160 results about "Social graph" patented technology

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 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 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 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 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 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.

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 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.

The present application provides a method for querying a high-connectivity shortest influence path between users, a device, and a product. The method includes: constructing an indexed linked list data structure based on an obtained social network graph; determining, based on the indexed linked list data structure, a start node, a target node, and a path connectivity in a current iteration, whether there is a shortest influence path satisfying the path connectivity in the current iteration; if yes, updating a minimum path connectivity in a previous iteration and determining the path connectivity in the current iteration based on the minimum path connectivity; or if no, determining the path connectivity in the current iteration as a maximum path connectivity in the previous iteration; and continuing the iteration based on the maximum path connectivity and the minimum path connectivity until a high-connectivity shortest influence path is determined.

The invention discloses a fine-grained privacy information propagation control strategy generation method and device, and belongs to the technical field of privacy control, and the method comprises the steps: obtaining an intimacy heterogeneous graph according to a social graph and a diffusion graph between users; updating the node embedding representation of the user node according to the graph neural network and the intimacy heterogeneous graph to obtain an updated node embedding representation; obtaining an initial user activation probability matrix according to the updated node embedding representation; obtaining a privacy disclosure score and a propagation scale according to the initial user activation probability matrix; constructing an optimization problem according to the privacy disclosure score and the propagation scale; and solving the optimization problem to obtain an optimal solution set, and taking a user in the optimal solution set as a node for next hop forwarding. According to the method, the accuracy of information diffusion prediction is higher and is closer to actual data, the static and rigid problems in the prior art are solved, a privacy control scheme with higher quality and more comprehensive selection is provided for the user, and the real optimal balance of effectiveness is realized.

A video recommendation method fusing social information comprises the steps that firstly, a user-video interaction graph and a social graph are constructed through video media platform data, different combination strategies of a diffusion model and graph convolution operation are applied in a denoising priority path and a structure priority path respectively, and collaborative perception and fusion of user embedding and structure information are achieved; secondly, aligning user embedding output by the double-track denoising path by adopting comparative learning, and enhancing robustness; secondly, performing deep coding on the user-video interaction graph and the social graph by applying a double-graph neural network, comprehensively capturing user preferences and embedding videos; and finally, for noise possibly introduced by multi-module fusion, deep denoising is executed to purify user embedding again. According to the method, guidance of structural information is introduced in the diffusion denoising process, and more robust user embedding is obtained by using a comparative learning strategy, so that a structural perception high-fidelity social denoising task is completed, and personalized video recommendation is realized on a video media platform.

The application discloses a social robot detection method and device, medium and product, and relates to the field of network security. The method comprises the following steps: taking all users in a current social network as nodes and taking the interaction between the users as edges to construct a social graph comprising a center node and a peripheral node; adopting a peripheral enhanced graph neural network to perform center node detection according to the social graph to obtain a detection result; determining a first classification loss according to the detection result of the center node and a corresponding label; determining a second classification loss according to the detection result of the peripheral node and a corresponding label; determining a cross-network domain adaptive loss based on an MK-MMD loss by using the features of the center node and the features of the peripheral node; and determining a total loss according to the first classification loss, the second classification loss and the cross-network domain adaptive loss. The application can improve the accuracy and robustness of social robot detection, thereby guaranteeing the security of the social network.

The invention discloses a social network overlapping community discovery method based on a DeepwalkGAN algorithm. The method comprises the following steps: S1, collecting social network graph structure data for preprocessing to generate a node set and an edge set; step S2, applying a DeepWalk algorithm to carry out random walk on the nodes, and generating initial node embedding; s3, constructing a generative adversarial network (GAN), optimizing node embedding through adversarial training of a generator and a discriminator, and improving the representation capability of a graph structure; step S4, dynamically adjusting the difficulty of the negative sample by adopting a Hard Negatory Mining method, and optimizing the training process of the discriminator; and S5, generating high-quality node embedding by alternately training the generator and the discriminator, and accurately discovering the overlapping communities in the social network. According to the method, the problem that a traditional community detection method cannot process the overlapping community problem can be effectively solved, experiments on multiple data sets show that the DeepwalkGAN algorithm has good performance and stability when processing the overlapping community problem of a complex social network, and an efficient social network community discovery technical scheme is provided.

