80 results about "Social web" patented technology

The application provides a kind of multi-modal graph denoising false news detection method and system based on Transformer, belongs to network data processing technical field, including: obtaining the metadata of source news, including news text, image, video and the historical tweet of forwarding user;Using the detection model trained in advance to process the metadata of the source news obtained, to obtain the result whether the source news is false news.The application can more accurately represent news by multi-modal features;Using the time sequence and hierarchical structure in news dissemination process, effectively modeling the real user dissemination behavior pattern, improves the adaptability of detection result to real scene;Reduce the deviation between graph structure and actual social network, more accurately reflect the information influence relationship between users;The representation method of user and news node is various, by modeling the user interest preference or historical behavior, it is easier to cope with the social network environment with highly personalized user behavior.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for presenting user profiles on a user interface. In some implementations, a server receives a request to view profiles for each of one or more other users from a client device associated with a requesting user. The server applies matching criteria to a profile of the requesting user and profiles for respective users to identify profiles for a sequence of profiles. The server enables sequential display of information representative of the identified profiles on a user interface of the client device, including enabling display of first information representative of a first profile of the identified profiles, determining a user review attribute for the first profile, dynamically adjusting the sequence of profiles based on the user review attribute, and enabling display of second information representative of a second profile of the identified profiles in the adjusted sequence of profiles.

The application provides a political orientation network public opinion manipulator detection model training method, a detection method and a device, comprising: constructing a training data set, each sample of which contains tweets collected from a social network platform; using a latent Dirichlet distribution to model the theme of the training data set, screening out political direction data to construct a political data set; constructing an initial detection model, which includes a feature selection module and a classification module; inputting the political data set, extracting features by the feature selection module, classifying by the classification module, and outputting a predicted classification result; training the initial detection model using the political data set, constructing a loss optimization model of the predicted classification result and the true classification; evaluating and selecting the best classification algorithm in terms of classification performance as the final classification algorithm of the classification model to obtain a detection model. The training method of the application guarantees the class balance ratio of the training set, has high training efficiency, and the detection model obtained by training has high precision and high performance.

The application provides a trust evaluation method of a multi-layer relationship-aware graph neural network, belongs to the technical field of social network data processing and graph learning, and aims to solve the problems of mixed multi-source trust information, insufficient modeling of nonlinear interaction between nodes, and insufficient utilization of multi-layer graph convolution features of existing trust evaluation methods in sparse social networks. The method constructs a directed trust graph, uses a relationship-aware encoder to model the node's own features and neighborhood features, combines an adaptive propagation module to represent the directional and conditional dependence relationship between nodes, and obtains the final node pair representation through cross-layer attention fusion to realize trust evaluation between target node pairs. The method can be applied to social platform risk control, recommendation system credibility analysis, transaction network reputation evaluation and other scenes.

The application provides a network user intention recognition method, vectorization representation of text content and emotion is realized through two kinds of Bert pre-training models, efficient extraction of global and local features of the text is realized by adopting a Text-Inception module and a Bi-LSTM network, further, dynamic fusion of the local features and the global features of the text is realized based on an improved SENet network, and dynamic combination of the text and emotional features is realized through a residual cross-attention mechanism, understanding and recognition of the intention of the social network user are realized. Finally, the application can accurately recognize the intention of the user, and further provide support for a wide range of application scenarios, such as malicious user analysis of social media.

An electronic personalization device and method for personalization of a vehicle that has a cockpit domain controller configured to identify a user of the vehicle, connect to an item of user equipment and collect social network data of the user. The method carried out by the device includes: a step for the cockpit domain controller to acquire social network data associated with a respective user; a step for building a model of a user profile, known as a user model, from the social network data associated with the user; and a step for transmitting the user model to at least one item of on-board electronic equipment in the vehicle, so that the on-board electronic equipment can perform an action dependent on the user model.

A method for celebrity-based advertising includes storing, in a server, a likeness of subject and a corresponding booking that includes parameters outlining availability of the subject for digital advertising. The method further includes receiving or creating, by the server, a pitch that falls within the parameters of the booking, the pitch corresponding to the subject. The method further includes receiving, by the server and from a first user device, a first image of the likeness of the subject or another likeness of the subject. The method further includes determining, by the server, that the image matches the likeness of the subject. The method further includes transmitting, by the server, the pitch to the first user device to be output by the first user device.

