15 results about "Web community" patented technology

The invention provides a training method and system of a network community dynamic graph generation model and a graph generation method, and is applied to the technical field of data mining. The method comprises the following steps: acquiring a historical dynamic time sequence diagram of a target network, and carrying out step-by-step feature extraction on the historical dynamic time sequence diagram by utilizing a feature extraction module according to a time sequence to sequentially obtain a diagram embedding representation, a hidden variable and a hidden state; inputting the graph embedded representation into a community detection module to obtain a community detection result; inputting the hidden state into a community prediction module to obtain a community prediction result of the prediction time step; inputting the hidden variable and the hidden state of the previous time step into a topological structure generation module to obtain a generated network diagram; determining training loss based on the community detection result, the community prediction result of the prediction time step and the generated network diagram; and updating model parameters based on the training loss. According to the method, the problems that application scene requirements sensitive to dynamic changes cannot be met and the quality of generated dynamic graph data is not high in the prior art are solved.

The application aims to provide a network community class group and user unified modeling graph pre-training method, which comprises the following steps: constructing a heterogeneous graph model with hierarchical features by extracting semantic information and structural information of two different objects of community level and user level; in the pre-training process of the model, by introducing a self-supervised task that can adapt to different hierarchical objects, the model can represent the characteristics of different hierarchical objects and the mutual relationship between the hierarchical levels; in the pre-training process of the model, by designing a hierarchical iterative training method, the model can promote the mutual representation between different hierarchical levels, and complete various complex inference tasks with community and user as target objects.

The application relates to the technical field of network analysis, and discloses a network community discovery system and method through matrix analysis, which comprises the following modules: a network module, which converts a static network topology into an information propagation model, and establishes a node state time sequence dynamic model by defining a node information processing rule and a multi-round propagation mechanism; a phase space reconstruction module, which maps high-dimensional time sequence data to low-dimensional phase space through a nonlinear dimension reduction method to form node trajectory distribution data; a community feature module, which analyzes the convergence, oscillation mode and attractor feature of the node trajectory, and generates community structure feature data through trajectory similarity; and a community division module, which identifies a community boundary through density clustering, and constructs a hierarchical community organization through multi-scale analysis. The application can deeply mine the internal community structure of a network from the perspective of dynamic information propagation, and overcomes the limitation that traditional methods only consider static topology.

The invention relates to the technical field of smart communities, and discloses a grid-based smart community big data service system, which comprises a grid division module, a data acquisition module, a big data processing module, a service execution module and a central control module, the system is also integrated with an interoperability and expansibility unit; the community geographic area is subjected to gridding division, the gridding granularity is dynamically adjusted according to the population density, the building type and the infrastructure distribution, fine space management and optimal configuration of community resources are achieved, meanwhile, multi-source data and services are integrated on the basis of the grid units, the definiteness and pertinence of community management are guaranteed, and the community management efficiency is improved. The resource utilization efficiency and the management precision are improved; multi-dimensional data such as environment, people flow and facility states are collected in real time through an Internet of Things sensor network, community monitoring equipment and a user terminal, edge calculation and a distributed framework are adopted for integrated analysis, and data support is provided for decision making.

The application belongs to the field of network community, and discloses a network community hiding method based on user attribute characteristics and related devices, which comprises the following steps: obtaining the edge addition selection probability of each community pair according to the separation degree, attribute similarity and the number of nodes of each community in the community pair; obtaining the edge deletion selection probability of each community according to the cohesion degree, attribute uniqueness and the number of node pairs; randomly selecting a community pair as a target community pair according to the edge addition selection probability of each community pair, and adding an edge to the non-edge with the minimum product of the sum of node degrees and (1-attribute similarity) in the target community pair; and randomly selecting a community as a target community according to the edge deletion selection probability of each community, and deleting an edge with the maximum product of the sum of node degrees and (1-attribute similarity) in the target community. The method breaks through the limitation of a specific target community mode, effectively overcomes the drawbacks that existing algorithms generally ignore node attribute information, and improves the community hiding performance.

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.

The present invention relates to the field of e-commerce network data mining and discloses a method and system for discovering e-commerce network communities based on semi-supervised network representation learning. The method comprises steps of constructing an e-commerce network adjacency matrix, a feature matrix, and a partial user label matrix; constructing and optimizing an e-commerce network community discovery model based on the semi-supervised network representation learning; and outputting the results of the e-commerce network community discovery model based on the semi-supervised network representation learning. The e-commerce network community discovery model based on the semi-supervised network representation learning is constructed using two independent GCN encoders, and a joint optimization objective function is designed, and the model is trained by minimizing the objective function. The system comprises a computer processor and memory, an e-commerce network adjacency matrix, a feature matrix, and a label matrix unit, a training unit for the e-commerce network community discovery model based on the semi-supervised network representation learning, and a unit for outputting the results of the e-commerce network community discovery model based on the semi-supervised network representation learning.

An information processing device (100) extracts a plurality of topics in which a first user is interested and thinking tendency of the first user on the basis of content posted by the first user, in a network community. The information processing device (100) extracts topics in content posted by the first user and a plurality of second users in the community, and generates a context graph including a plurality of pieces of the posted content which are related to the same topic. The information processing device (100) updates the context graph by summarizing, on the basis of the thinking tendency, the plurality of pieces of the posted content which are included in the context graph. When a question is received from the first user, the information processing device (100) generates an answer to the question on the basis of the context graph corresponding to the topic of the question.

