251 results about "Social graph" patented technology

The invention provides a text user sentiment analysis method and system based on multiple modes and AI, and relates to the technical field of text analysis, and the method comprises the steps: obtaining and preprocessing text, image and audio information of a user, and extracting semantic, visual and acoustic feature vectors; multi-modal features are fused through a cross-modal attention mechanism; utilizing a graph neural network to construct a user emotion social graph to calculate emotion propagation intensity; and finally, obtaining an analysis result containing emotion category and intensity through an emotion classifier. The emotion state of the user can be comprehensively captured, the emotion analysis accuracy is improved, and complex emotion expression is effectively recognized.

The invention provides a key user identification method and system in a social network, and relates to the technical field of user identification, and the method comprises the steps: constructing a social network graph model according to social network data; calculating a basic centrality index through a graph theory algorithm; dividing the social network data into a plurality of communities, and identifying local roles of user nodes in the plurality of communities; calculating a local centrality index of the local role; generating a global score and a local score; and performing score ranking on the global scores and the local scores, and screening key user nodes. According to the method, through two-dimensional analysis fusing global centrality indexes and local role features, the problem that cross-community bridging nodes and local core users are insufficient in recognition in a traditional method is effectively solved, and the comprehensiveness and accuracy of key user recognition are remarkably improved; and through dual verification of time sequence feature analysis and rule engine verification, the abnormal state of the disguised high-influence user is effectively identified, and the result reliability is remarkably improved.

The invention discloses a traffic information sharing method and device for vehicle and road cloud integration, and a medium. The method comprises the following steps: constructing a vehicle social network graph; analyzing a natural language subscription request of a user, and generating a structured decision element; a data and knowledge dual-driven collaborative decision framework is adopted to carry out multi-attribute decision, and optimal working group configuration is generated; the sensing demand parameters are encrypted and issued to all nodes in the optimal working group, sensing fusion of multi-node space-time association is achieved, and multi-source sensing data are obtained; performing standardized packaging and credibility evaluation on the acquired multi-source sensing data, and filtering untrusted data to obtain trusted data; cleaning the credible data, and performing hierarchical desensitization on sensitive information in the credible data to obtain traffic information; and pushing the traffic information to the subscribing user, and constructing an intelligent closed-loop optimization system based on user feedback to optimize the traffic information sharing service. According to the invention, the dynamic cooperative sensing capability in traffic information service can be improved.

The invention provides a transaction customer social network construction and expansion method and system, and relates to the technical field of data processing, and the method comprises the steps: obtaining an original data set of a target customer; pre-processing the unstructured data in the original data set; according to the feature vector sets of the structured data and the unstructured data, respectively calculating association indexes between the target customer and other customers; according to the association indexes, constructing a multi-dimensional association matrix of the target customer and other customers; element values in the multi-dimensional incidence matrix are converted into edge weights, according to a preset edge weight threshold value, the relation that the edge weights are lower than the threshold value is eliminated, a graph structure containing nodes and edges is constructed, and a customer social network graph is generated; calculating a centrality index of each client node according to the client social network graph; and sorting according to the centrality indexes, and selecting client nodes with the top N ranks as high-value potential clients. According to the invention, the autonomy and accuracy of social network management are improved.

The invention provides a social robot behavior detection method based on structure entropy and electronic equipment, and the method comprises the steps: determining an adjacent matrix of a harmless graph corresponding to a social network graph according to the social network graph of a user; wherein the output matrix of the graph neural network taking the harmless graph as the input is the same as the output matrix of the graph neural network taking the social network graph as the input; and inputting the adjacent matrix of the harmless graph into a pre-constructed robust neural network model with unsupervised structure entropy, so that the robust neural network model with unsupervised structure entropy outputs the classification probability of the social network graph, and obtaining a social robot behavior detection result of the user. According to the method, the interference problem caused by randomness of the social network graph can be relieved, so that the accuracy of behavior detection of the social robot is effectively improved.

