7 results about "Web community" patented technology

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.

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 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.