169 results about "Social network" patented technology

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 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 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 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 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.

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.

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.

The invention relates to the field of social network recommendation, and discloses a social recommendation method and system based on structure and semantic double view comparison and a medium, and the method comprises the steps: S1, constructing a heterogeneous information network; s2, constructing a meta-path neighborhood view: defining a target node i, extracting a meta-path neighborhood # imgabs0 # of the target node i based on a predefined meta-path, and obtaining a meta-path neighborhood view representation # imgabs1 # of the target node i by aggregating neighborhood information; s3, constructing a node attribute view; s5, cross-view comparison optimization: projecting the representations obtained in S2 and S3 to a unified feature space, and performing optimization by adopting a difficulty perception comparison loss function, so that the node representations under different views are aligned; and S6, performing recommendation based on the node representation after comparison optimization. According to the method, the multi-dimensional information of the nodes is fully fused, information omission and information redundancy are avoided, the expression ability of node representation is improved, and the social recommendation effect can be effectively improved.

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.

A social volunteering trade, payment and distributed-storage computerized deep network system, comprising the following entities: social communities; system users using the system for selling or buying products wherein at least part of the proceeds to manage and control said social trade and to match social values of said users and said communities; a system website; a search engine configured to enable said users to seek said products according to said social values; a system network configured to communicate between said entities; a reports module configured to calculate each community and each user's contribution to predefined goals and publish said calculation results; and feedback means configured to back-propagate said events' outcomes.

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.