The invention discloses a multi-modal rumor detection system based on multi-scale enhancement, relates to the technical field of rumor detection, and is used for solving the technical problem of low detection precision of a multi-modal rumor in the prior art. The multi-modal rumor detection system comprises a multi-scale feature extraction module, a modal perception filtering module, a modal alignment module, a category alignment module and a classifier. Wherein the multi-scale feature extraction module is used for acquiring different modal features; the modal sensing filtering module is used for extracting modal sharing information and modal specific information; the modal alignment module is used for aligning the text features after image enhancement with the self-enhanced social graph features; the category alignment module is used for enabling rumors and rumors to be close in the same vector space and rumors and non-rumors to be far away in the same vector space when the multi-mode rumor detection system is trained; and the classifier is used for acquiring probability distribution of rumor detection according to the modal sharing information and the modal specific information obtained by the modal perception filtering module.

A graph neural network recommendation method based on fusion enhancement of a social graph, a knowledge graph and a time decay mechanism comprises the steps that node coding is conducted, an initial embedding layer is dedicated to coding user, project and entity nodes, attribute information of users, projects and entities is preprocessed, and the embedding technology is applied; converting the attributes into vector representation in a high-dimensional space; feature fusion: fusing features from different sources to enhance the characterization capability of the model; propagation iteration: effective propagation and iterative updating of information are realized through a multi-layer structure of the graph neural network; in each layer, the representation of the nodes is updated according to the information of the neighbor nodes, and each node can indirectly acquire and fuse wider network information through multi-round iteration; splicing and synthesizing: integrating and splicing the node representations after multiple rounds of iteration updating to form comprehensive feature representations; score prediction and optimization: predicting a matching score through an inner product embedded by a user and a project; performing parameter optimization by adopting a Bayesian personalized ranking (BPR) loss function, and covering embedding and convolution weights; in consideration of the change of user interests along with time, introducing a time decay function to improve the weight of recent rating, and calculating mean square error MSE loss through time weighted rating; and finally, combining the loss function with the BPR loss and the time weighted MSE loss to optimize the model performance.

The invention discloses a user geographic position positioning method and device, terminal equipment and a storage medium, and the method comprises the steps: obtaining target social data of a social user, and the social data at least comprises target latitude and longitude and target user text data; according to a pre-established geographic position prediction model, geographic position information of a social user corresponding to the target social data is determined, and the pre-established geographic position prediction model is used for processing sample data, determining a multi-dimensional user relation graph corresponding to the sample data, and predicting the geographic position of the social user according to the multi-dimensional user relation graph. A multi-dimensional user relation graph is adopted to train a multi-scale feature fusion model based on a graph attention network, and a social network graph and a position association graph are constructed through the social relation of users and hidden position correlation; processing through the proposed multi-scale feature fusion model to obtain the overall representation of the user; the position of the user is deduced through the user positioning classifier, and the accuracy and efficiency of user position prediction are improved.

The invention relates to the technical field of personnel management, and provides an emergency scheduling scheme generation method and device, equipment and a medium. The method comprises the following steps: constructing a social network graph based on all dispatchers and all information flow relationships; calculating an initial manpower scheduling cost value and an initial time cost value in the scheduling task execution process of each commander based on the social network graph; constructing a scheduling cost optimization model according to the maximum number of the scheduling personnel, the initial manpower scheduling cost values and the initial time cost values of all the commanders, the social network graph and all the information transmission time, and performing minimization solution on the scheduling cost optimization model to obtain a first emergency scheduling scheme; and constructing a response time optimization model according to the maximum scheduling personnel number, the social network graph and all information transmission time, and carrying out minimization solution on the response time optimization model to obtain a second emergency scheduling scheme. According to the method, the reasonability of the emergency scheduling scheme can be improved.