This invention relates to the field of artificial intelligence technology and discloses a multi-agent social network simulation method and system based on a cognitive reasoning chain. The method includes: initializing a multi-agent system comprising a social environment engine, a user profile engine, and a cognitive reasoning engine; executing a multi-agent social network simulation loop with a preset number of iterations; in each iteration, the social environment engine pushes social information to the agents as external stimuli and activates the agents to make independent decisions; updating the cognitive states of each dimension in the cognitive reasoning chain and generating corresponding social behaviors through reasoning using a large-scale language model; recording the social behaviors and their corresponding cognitive state trajectories; periodically analyzing historical records to optimize the influence coefficients between the cognitive dimensions of the cognitive reasoning chain; and adjusting the reasoning strategy of the preset large-scale language model. This makes the simulation of the agent's cognitive process a transparent and traceable evolutionary process, achieving a complete and understandable simulation of the "observation-cognition-behavior" cycle.

The application relates to the technical field of artificial intelligence, and provides a video recommendation method, device, equipment and computer program product. The method determines a photographing user through obtained social network data, extracts communication data and behavior data from a personal terminal of the photographing user, constructs a family group of the photographing user based on the communication data, extracts behavior data of each family member from a public terminal of the family group, determines preference information of each family member based on the extracted behavior data, obtains position signaling data of each family member, determines a target member currently using the public terminal according to the position signaling data, determines a target video according to the preference information of the target member, and recommends the target video in the public terminal. The video recommendation method provided in the application can identify the preference information of a hidden user, identify a watching user through position signaling data, recommend a video meeting the preference of the watching user to the watching user, and realize accurate personalized recommendation.

With the widespread ubiquitous connectivity to smartphones and the Internet and social networks, software applications and cameras have become commonplace for individuals in our daily lives. We take pictures and videos of all kinds of events, goods and situations on our smartphones, easily upload them to cloud services, and with friends, family and others who subscribe or follow our shared content. However, through the use of all this technology we do not do a very job of recording the entirety of data available since other sensors are not utilized in the capturing of a significant, fleeting event, such as a UFO sighting, suspected criminal conduct, etc. Embodiments allow users to employ numerous sensors and other data to capture information about an event.

The application relates to a social network text target topic detection method based on sparse subspace clustering, and belongs to the technical field of computer data processing of natural language processing and social network data mining.The method writes text identification into target platform text content and interaction event records, carries out word segmentation, denoising and vectorization processing, reduces short text noise and event mismatch interference on subsequent analysis, constructs a social relationship graph, calculates edge confidence weight to form a graph regular constraint parameter, reduces the weight of a low-confidence screen edge in the constraint, suppresses relationship noise caused by organized manipulation from the source, solves sparse representation coefficients, constructs a similarity matrix, performs spectral clustering, enhances the separability of samples with similar semantics but different propagation modes, calculates semantic cohesion and propagation deviation, determines a target topic cluster by using a double threshold, extracts a key text set and a propagation evidence set, forms a reviewable target topic detection result, and improves target topic detection rate and reduces false positives.

We provide the system. [Solution] In order to suggest the most suitable items based on the user's preferences, characteristics, budget, and lifestyle, a means of collecting purchase history, browsing history, and social networking data to generate user characteristics, A means for selecting and proposing the most suitable items for the user using artificial intelligence processing based on the user's characteristics, A means of virtually trying out or placing selected items in a user's characteristics or environment using augmented reality technology, A means of comparing market values, obtaining the most economical purchasing options and available discount information, and presenting them to the user. A means of interpreting user inquiries using natural language processing and providing relevant information, A means of providing a more intuitive purchasing experience through virtual try-ons of items that users are interested in, A system that includes this.

We provide the system. [Solution] A means to automatically detect the popularity of social networking platforms in each region, A means of translating product information into multiple languages, A means of automatically posting translated product information to detected social networking platforms, A means for automatically detecting product-related social media content and generating responses, A means of measuring the performance of marketing activities and generating reports, A system that includes this.

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.

