prediction. It offers comprehensive, accurate, and instructive benefits.

The application belongs to the field of network topic propagation analysis, and particularly relates to a derivative topic propagation prediction method based on multi-topic sentiment measurement; the method comprises the following steps: obtaining topic data; performing message factor feature extraction and user factor feature extraction on the topic data; calculating message influence according to topic heat, user driving force, user activity and topic personal sentiment matching degree; dividing user networks to obtain user classification results; calculating user independent forwarding probability according to the message influence; calculating conversion probability between different types of users according to topic correlation and the user independent forwarding probability; constructing a dynamic equation according to the user independent forwarding probability and the conversion probability between different types of users; solving the dynamic equation to obtain a propagation prediction result of the derivative topic by users; and the application can effectively predict the propagation trend of derivative topics in social networks and has high practicability.

The application discloses a kind of learner portrait construction and analysis method based on time slot, first, based on course key event proposes adaptive time slot division method, the learning period between adjacent two key events is defined as a time slot.Second, construct individual time slot portrait LITP that can describe the dynamic change of learner interest and preference.Two methods for analyzing its sTP evolution are proposed, namely AsTP-DV and OsTP-EP.The former focuses on analyzing the degree of change between adjacent time slots sTP, and the latter focuses on analyzing the global evolution rule and trend of sTP.The relationship between learners is abstracted as a social network and combined with community detection algorithm to propose a time slot learning group division algorithm to divide learners into groups.The Top-N tags of representative nodes in the group are used as the basic tags of the group.Finally, the proposed method is verified using a real public dataset OULAD for multiple courses.The results show that the method is effective in constructing time slot dynamically changing learner portraits and time slot group portraits.

The invention provides a group search method and device based on maximum group search on an uncertain graph, and relates to the field of data processing, and the method comprises the steps: obtaining an initial graph corresponding to a to-be-processed social network; determining a reference value of each vertex in the initial image; deleting vertexes of which the corresponding reference values are not greater than the heuristic solution values in the initial image to obtain a vertex set corresponding to the heuristic solution values, and determining a middle sub-image based on the vertex set; all the vertexes in the middle sub-graph are traversed to execute multiple search operations, a target sub-graph with the maximum number of the vertexes is determined based on candidate sub-graphs obtained through the search operations, an edge exists between any two different vertexes in the target sub-graph, and the product of the existence probabilities of all the edges is larger than a preset probability value; and determining a target social user group in the to-be-processed social network based on the target sub-graph. According to the method, the social user group can be searched based on the uncertain associated data between the social users.

This invention belongs to the field of online public opinion dissemination, and specifically relates to a rumor information control method based on evolutionary game theory and multi-state analysis. It acquires data sources and extracts internal factors, external factors, and message influence that affect rumor dissemination from these sources. Internal factors include user activity and user sensitivity; external factors include topic popularity and friend referral power; and message influence includes the influence of rumor messages and the influence of doubtful rumors. Based on the payoff function of message influence in terms of users believing and forwarding rumor information versus believing and forwarding doubtful rumors, evolutionary game theory is used to obtain the driving forces behind user participation in the dissemination of rumor messages and the behavioral driving forces behind user participation in the dissemination of doubtful rumors. A control system is constructed, and the optimal conversion rate of the control strategy to transform the target group into a specified group per unit time is calculated. This invention accurately describes the multi-state user rumor dissemination process in social networks and provides a theoretical basis for rumor control.

A computer system receives a request from a device to join a device social network supported by an enterprise device social media cloud. The request comprises a user packet that includes a network social name, enterprise credentials of the device, and device information metadata. The computer system validates the user packet. When the validation is successful, the computer system calls a directory service to create a directory service user entry corresponding to the device in the directory service. When the creation of the directory service user entry is successful, the computer system creates a device social network user corresponding to the device in the enterprise device social media cloud.

The invention discloses a polarized group emotion prediction method and system based on retrieval and a social network, and relates to the technical field of behavior prediction, and the method comprises the steps: collecting and screening data for different topics, obtaining a group emotion value regression index, and constructing a real social network polarized emotion verification data set; based on the real social network polarized emotion verification data set, obtaining a video set related to the to-be-tested video through three correlation retrieval paths of content, context and big data; based on the related video set, obtaining corresponding group emotion features and popularity features through multi-modal correlation evaluation, and constructing a prediction vector; and through a pre-trained group polarization emotion prediction model, predicting the polarization group emotion of the to-be-tested video. Therefore, by adopting the polarized group emotion prediction method and system based on the retrieval and the social network, the breadth and the dimension of the search can be expanded through different paths, and the early and accurate prediction of the group emotion can be realized by utilizing the multi-modal social data.