The invention provides an early information propagation prediction method and system based on a diffusion model. The method comprises the following steps: acquiring a statistical user social graph, a user currently participating in propagation of information to be predicted and a time point when the user participates in propagation; determining a user code of a user participating in propagation of the to-be-predicted information based on the user social graph, and determining a user sequence group based on the user code and a time point at which the user participates in propagation; constructing a historical propagation sequence based on the user sequence group sequence; inputting the historical propagation sequence into a cascade representation learning module to obtain a first cascade representation sequence group; the first cascade representation sequence group is input into a pre-trained cascade generation model, the cascade generation model outputs a second cascade representation sequence group, and the cascade generation model is of a diffusion model structure; and inputting the second cascade representation sequence group into a pre-trained popularity prediction module, and outputting a predicted popularity value.

The invention relates to the technical field of computer technologies, and discloses an old people social network analysis and recommendation system based on a graph neural network, which comprises a data acquisition module, a graph construction module, a graph neural network model module, a social network analysis module, a personalized recommendation module and a user interface module, the data acquisition module is used for acquiring and cleaning social related data of old people; and the graph construction module is used for constructing static and dynamic social network graphs. According to the old people social network analysis and recommendation system based on the graph neural network, a dynamic social network graph is constructed through the graph construction module, sliding time window updating is adopted, dynamic changes of the social relation of old people along with time can be captured, meanwhile, edge weight calculation is combined with the interaction frequency and the interaction depth, and the recommendation efficiency is improved. The interaction depth fuses semantic similarity and emotion scores, the interaction depth is accurately quantified, and a social network analysis module introduces a time decay factor prediction relation.

The invention provides a fraud detection method based on graph data and related equipment, and relates to the technical field of artificial intelligence, and the method comprises the steps: obtaining user data; constructing a user social graph based on the user data; and performing graph calculation based on the user social graph to obtain a detection result. By constructing the user social graph, the complex relationship among the user groups can be grasped on the whole, and fraud detection can be carried out more comprehensively. Different from the prior art which only pays attention to behavior data of users, the method has the advantages that a relation network between user accounts and equipment and basic information association among different accounts are included into core features of fraud detection. The defects in the aspect in the prior art are effectively overcome, and the fraud detection accuracy, efficiency and recall rate are improved.

The invention relates to the technical field of information propagation prediction, and discloses an information microcosmic diffusion prediction model fusing cascade frequency domain characteristics, which comprises the following steps: firstly, constructing a social graph and an information diffusion hypergraph, and capturing the influence of social homogeneity and social influence factors on information propagation by using the powerful capability of a graph convolutional neural network; then, Fourier transform is ingeniously applied, time domain information of the cascade is mapped to a frequency domain, and the deep matching degree between the user and the cascade is learned by combining high-frequency and low-frequency components. Finally, the model performance is further optimized through an attention fusion layer, and the purpose is to generate more expressive user representation, so that the problems of feature redundancy and information loss are effectively avoided. Experiments on four public datasets, such as Douban and Meteracker, show that compared with the existing most advanced baseline model, the method provided by the invention has the advantages that the two key evaluation indexes, namely Hits (at) K and Map (at) K, are obviously improved, and the highest values are up to 10.94% and 23.03%.

The invention provides a pedestrian trajectory prediction method fusing social interactive perception, and belongs to the technical field of intelligent traffic, and the method comprises the following steps: S1, coding original pedestrian trajectory data into a high-dimensional feature space through a special embedded layer; s2, modeling a time relationship in the trajectory data; s3, constructing a pedestrian social graph; s4, a decoder of the Transformer integrates features subjected to time and space processing, and the features are combined with historical motion information and current social interaction, so that a comprehensive feature representation is generated; according to the invention, through cooperative combination of time and space processing, the pedestrian trajectory can be predicted more accurately, especially in a complex crowd-intensive environment; and meanwhile, the features of Transform and GCN architectures can be effectively integrated, so that deep context understanding of pedestrian dynamics and association of pedestrian tracks in a hidden state can be captured, and personal motion modes and collective social influence are considered at the same time.