This invention discloses a graph anonymization method for privacy protection in weighted social networks. It combines member fuzzy clustering and simulated annealing to create an optimal cluster of node degree sequences, resulting in a new degree sequence. Edge addition and deletion operations are performed on the original graph to reconstruct the graph and satisfy the new degree sequence. For nodes with the same degree, to resist background knowledge attacks, the edge weights of some nodes are generalized so that the weight values ​​of nodes with the same degree satisfy a diversity model. Experimental results show that, compared with other methods, the combination of member fuzzy clustering and simulated annealing provided by this invention can not only resist background knowledge attacks on node degree and weighted edges in weighted social networks, but also effectively reduce the amount of data loss after anonymization and improve the actual utility of the data.

Provide a system. 【Solution means】 An information receiving means for confirming the death of a user, A processing means for analyzing the death confirmation information, A communication means for sending a death notice to a financial institution, A control means for automatically executing the cancellation of a subscription service, A changing means for setting a social network account to a mourning mode, A sending means for sending a will message, An information management means for communicating with a virtual environment to protect user information, A system including the above.

The application discloses a social network key node identification method based on double population interaction, which comprises the following steps: 1, double population construction, based on the original network space and the reduced search space, the main population and the auxiliary population are generated respectively to complete initialization; 2, double population evolution, the main population and the auxiliary population obtained in step 1 are respectively subjected to genetic operation, offspring is generated through crossover and mutation, and a new population is formed according to the corresponding population updating method; 3, double population interaction, when the double population evolution reaches a certain iteration number and meets the interaction condition, the influence strategy and the expansion strategy are alternately executed to realize the information interaction of the main population and the auxiliary population; 4, individual selection, the individuals in the main population and the auxiliary population are sorted based on the target function value, and the optimal individual is selected as the key node combination output. The application can quickly and effectively identify the key node combination in a large-scale social network, and provide a high-quality selection scheme for decision makers within a reasonable time.

This invention relates to a method and apparatus for social network anonymization based on multi-agent reinforcement learning. The method includes: S1: Constructing a reinforcement learning environment and initial state; acquiring the social network graph data to be processed, modeling the anonymization task as a Markov decision process, and establishing the state representation and reward feedback mechanism of the environment; S2: Executing dual-agent collaborative decision-making; constructing a structural perturbation agent and a feature confusion agent, with the structural perturbation agent deciding on edge-connecting actions at nodes, and subsequently, the feature confusion agent deciding on feature modification actions at nodes based on structural perturbation information; S3: Constructing a joint parameter optimization objective based on a proximal policy optimization algorithm; evaluating the value of the global state using a centralized value network, and calculating the advantage function of the dual agents in conjunction with immediate rewards; constructing the policy loss and value loss of the dual agents; S4: Executing an iterative training process to optimize network parameters; collecting the interaction trajectories between the dual agents and the environment, and synchronously updating the parameters of the policy network and value network according to the joint parameter optimization objective; S5: Obtaining the social network anonymization results and evaluating the model utility; using the trained policy network to perform deterministic perturbations on the input test set graph data, outputting the anonymized graph data results, and calculating the anonymization success rate.

The application discloses a kind of information propagation heat prediction method based on community inside and outside dynamic evolution, method includes obtaining cascade diagram, constructs community graph structure, constructs community graph structure and information propagation heat prediction.The application belongs to social network data processing field, specifically refers to a kind of information propagation heat prediction method based on community inside and outside dynamic evolution, the scheme is based on the newly added participating node constructs instruction information and forms mask adjacent relationship, carries out community graph structure information propagation update on mask adjacent relationship and is aggregated to obtain community representation;Community graph structure is built, and the edge feature is extracted to the community interrelation and is encoded, and the community representation is updated on community graph structure to obtain intercommunity representation;Multi-head attention aggregation is generated to segmented cascade representation for intercommunity representation, combined with segmented growth statistics to form time series input and carry out sequence coding, output information propagation heat prediction result in future time window.