The present disclosure relates to a content distribution method and apparatus, an electronic device, a storage medium, and a program product, and relates to the field of computer technologies. The content distribution method includes: generating dynamic content of a target agent according to timeliness information, and attribute information of the target agent; and distributing the dynamic content of the target agent in a network community, wherein the network community includes the dynamic content of the target agent and dynamic content of a user.

The invention relates to the technical field of network analysis, and discloses a network community discovery system and method through matrix analysis, and the system comprises a network module which converts a static network topology into an information propagation model, and builds a node state time sequence dynamic model through defining a node information processing rule and a multi-round propagation mechanism; the phase space reconstruction module is used for mapping high-dimensional time sequence data to a low-dimensional phase space through a nonlinear dimension reduction method to form node track distribution data; the community feature module is used for analyzing convergence, oscillation modes and attractor features of node trajectories and generating community structure feature data through trajectory similarity; the community division module identifies community boundaries through density clustering and constructs hierarchical community organizations through multi-scale analysis, the community structure in the network can be deeply mined from the perspective of dynamic information propagation, and the limitation that only static topology is considered in a traditional method is overcome.

The invention relates to the technical field of artificial intelligence, in particular to a network community security and governance system based on AI drive, and the system comprises a content auditing module which carries out the multi-modal analysis of text, voice and image data issued by a user, and precisely recognizes the type of illegal content; the account risk control module deeply analyzes user behavior data, constructs a model, identifies risk accounts and performs grading processing; the conflict mediation module fuses sentiment analysis and interaction data to evaluate a conflict level and intelligently formulates a mediation strategy; meanwhile, the model optimization module monitors the performance of each module, compares an evaluation result with a preset threshold value, and dynamically optimizes a model strategy. According to the method, community health ecology is comprehensively maintained, content compliance is remarkably improved, false malicious accounts are effectively prevented, user conflicts are timely resolved, community management demand changes are continuously adapted, and the credibility and satisfaction of users to network communities are greatly enhanced.

The invention discloses a multi-population alternate coevolution method based on community division guidance, and belongs to the field of large-scale network key node detection. The core of the method is that a network community is accurately divided through a Leiden algorithm, a multi-dimensional node evaluation system and a structured population interaction mechanism are constructed, and a CPMCEA algorithm is adopted for solving. According to the algorithm, a community structure penetrates through the whole process of population initialization, evolutionary operator design and population interaction, population quality and diversity are considered, community-level local optimization and cross-community coevolution are achieved, the efficiency and quality of large-scale network key node detection are remarkably improved, and the method is suitable for large-scale network key node detection. The method is suitable for key node identification tasks in scenes of network security, system vulnerability evaluation and the like.

This invention discloses an automatic method for generating shape-constrained semantic word clouds. The technical solution of this invention involves the processing of text and shapes. The text processing flow includes: extracting words from the text; converting words into word vectors using the word2vec model and calculating the similarity between words; constructing an undirected graph based on the similarity and grouping words using a network community partitioning algorithm. The shape processing flow includes: extracting shape boundary points to form simple polygons; calculating the central axis of the simple polygons; obtaining the Venn diagram of the simple polygons based on the central axis, thereby generating spirals inside the shapes; and calculating vector fields based on direction constraints. After processing the text and shapes, a word cloud layout is performed to obtain a shape-constrained semantic word cloud. The word cloud generated using this technology satisfies both the characteristic of semantically similar words being close to each other in the word cloud layout and the characteristic of shape-constrained word clouds where words are placed inside arbitrary shapes.

The application relates to the technical field of Web data mining, and discloses a Web network community division method based on multi-order neighbor information importance. In the method, the network community division process comprises the following steps: Web network graph representation, pretreatment of graph data, feature propagation, calculation of neighbor importance around nodes, and updating of graph topology. After the Web network is represented in the form of a graph, the attributes of the nodes can describe Web pages, and the network topology can represent the connection between the Web pages. Since the information of a single Web page is often not comprehensive enough, feature propagation can be carried out on the basis of the graph topology, and then the features of the nodes and the surrounding neighbors can better describe the characteristics of a community. Meanwhile, the strategy of learning the multi-order neighbor information importance of the nodes can better distinguish the importance of the neighbor information of different orders for the Web pages. When the community structure feature is described, the method can fully reflect the comprehensive features of the community, so that the rationality and accuracy of the community division result are improved.

The invention belongs to the field of network communities, and discloses a network community hiding method based on user attribute driving and a related device, and the method comprises the steps: obtaining the edge addition selection probability of each community pair according to the separation degree and attribute similarity of each community pair and the number of nodes of each community in the community pair; obtaining an edge deletion selection probability of each community according to the cohesion, the attribute uniqueness and the node pair number of each community; randomly selecting the community pair as a target community pair according to the edge addition selection probability of each community pair, and selecting a non-edge with the minimum product of the sum of node degrees and (1-attribute similarity) in the target community pair to perform edge addition; randomly selecting the community as a target community according to the edge deletion selection probability of each community, and selecting the edge with the maximum product of the sum of node degrees and (1-attribute similarity) in the target community for edge deletion. According to the method, the limitation of a specific target community mode is broken through, the defect that node attribute information is generally neglected in an existing algorithm is effectively overcome, and the community hiding performance is improved.