The invention discloses an illegal transaction group identification method and device, equipment, a storage medium and a product, relates to the technical field of data processing, and discloses an illegal transaction group identification method comprising the following steps: constructing a social network graph according to recharging information provided by a telephone charge recharging platform; performing loop iteration on the social network graph by using a community discovery algorithm, and dividing the main payment number in the social network graph into a plurality of communities; and screening out a suspicious illegal transaction group from the plurality of communities according to the distance between the associated main payment number in the user complaint order received by the telephone charge recharging platform and the main payment number in each community. Accurate identification, prevention and control of illegal transactions and illegal accounts are realized.

The invention relates to the field of influence measurement, and discloses a social media-based content influence measurement method, which comprises the following steps of: constructing a social network graph according to collected user social relation data; each user is a node, interaction between the users is an edge, and features in the social network are analyzed; modeling a propagation path of the information by adopting a propagation model, and weighting the weight of the information propagation according to the social relationship between the users; establishing a weight model, endowing each edge in the propagation path with different weights according to the strength of different social relationships, and strengthening the influence of the core social group on content propagation; evaluating the propagation breadth and propagation speed of the content in the social network, analyzing the participation behavior of the user, and evaluating the propagation effects of the content in different social circles; and training the data by using a machine learning algorithm, and optimizing the propagation effect prediction model. The method has the advantage of accurately evaluating the actual propagation effect and influence of the content.

The invention relates to the technical field of social network security, in particular to a social robot detection method and system based on residual propagation and label smoothing, a directed social graph G = (V, E) is constructed, a node u belongs to V and represents a user, and a directed edge (u, v) belongs to E and represents a social relation between users; training a topology-independent base predictor f based on the attribute feature xu and the label yu of the labeled node, and outputting an initial prediction zu of the labeled node; based on the prediction residual yu-zu of the annotated node, correcting the prediction result of the unannotated node through a directional residual propagation mechanism; and performing label smoothing processing on the corrected predicted value, and outputting a final classification result. According to the method, the detection precision equivalent to that of the most advanced GNN model is realized through a lightweight architecture (parameter quantity is reduced by 1-2 orders of magnitude), and an efficient and extensible solution is provided for large-scale social network deployment.

The invention discloses a rich neighborhood social contact recommendation method based on comparative learning, which comprises the following steps: acquiring interaction data of a user and an item from a selected data set, and dividing the data set into a training set and a test set; constructing a user-project interaction graph and a user-user social graph; updating the user, the project and the social embedding vector by using lightweight graph convolution; users and structural neighbors of the users are regarded as right facing for comparative learning; performing cross-view comparative learning between the user-item interaction graph and the user-user social graph, and jointly optimizing a loss function to train a model; and performing recommendation prediction on the test set, and evaluating the performance of the model. According to the method, the social information of the user is fused into a recommendation system, in consideration that direct fusion of heterogeneous information may cause damage to representation learning, cross-view contrast learning is used to maximize the consistency of the same vector in different views, and meanwhile, the relationship between the user and a structural neighbor thereof in the social graph is deeply mined, so that the social information is further enriched, and the recommendation efficiency is improved. And the recommendation performance is improved.

The invention is suitable for the field of investment risk assessment, and provides an investment project risk assessment method and device and a storage medium, and the method comprises the steps: constructing a social network graph and a relationship strength scoring model, analyzing node centrality and community stability in real time, simulating an interference situation, and recognizing a key cooperation relationship and a potential risk. Therefore, comprehensive risk management support is provided for financial health of the startup company. According to the method, through the social network graph and the dynamic relationship strength scoring model, the key cooperation relationship of the startup company is effectively identified and analyzed, the risk is monitored in real time, and deep scene analysis is provided, so that effective investment risk assessment and management are realized under the conditions of short-term financial data fluctuation and financial history scarcity.

