16 results about "Community network" patented technology

1. Name of the product in this design: Intelligent Positioner. 2. Purpose of this design: Bluetooth anti-loss device, used to help users locate easily lost items such as keys, wallets, and backpacks. Its core function relies on Apple's "Find My" network or Google's "Find Hub" global community network to find items. 3. The key design feature of this product is its shape. 4. The image or photograph that best illustrates the design's key points: 3D view 1.

This invention discloses a dynamic quantile filtering method for product co-occurrence networks, comprising: firstly, constructing a product co-occurrence network based on the co-occurrence relationships of products in orders; then, employing a dynamic quantile filtering strategy to denoise the network, specifically: obtaining the co-occurrence frequency weights of all product pairs and forming a co-occurrence matrix; extracting all non-zero co-occurrence frequency weights for each node; defining a local adaptive threshold based on the quantiles of these weights; retaining edges greater than or equal to the threshold; resetting the weights of edges less than the threshold to zero; and retaining isolated nodes; symmetricizing the adjacency matrix using an intersection strategy to obtain a weighted adjacency matrix; updating the network to obtain the denoised product co-occurrence network; and finally, using the Louvain algorithm to perform community partitioning on the denoised network to obtain a product community network. This invention can adaptively filter out weak connections and noisy edges in the product co-occurrence network, effectively improving the accuracy and robustness of community detection.

The application provides a non-negative matrix factorization community detection method based on latent structure and attribute, and relates to the field of community network detection. The method mainly solves the problem that the existing method does not sufficiently utilize community network information. The method comprises the following steps: firstly, preprocessing community network data, and representing information in the network; calculating an adjacency matrix, an attribute matrix and a latent structure matrix; establishing a community detection model mainly based on direct topological structure and attribute information and supplemented by latent structure information; calculating an iterative updating rule; setting the number of iterations, initializing a community member matrix, two community-community matrices and the latent structure matrix, and adjusting a weight parameter; performing iteration to obtain a target matrix; and finally discovering a community according to the iteration result community member matrix.

The application discloses a fast community query system and method with precision guarantee under a large-scale network.The method is as follows: 1, obtaining attribute data from multiple different sources, involving target information of related fields, and constructing a large-scale community network of a target field in a graph manner according to the information; 2, setting precision constraints, confidence, structure constraints and a target node according to a query requirement, the target node being a searched object; then, attribute-aware sampling, approximate estimation, candidate community optimization and sampling parameter adjustment based on errors are sequentially performed on the large-scale community network, and finally, a query result of the community query is obtained.The application introduces attribute-aware sampling into the community query method to control the size of the queried community, and uses an approximate estimation method to ensure the community quality.Meanwhile, the application comprehensively considers the attribute association between entities, and has the close characteristics with high similarity.

The invention discloses a user role analysis method and system in a network community, and relates to the field of social networks, and the method comprises the steps: respectively obtaining community network interaction data about a preset category viewpoint published in a preset social network platform; wherein the community network interaction data comprises a post and a posting user or a forwarding user of the post; determining an optimal issue on a preset category viewpoint; based on the social network interaction data, calculating a semantic polarization degree of an optimal topic of each user on a preset category viewpoint; and performing network community user role division on each user according to the semantic polarization degree of each user on the optimal issue to obtain a division result. According to the method, more detailed understanding and role division can be carried out on the community presenting the binary structure on the preset category viewpoint in the social network, and whether the user is living in the information cocoon house of the preset category viewpoint or not is analyzed.

The application relates to the technical field of big data, and particularly discloses a user behavior identification method and device, equipment, a storage medium and a program product. The method comprises the following steps: acquiring a community network to be processed, the community network comprising a plurality of nodes and a plurality of edges, each node being used for representing user data of a user to be identified, and each edge being used for representing association weight information between two nodes; using a community discovery algorithm to perform a graph reconstruction cycle on the community network in a plurality of processes until the modularity gain between the plurality of nodes in the community network is all lower than a gain threshold; wherein the community discovery algorithm is used for performing community merging on the plurality of nodes in the community network according to the plurality of edges in the community network, and performing sub-community splitting according to the connected state between the nodes in the merged community; and the user behavior of the user to be identified is inferred according to the optimized community network. The method can improve the identification accuracy of the user behavior.