This invention provides a continuous subgraph matching method for social networks based on dynamic pruning, belonging to the field of social network behavior detection. The invention proposes a dynamic pruning algorithm that establishes local equivalence theory by calculating the backward neighbors of query points and candidate points, defines subtree-level equivalence criteria in dynamic graph environments, and is applicable to highly isomorphic behavior patterns such as zombie account groups and terminal nodes of propagation chains. The dynamic pruning algorithm skips isomorphic subtree structures to achieve the pruning effect. A query graph decomposition and sorting mechanism is proposed to remove the binding constraint of the matching order on the update edges and candidate sets, actively identifying and prioritizing highly redundant candidate set vertices, further reducing the search space. Ultimately, while ensuring result completeness, it achieves a 16.9-fold performance improvement over RapidFlow and a 100% completion rate for complex queries, providing sub-second decision support for abnormal behavior detection in social networks and promoting the application of dynamic graph analysis in resource-constrained environments such as edge computing.

The subject matter of this specification can be embodied in, among other things, a method that includes receiving two or more data sets each representing speech of a corresponding individual attending an internet-based social networking video conference session, decoding the received data sets to produce corresponding text for each individual attending the internet-based social networking video conference, and detecting characteristics of the session from a coalesced transcript produced from the decoded text of the attending individuals for providing context to the internet-based social networking video conference session.

The application discloses a social network event key element identification method based on fusion graph calculation and belongs to the technical field of internet data processing. First, social network event information texts of a target topic are acquired, five types of event elements of time, place, doer, behavior and receiver are extracted, and a social network event element knowledge graph is constructed with the five types of event elements as nodes and logical relations among the elements as edges. Second, the degree centrality, betweenness centrality and PageRank value of each node in the graph are calculated, nodes ranked before a proportion threshold value are selected for each type of index, and an initial key node set is formed by merging. Finally, based on the graph and the initial set, SLPA community division is iteratively executed, a key community subset is screened, overlapping nodes are detected and merged, and the final key node set is output as event key elements until convergence. The application can effectively identify explicit and potential key elements and solves the problem of one-sided evaluation and high omission rate of existing single index methods.

The invention relates to the technical field of social network analysis, and discloses a method for identifying important nodes in social network data, a terminal and a storage medium. The method comprises the following steps: acquiring social network data and calculating semantic similarity between posters; the method comprises the following steps: mapping users, stickers and topics into nodes, mapping attention, interactive behaviors and similarity relationships into edges, and constructing a multi-relationship heterogeneous graph; secondly, defining a meta path to capture high-order topological dependence, performing multi-hop neighborhood aggregation on nodes, and fusing different path characteristics by using a self-attention mechanism to obtain high-order structure characteristics; meanwhile, a unified semantic space is constructed based on a pre-training language model, and semantic embedding representation of nodes is extracted. And finally, the high-order structure features and the semantic features are deeply fused to generate joint representation, and scores are calculated based on the joint representation so as to accurately identify important nodes. According to the method, the hierarchy and potential influence of the nodes in the network can be comprehensively described, so that the accuracy and robustness of important node identification are improved.

This application provides a user mining method and apparatus, electronic device, program product, and storage medium. The method includes: acquiring user data through public API interfaces of multiple social networking platforms and storing the acquired user data in an intelligent computing performance-enhanced file storage service deployed on a cloud computing platform; using a data processing cluster on the cloud computing platform to read the user data from the intelligent computing performance-enhanced file storage service, reconstructing features based on the user data, and storing the reconstructed feature data back to the intelligent computing performance-enhanced file storage service; constructing and training a reinforcement learning model on the cloud computing platform based on the reconstructed feature data; storing historical experience data of the agent in the intelligent computing performance-enhanced file storage service during the training process; deploying the trained reinforcement learning model to a production environment and using the reinforcement learning model to perform user mining based on real-time acquired user data.

The application provides a multi-modal sarcasm detection method based on hierarchical attention fusion and a graph attention network, belongs to the technical field of social network public opinion analysis and natural language processing, S1: feature extraction and preprocessing, acquires social media image-text data, S2: graph network interaction modeling, constructs a cross-modal heterogeneous graph, S3: sentiment conflict quantification, generates a sentiment conflict score, S4: hierarchical fusion prediction, realizes end-to-end discrimination of sarcasm attributes, and the hierarchical attention fusion mechanism is designed, multi-modal information is fused layer by layer at the feature layer, the relationship layer and the decision layer, different levels of semantic information can be fully utilized, high costs caused by full connection calculation of a traditional complex interaction model can be effectively avoided, and the expansion capability of the model for processing long texts and high-resolution images is improved.