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

The invention relates to an interaction method and device based on virtual reality, equipment and a medium. According to the method, firstly, state data of a user in a virtual reality environment is collected and preprocessed to generate a standardized data stream, and then the distance, orientation similarity and interaction frequency between the user and a virtual avatar are calculated based on the data so as to construct a situational social graph representing social relation strength. Meanwhile, an attention model representing attention weight is constructed by analyzing a fixation point and a head direction of a virtual avatar of the user, and then information priority is calculated through weighted summation of social relation strength and the attention weight by utilizing a situational social graph and the attention model; according to the method and the device, the priority of the user is obtained, the audio, visual and text information is dynamically filtered or enhanced according to the priority to generate the optimized information flow, and finally the information flow is presented in the virtual reality environment, so that the cognitive load of the user in a dense social scene is effectively reduced, and the social interaction efficiency and immersion are improved.

The invention provides an influence maximization method based on graph attention weight fusion. The method comprises the following steps: S1, converting network nodes of an input social network graph G into an adjacent matrix adj of the graph; s2, performing feature compression extraction on the adjacent matrix adj of the incoming graph by using an encoder, the feature extraction comprising compressing and mapping the adjacent matrix of network node data to a low-dimensional space to obtain a potential space representation z; s3, inputting the potential space representation z into a graph attention network, and performing graph attention weight fusion updating learning to obtain a feature z '; s4, inputting the feature z'and the adjacent matrix adj of the graph into a decoder, and updating the probability value of the seed node through the decoder; and through multiple rounds of updating iteration, selecting a node which ranks the top Top in the probability value as a finally selected seed node. According to the method, the node with the most influence can be effectively found, and the technical problem of influence maximization (IM) is solved to the maximum extent.

The invention relates to the technical field of information processing, and particularly discloses a social graph construction and abnormal behavior early warning method for old people, and the method comprises the steps: S1, continuously collecting the position data of the old people; obtaining a pre-constructed place semantic knowledge graph; for every two old people, whether a position co-occurrence relation exists between the two old people is judged, and when the position co-occurrence relation exists, the position co-occurrence intensity is calculated; calculating a social interaction strength score between the old people according to the position co-occurrence strength and the place social function weight; according to the social interaction strength score and the place function information, constructing an old people community social graph; according to the social graph construction method for the old people, the social interaction information can be effectively mined from the position data, and then the social graph of the old people community is constructed, so that the real social interaction between the old people can be more accurately identified and quantified, and data support is provided for the old people institution to optimize the nursing service and activity arrangement.

The invention relates to a node representation learning method fusing diffusion hypergraph modeling and a graph convolutional network, and belongs to the technical field of modeling and prediction of a complex diffusion process. The method comprises the following steps: extracting a static node structure representation of a social graph, and obtaining a social context feature representation of the static node structure representation; constructing a diffusion hypergraph according to the propagation diffusion sequence, and dividing the diffusion hypergraph into a plurality of sub diffusion hypergraphs; performing feature extraction and fusion on each sub-diffusion hypergraph to obtain user information features; obtaining time sequence feature information embedding based on the improved LSTM network and the user information features; and performing cross-dimension splicing on the social context feature representation and the time sequence feature information to obtain a splicing fusion feature, obtaining a propagation probability based on the splicing fusion feature, and completing model performance evaluation based on the propagation probability. The objective of the invention is to solve the technical problems that time sequence information is not deeply learned and global information is not integrated in the prior art.