The invention provides an insurance product recommendation method and device, electronic equipment and a storage medium, and the method comprises the steps: extracting an explicit feature vector of a to-be-recommended user according to the user information of the to-be-recommended user; based on the social relation of the to-be-recommended user in the community network, extracting an implicit feature vector of the to-be-recommended user; splicing the explicit feature vector and the implicit feature vector of the to-be-recommended user to generate a fused feature vector of the to-be-recommended user; determining a plurality of target reference users based on the similarity between the fusion feature vector of the to-be-recommended user and the fusion feature vector of the reference user; and based on the insurance type purchase list of the target reference user, determining an insurance type recommendation list of the to-be-recommended user.

This invention discloses a method and system for analyzing user roles in online communities, relating to the field of social networks. The method includes: acquiring community network interaction data on preset social network platforms regarding preset category viewpoints; wherein the community network interaction data includes posts and the users who posted or forwarded the posts; determining the best topic on the preset category viewpoint; calculating the semantic polarization degree of each user on the best topic on the preset category viewpoint based on the community network interaction data; and classifying each user into online community user roles based on the semantic polarization degree of each user on the best topic, obtaining the classification result. This invention enables a more detailed understanding and role classification of communities in social networks that exhibit a binary structure on preset category viewpoints, and analyzes whether users are living in information cocoons of preset category viewpoints.

The invention discloses an abnormal object recognition method and device, a medium and equipment, and relates to the technical field of computers, and the method comprises the steps: obtaining the attribute information and behavior information of a to-be-recognized object in a target interaction scene; performing feature extraction processing on the attribute information to obtain first feature data of the to-be-identified object; performing statistical analysis processing on the behavior information, and determining second feature data of the to-be-identified object; based on a community network corresponding to the target interaction scene, determining third feature data of the to-be-identified object, the third feature data indicating correlation between the to-be-identified object and an abnormal sub-scene in the target interaction scene; the first feature data, the second feature data and the third feature data are input into an abnormal object recognition model for recognition processing, a recognition result for the to-be-recognized object is obtained, and the recognition result indicates whether the to-be-recognized object is an abnormal object in the target interaction scene or not. According to the invention, the recognition accuracy of the abnormal object can be improved.

The invention provides a community information dissemination system and method based on a random directed hypergraph theory, which introduces community structures and attributes on the basis of a traditional information dissemination model on the basis of the directed hypergraph theory, simulates high-order asymmetric interaction in a real society through a method of randomly adding directed hyperedges, and provides a community information dissemination system and method based on the random directed hypergraph theory. According to the method, roles and susceptibility degrees of different individuals in information propagation are distinguished, and a new social information propagation model and a theoretical calculation method of the influence density of the social information propagation model are constructed. In practical application, community network model parameters are set according to a to-be-analyzed information spreading scene, a random simulation experiment is carried out by using a Monte Carlo algorithm, so that the influence density approaches a theoretical value, and spreading of information, influence or infectious diseases in a complex network in a real society can be better simulated, understood and explained; the critical value of the key control parameter can be quickly found, and the control and management of the information spreading can be further realized.

This invention discloses a method, apparatus, equipment, and medium for flood risk assessment that integrates population networks. The method includes: constructing a flood physical risk layer based on hydrological and meteorological data and geographic information data of the target area; constructing a population network of multiple population network scenarios with individuals in the target area as nodes based on spatiotemporal trajectory data of the target area, and determining the target population network corresponding to the weakest population network scenario; performing spatial overlay analysis on the flood physical risk layer and the target population network to generate a flood risk distribution map representing the collaborative flood risk level of different spatial units in the target area; this invention uses the spatiotemporal trajectory data of individuals to represent the connections between individuals in the target area for collaborative flood risk analysis, which can effectively overcome the systematic bias caused by assessment based on a single physical dimension, and achieve accurate quantification and true reflection of urban flood resilience level.