This application relates to the field of relational network analysis in artificial intelligence, specifically to a user differentiation method, apparatus, device, and medium based on content and network features, comprising: acquiring a social network graph; acquiring node features of each node based on the social network graph; inputting the node features into a Markov random field model to obtain a first classification result of the node; extracting content features from the content information; inputting the content features into a trained content classification model to obtain a second classification result of the content features; and determining the user type of the node based on the first classification result and the second classification result. This application combines the different characteristics of content features and network features, utilizing more comprehensive user information to detect whether users on social network platforms are spam accounts, making it less likely for malicious users to bypass the detection.

The invention provides a credit card risk control method, device, medium and system, and the method comprises the steps: constructing and updating a multi-dimensional user portrait, the multi-dimensional user portrait comprises a risk type of a credit card holder determined on the basis of real-time transaction flow data, multi-source credit investigation data, behavior biological characteristic data and social graph data under the condition of user authorization; generating a risk user management list according to the risk type; and under the condition that the repayment event is detected, adjusting the credit line of the card holder of the credit card according to the information in the risk user management list. The problem that risk intervention of the credit card depends on manual judgment in the prior art, so that the risk control effect is difficult to meet the expectation is solved.

A social network image pool system can capture one or more image data items (e.g., image, video) in a temporary persistent post pool. The post pool enables for efficient capture of multiple image data items for publishing in a manner that allows multiple images data items to be captured while preserving the editability of the multiple items before they are published to a social network site.

The invention discloses a private domain traffic automatic operation and customer expansion method and system, relates to the technical field of intelligent marketing, analyzes multi-modal data through a deep learning algorithm, constructs a comprehensive customer portrait, and generates a personalized recommendation strategy. Meanwhile, the social graph analysis technology is used for mining the potential relation between customers, designing fission marketing activities, stimulating the customers to share and attracting more new customers to join in a private domain traffic pool, and rapid expansion of private domain traffic is achieved. According to the method, key clients and social relations can be accurately identified, the propagation effect and the conversion rate of marketing activities are improved, and thus the enterprise competitiveness and the market share are improved.

Systems and methods for social graph data analytics to determine the connectivity between nodes within a community are provided. A user may assign user connectivity values to other members of the community, or connectivity values may be automatically harvested, calculated, or assigned from third parties or based on the frequency of interactions between members of the community. Connectivity values may represent such factors as alignment, reputation, status, and / or influence within a social graph within the network community, or the degree of trust. The paths connecting a first node to a second node may be retrieved, and social graph data analytics may be used to determine a network connectivity value from all or a subset of all of the retrieved paths and / or one or more connectivity statistics value associated with the first node and / or the second node. A parallel computational framework may operate in connection with a key-value store to perform some or all of the computations related to the connectivity determinations. Network connectivity values and / or other social graph data may be outputted to third-party processes and services for use in initiating automatic transactions or making automated network-based or real-world decisions.

A song recommendation method based on graph neural networks and knowledge graphs includes: first, calculating the user embedding vector of the user in the interaction graph by combining the user-song interaction graph and the song knowledge graph; then, calculating the user embedding vector in the social graph by using the user social graph; and combining the two embedding calculations to obtain the final user embedding vector. Then, combining the song embedding vector in the song knowledge graph and the song embedding vector in the user-song interaction graph to obtain the final song embedding vector. Finally, the user's possible rating for the song is calculated using the final user embedding vector and the final song embedding vector, and a predicted ranking result of the user's recommended songs is obtained from the highest to the lowest rating. This method considers the importance of external auxiliary information in the recommendation system and introduces knowledge graphs and social network information, resulting in high accuracy and good recommendation results.

This invention discloses a method and apparatus for predicting the popularity of short videos based on learnable graph augmentation, belonging to the field of artificial intelligence technology. The method includes: collecting keyframe sets, text data sets, and behavioral data sets from short videos; constructing a graph edge to obtain a heterogeneous social graph; inputting this graph into a heterogeneous attribute graph neural network; extracting content features from the target video; performing odd-hop propagation and even-hop propagation; employing feature fusion operations and learnable sparse masks; and inputting this into a trained popularity prediction model to obtain the predicted future popularity of the target video. This invention, by integrating learnable graph augmentation and social propagation relationship modeling, can effectively utilize unlabeled data even when labeled samples are insufficient, enhancing the model's robustness and generalization ability, thereby more accurately predicting the popularity of short videos.