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

This invention discloses a social denoising recommendation method based on edge confidence invariant learning, belonging to the recommender system field within machine learning. The invention constructs an edge confidence estimation mechanism that integrates preference similarity and structural priors, and based on this, performs soft pruning and confidence-weighted propagation on the social graph to suppress noise diffusion from low-quality social edges at the source. Addressing social structural perturbations and noise distribution changes, the invention introduces a multi-environment generation mechanism, constructing multiple differentiated social environments under the guidance of edge confidence priors, and learning stable and effective preference signals through cross-environment invariance constraints, thereby reducing the model's dependence on environment-specific spurious correlations. Simultaneously, the invention designs a cross-domain gating fusion mechanism to selectively forget and enhance social representations, and combines cross-domain alignment constraints to alleviate semantic shifts between the social and recommendation domains, improving the controllability of social information injection.

The invention provides an information propagation prediction model training method, prediction method and system based on training during testing. The method comprises the following steps: constructing a directed social graph and a sequence diffusion hypergraph according to a user set and a cascade set, and constructing a training set and a testing set; the information propagation prediction model comprises a macroscopic prediction branch, a microscopic prediction branch and an auxiliary self-supervised learning branch introducing MAE, and the three branches share a feature extractor; during training, performing joint training on the model based on a training set, and introducing consistency loss to ensure that the optimization directions of the auxiliary task and the main task on the parameters of the feature extractor are consistent; during testing, training during testing is carried out by taking minimization of auxiliary self-supervised learning task loss as a target aiming at a specific test sample, a feature extractor is adjusted to adapt to sample distribution, and an information propagation prediction model is acquired in combination with a task-specific prediction head. According to the method, multi-scale information propagation prediction can be realized, the problem of distribution offset in an information propagation prediction task is solved, and the generalization performance of the model in different test environments is improved.

There is provided a computer implemented method of dynamic generation of audio content of a panel including questions asked by a moderator and responses by responders, comprising: accessing user interest(s) of a target user, accessing a social network graph that includes the target user, selecting questions correlated with the user interest(s) of the target user, selecting responses to questions by responders, wherein the responders are linked to the target user in the social network graph, wherein the responders are associated with user interests correlated with the user interest(s) of the target user, dynamically assembling audio content(s) from voice records and / or from audio recordings generating by converting text to voice, each audio content including a sub-set of the selected questions and a sub-set of the responses, and providing dynamically assembled audio content(s) for selection thereof for playing on a speaker.

The invention belongs to the field of data mining, and particularly relates to a social friend recommendation method based on spatio-temporal evolution. Comprising the following steps: acquiring social network diagrams at a plurality of historical moments and extracting an adjacent matrix and a user feature matrix; inputting the adjacency matrix and the user feature matrix into a graph convolutional neural network for processing to obtain social space features of a plurality of snapshots; processing the social space features of the plurality of snapshots by using a multi-dimensional cloud model to obtain three digital feature sequences; wherein the three digital feature sequences are respectively expressed as Ex, En and He. Processing the three digital feature sequences by adopting a grey prediction model to obtain a first development matrix and a second development matrix; predicting the three digital feature sequences according to the first development matrix and the second development matrix to obtain three predicted digital features; inputting the three predicted digital features into a decoder to obtain a predicted adjacency matrix, namely a friend recommendation result; the method can predict the development trend of the user relationship.

The present invention relates to the field of information processing technology, and specifically discloses a method for constructing a social graph and warning of abnormal behavior for the elderly, wherein the method for constructing a social graph for the elderly comprises the following steps: S1, continuously collecting location data of the elderly; obtaining a pre-constructed place semantic knowledge graph; for each two elderly people, determining whether there is a location co-occurrence relationship between the two, and calculating the location co-occurrence strength when there is a location co-occurrence relationship; calculating the social interaction intensity score between the elderly based on the location co-occurrence strength and the social function weight of the place; constructing a social graph of the elderly community based on the social interaction intensity score and the place function information; the method for constructing a social graph of the elderly community can effectively mine social interaction information from location data, and then construct a social graph of the elderly community, so as to more accurately identify and quantify the real social interactions between the elderly, and provide data support for optimizing care services and activity arrangements for nursing homes.