This paper relates to a cross-platform method and apparatus for detecting abnormal account behavior. It involves segmenting historical account behavior data from one or more platforms into community networks to obtain account behavior communities, and training a corresponding anomaly detection model for each community. During model training, a core platform set within each community is identified that meets the risk prediction requirements, and the amount of historical account behavior data within those communities is adjusted based on this core platform set. In the application phase, the target account behavior community to which the account to be detected belongs is determined, and its real-time behavior data is input into the corresponding model to obtain the abnormal behavior risk prediction result. The proposed method effectively identifies cross-platform collaborative abnormal behavior, significantly improving anomaly detection accuracy and model training efficiency.

The application discloses a kind of attribute community query method and system based on relationship tree, the method first obtains the data relationship of target field, constructs the attribute information network of target field in the mode of graph.Secondly, attribute information network is loaded through unified access interface, data processing, subgraph construction and algorithm operation and query are carried out on attribute information network.Then, the interface of attribute community query is provided to user, input community structure parameter k value and query target q, return a community containing query point q, satisfy structure cohesion and optimal attribute cohesion.Finally, the rendering and interaction of user interface, it presents the community query result returned by interface to user through graphical method, renders the query result as dynamic community network graph.The application does not need to give query attribute list, reduces user input, so that community query result is more in line with actual.

The application discloses a network reconstruction and overlapping community detection joint method, device, equipment and medium, the method comprises the following steps: obtaining binary time series data and community quantity of community network; initializing community membership matrix and community interaction matrix; using expectation maximization algorithm to iterate community membership matrix and community interaction matrix to obtain target community membership matrix; determining the community structure of the community network and the overlapping community in the community network based on the target community membership matrix. The application explicitly introduces the community membership variable in the parameter space, so that the network inference and community detection, which are regarded as independent problems, form a unified optimization architecture. The responsibility variable is used to track the specific contribution of each edge to the observed dynamics, ensuring the deep coupling and mutual promotion of network inference and community detection. The application not only realizes the detection of the underlying overlapping community structure of the complex network and the accurate reconstruction of the hidden network topology structure, but also ensures the reconstruction accuracy of the complex network.

This invention relates to the fields of complex network analysis and social network data mining, and provides a community discovery method based on user values, comprising the following steps: S1: constructing user-topic dual-encoding representations; S2: decoupling representations based on value hierarchy structure; S3: value semantic alignment constraints; S4: user value representation generation and optimization; S5: hybrid similarity construction and node centrality calculation; S6: label update strategy based on hybrid centrality ranking; S7: label propagation driven by multi-level influence; S8: community attribute center construction and fuzzy node identification; S9: fuzzy node re-attribution correction based on community attribute centers to solve the problems of difficulty in obtaining stable user value representations under low / no corpus conditions, and difficulty in effectively introducing value orientation as node attributes into community partitioning, thereby achieving stable community partitioning based on user values.

The application discloses a question recommendation method based on a schema in a question and answer community, and the steps include: 1, collecting data and constructing a question and answer community network; 2, using a schema mining algorithm to mine the schema in the user network, and constructing a schema network based on the schema; 3, using a network embedding learning algorithm to learn the node embedding of the network, and learning the preferences of the answerer for the questioner and the question; 4, fusing the preferences of the answerer in two aspects, using a scoring function to predict the matching degree of the answerer for a new question, arranging the answerer in descending order according to the matching score, and recommending the first N users to answer the question, so as to complete the question recommendation task. The application combines the schema mining algorithm and the embedding learning algorithm of the schema network, fully captures the historical interaction information of the users in the schema network by using the graph neural network, and fully fuses the personal feature information, network structure information and text semantic information of the users, so that more accurate recommendation effect is realized.