The application discloses a red envelope flow control method and system based on a social platform, and relates to the technical field of computers.The application dynamically divides user levels based on a social graph, so that high-value users in a star fan group can obtain priority access rights, the requests of core fans can still be stably reached in a flow peak, and the risk of loss of high-value users is reduced.Rigid concurrent control is converted into dynamic probability decision-making, the passing rate under a node overload scenario is automatically inhibited through a load-sensitive function, and global lock competition caused by atomic operation is avoided.A random decision mechanism disperses request peak pressure, maintains millisecond-level response capability, and guarantees the stability of the system under the condition of instantaneous concurrent access of ten thousand people.Meanwhile, a delay retry channel associated with a social level is enabled for limited requests, buffer opportunities are provided for core propagators, a timeout compensation strategy is combined, negative emotions of users caused by direct rejection are reduced, the integrity of a red envelope fission path is maintained, and positive circulation of social propagation is strengthened.

The application provides a kind of key user identification method and system in social network, it is related to user identification technical field, comprising: according to social network data, construct social network graph model;Through graph theory algorithm, calculate basic centrality index;Social network data is divided into multiple communities, and the local role of user node in multiple communities is identified;Calculate the local centrality index of local role;Global score and local score are generated;Global score and local score are ranked respectively, and key user node is screened out.The application solves the problem of insufficient identification of cross-community bridge nodes and local core users in traditional methods by fusing two-dimensional analysis of global centrality index and local role features, significantly improves the comprehensiveness and accuracy of key user identification;Through the double check of time sequence feature analysis and rule engine verification, the abnormal state of disguised as high-influence user is effectively identified, and the result reliability is significantly improved.

The application discloses a social recommendation method and device based on hyperbolic space contrast learning, and the method comprises the following steps: step S1, mapping the nodes of a user-item interaction graph and a social network graph into a hyperbolic space, and reconstructing the user-item interaction graph to obtain a reconstructed graph; step S2, combining the user-item interaction graph, the social network graph and the reconstructed graph, and performing hyperbolic graph learning by using a graph neural network to obtain preliminary fusion embedding of each node; step S3, based on the preliminary fusion embedding, performing social information conversion fusion for each user to obtain personalized fusion embedding; step S4, taking the hyperbolic distance between the user and the item as a prediction score to construct a hyperbolic space model, setting a contrast learning strategy of positive sample weighting and negative sample screening, and setting a contrast learning loss of social awareness and interaction awareness; and using the hyperbolic space model to score the correlation between the user and the item. The application is significantly improved in recommendation effect.

The application discloses a kind of federal social recommendation method and device based on adaptive diffusion denoising, belong to personalized recommendation technical field in electronic commerce and social platform. For the problem of serious noise interference, server load is too high and slow response in existing social federal recommendation, the application constructs interaction graph and social graph by collecting user interaction and social data, initializes user and project embedding representation, carries out adaptive diffusion denoising process in client local, wherein noise scheduling is dynamically adjusted according to user social structure characteristics, and through federal training framework, client local model updating and server global parameter aggregation are realized, finally, denoised social embedding and user interaction embedding are fused to carry out recommendation score prediction and list generation. The application effectively suppresses social noise interference, improves recommendation accuracy and robustness, reduces server load and communication overhead, and is suitable for personalized recommendation service of electronic commerce and social platform.

This disclosure relates to the field of computer technology and discloses a method, apparatus, computer device, and storage medium for identifying associated communities. The method includes: acquiring each node in a social network and the atomic relationships between them; for any target node among the nodes, determining the graph embedding information corresponding to the target node based on the atomic relationships; constructing a social graph between the target node and all other nodes according to the graph embedding information; and identifying the target associated community from the social graph based on the node relationships in the social graph. By implementing the technical solution of this disclosure, multi-path information between nodes can be considered, facilitating better capture of complex relationships and features between nodes, and fully utilizing the graph data structure information of the social graph to achieve the identification of associated communities.