The application relates to a social network repeated event detection method based on repeated subgraph pattern mining. The method comprises the following steps: obtaining a social network snapshot graph of a current timestamp, mapping an m-edge subgraph related to a newly inserted edge e in the social network snapshot graph into a corresponding bucket of a first auxiliary data structure by using a first hash function, and updating the frequency and frequency counter state of a subgraph pattern in the corresponding bucket; obtaining the frequency of the updated subgraph pattern, and if the frequency is equal to a support threshold value sigma, mapping the corresponding four-element information of the subgraph pattern into a corresponding unit of a second auxiliary data structure by using a second hash function, and updating the four-element information in the corresponding unit; and obtaining repeated events in the current social network according to the events corresponding to the subgraph pattern in the updated second auxiliary data structure. The method can efficiently detect repeated events on a social network stream graph.

The application discloses a kind of social network information propagation effect fast evaluation's prediction user influence method, method includes the following steps: step A, training data and constructing with offline annotation;Step B, seed set-graph joint coding;Step C, comparative learning;Step D, influence value regression and sorting consistency joint training;Step F, agent model auxiliary evolution's intelligent optimization solution.The application significantly reduces the time overhead and the power consumption of single influence evaluation, so that the influence maximization method can be stably landed in large-scale network, real-time or quasi-real-time business system;The application constructs influence similar sample, significantly different sample and marginal gain sorting sample, and applies constraint in representation space and output space simultaneously, so that the agent model can still maintain stable and reliable relative ordering relationship between candidate seed set in the presence of certain numerical estimation error, significantly improve the overall consistency.

This invention discloses a method for predicting user influence for rapid evaluation of information dissemination effects on social networks. The method includes: acquiring and preprocessing graph data of the target social network; inputting the preprocessed graph data and node features into a cascaded perceptron graph neural network, performing multiple rounds of cascaded perceptron message passing, and generating embedded representations of all candidate nodes in the target social network; inputting the embedded representations into a deep reinforcement learning policy network and evaluation network, using a Mannhausen reinforcement learning mechanism to calculate the value function of the current candidate node's action, and evaluating the influence potential of each candidate node; selecting the candidate node with the highest score based on the evaluation results and adding it to the seed set, and updating the node state mask; repeating the above steps until the seed set reaches a preset budget, and outputting the final influence-maximizing seed set. This invention significantly improves the accuracy of seed selection, decision stability, and computational efficiency.

This invention discloses a parallel reverse shuffling sampling method, belonging to the fields of graph data processing and machine learning. The method involves sampling k elements from n elements, performing k reverse shuffling operations to process the shuffling process in reverse order, randomly selecting a position within the corresponding deck size range for each operation, and determining the sampling strategy based on whether an element has already been sampled. The time complexity of this method is O(k·log₂k), which is only related to the number of samples, overcoming the limitation of traditional methods by the degree of the original graph nodes. Furthermore, a GPU parallel implementation method is provided, which decouples operation dependencies through a label propagation strategy, achieving a time complexity of O(k·log₂k). 2 (k / warp_size). This invention achieves a 5.89x performance improvement in fanout-based neighbor sampling and a 5.30x performance improvement in probability-based neighbor sampling, and can be widely applied to large-scale graph data processing in fields such as social networks, recommender systems, and bioinformatics.

The application discloses a kind of prompt enhanced online social network privacy perception method, system, equipment and medium, method includes: depth collection analysis social network dataset, based on the dataset constructs the field knowledge graph containing multidimensional privacy disclosure mode, with hybrid retrieval algorithm retrieves privacy trigger word, entity and disclosure mode from graph with input text semantic highly relevant as result, result is integrated into dynamic prompt word template generation enhanced prompt, input large language model carries out fine-grained privacy identification.The application realizes the change from "entity recognition" to "entity-situation relationship determination" by association labeling strategy, combined with the situational knowledge injected by dynamic prompt word mechanism, so that the model can accurately distinguish between real privacy disclosure and public information description.In dealing with ambiguous expressions and new network language, due to the continuous updating ability of knowledge graph and the generalization performance of contrast learning feature representation, the recognition accuracy is significantly improved compared with existing methods.