117 results about "Community based" patented technology

A disclosed data platform may be configured to access a custom policy developed by a particular client entity that uses the data platform. The custom policy may invoke a query that targets a target dataset ingested from a cloud environment and stored in a data store. The data platform may determine that the custom policy is likely to be of value to one or more other client entities, besides the particular client entity, that also use the data platform. In response to the determining that the custom policy is likely to be of value to the one or more other client entities, the data platform may generate a community policy based on the custom policy. The community policy may be available for use by the one or more other client entities. Corresponding methods, systems, and products are also disclosed.

The invention provides an electric power overhaul decision-making method and device based on knowledge domain mapping map retrieval generation. The method comprises the following steps: performing deep semantic analysis on an electric power overhaul file through a prompt project guide large language model LLM, and constructing an electric power overhaul knowledge domain mapping map; generating communities with hierarchical structures by adopting a community detection algorithm Leiden based on the electric power overhaul knowledge domain mapping map, and creating a community abstract in a report form for each community; and mapping an electric power overhaul query request initiated by a user to the embedding space of the electric power overhaul knowledge domain mapping map through a vectorization technology to realize semantic matching with the community abstract, and outputting an electric power overhaul decision answer result corresponding to the electric power overhaul query request. According to the method, an intelligent question-answering system in the field of electric power overhaul is constructed by fusing knowledge domain mapping map retrieval enhancement generation and LLM, automatic knowledge extraction and map construction of unstructured overhaul texts are realized, and high-reliability decision support is provided for fault diagnosis, overhaul plan optimization and risk early warning of electric power equipment.

The invention discloses a multi-modal social network public opinion hidden danger troubleshooting method and system, and the method comprises the steps: collecting the multi-modal data of a social platform in real time through a distributed edge node, and the multi-modal data comprise text, image and audio data; dividing a plurality of social network communities based on user interaction relationships, wherein the user interaction relationships include but are not limited to topic circles, friend relationships, comment interaction and forwarding likes; community propagation dynamic features, group topological features and content features are extracted from the multiple social network communities one by one based on the multi-modal data; screening out a plurality of suspected hidden danger communities from the plurality of social network communities based on the community propagation dynamic characteristics, the group topological characteristics, the content characteristics and a preset screening strategy; and after carrying out hidden danger detection and hidden danger classification on the plurality of suspected hidden danger communities based on the multi-modal data, outputting a plurality of hidden danger classifications corresponding to the plurality of suspected hidden danger communities. According to the method and the system, the comprehensiveness and the accuracy of public opinion hidden danger recognition are remarkably improved.

The invention discloses an overlapping community detection method, system and device and a medium, and particularly relates to the technical field of community detection, and the technical key points are as follows: extracting node information data from a pre-constructed adjacent matrix of an overlapping community, and inputting the node information data into a point mutual information function to calculate and obtain point mutual information; obtaining a superpoint mutual information matrix by combining the point mutual information with the hypergraph structure; constructing a target function by using the super-point mutual information matrix and a pre-constructed three-factor illegal matrix decomposition optimization model based on graph regularization; decomposing the super-point mutual information matrix in the objective function to obtain an indication matrix, and solving the indication matrix by using an alternating iteration method to obtain an optimal solution of the indication matrix; and detecting the overlapping community based on the optimal solution of the indication matrix to obtain an overlapping community detection result.

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 invention discloses a social network community division method based on a graph attention mechanism, and belongs to the technical field of social network analysis. According to the method, firstly, nodes and edges of the nodes in the social network are preprocessed, and then a graph attention mechanism is introduced through a graph neural network to capture local features and global structure information of the nodes. After node feature embedding is completed through multi-layer information aggregation, a clustering algorithm is improved by comprehensively considering the node attribute feature similarity degree and the proximity degree of nodes in a network topology structure, and community division is conducted on the nodes through the improved clustering algorithm. Experimental results show that compared with an existing community division method, the method has the advantages that the accuracy and the efficiency are remarkably improved, the potential community structure in the social network can be better recognized, and the method can be widely applied to the fields of social network analysis, user behavior prediction, recommendation systems and the like.

The invention discloses a community division-based power distribution network two-stage voltage out-of-limit control method and related equipment. Firstly, community division is carried out on nodes containing a photovoltaic power distribution network based on an improved particle competitive learning algorithm to obtain a plurality of communities so as to enhance the rationality and stability of community division. And secondly, establishing a voltage out-of-limit control model based on a linearized power flow equation in the community, and when node voltage is out of limit, realizing rapid autonomous control and eliminating local voltage out-of-limit by adjusting SVG reactive power and photovoltaic active and reactive power in the community. And thirdly, after autonomous control in the community is completed, power and voltage information of boundary nodes of the community is collected, a distributed optimization method is adopted to carry out coordinated regulation on SVG and photovoltaic across the community, and a power flow equation is utilized to correct the voltage of the boundary nodes, so that global optimization of the voltage of the whole network is realized.

An electronic gaming device for providing a community-based electronic game including a community matrix for providing community gameplay of the community-based electronic game. The community-based electronic game includes a base game including a first set of reels and the community matrix displayed within a display interface of the community-based electronic game. One or more social trigger symbols associated with play of the base game by a plurality of players are randomly determined and presented on one or more reel columns of the first set of reels. For each of the one or more social trigger symbols, one or more community social trigger symbols are presented on one or more columns of the community matrix, each of the one or more community social trigger symbols being attributed to respective one or more players of the plurality of players of the base game associated with at least one social trigger symbol.

The invention relates to the technical field of intelligent supervision data processing, and discloses an intelligent supervision method and system based on big data, and the system comprises a community intelligent supervision recognition module, a community intelligent supervision processing module, and a community intelligent supervision pushing module. Community violation person registration and non-registration identity type efficient and accurate judgment are carried out based on standard community real-time scene image information in combination with an intelligent search algorithm and community registration person appearance image information stored based on big data; according to the standard community real-time scene image information, in combination with an intelligent recognition algorithm and the identity feature information corresponding to the standard stored appearance image of the community registration personnel, carrying out intelligent search on the identity information of the illegal personnel of the community registration; community intelligent supervision result summary information is scientifically constructed based on registration and non-registration identity type classification of community violation personnel, and the intelligence and quality of community intelligent supervision are improved.

Systems and methods are provided, that include collecting, via a data collection system, a social network data from one or more social networks, and receiving a credit voucher from a first entity of the one or more social networks, wherein the credit voucher assigns a credit score to a second entity of the one or more social networks. The systems and methods also include generating a community-based credit score for the second entity of the one or more social networks based on an analysis of the social network data and the credit score, and receiving a request for the community-based credit score sent by a requestor. The systems and methods additionally include providing the community-based credit score to the requestor.

The present invention relates to the field of data processing technology, and in particular to a smart community integrated management platform based on big data analysis, comprising: a communication load analysis module, which obtains load data of the community network by analyzing the types and quantities of various terminals accessing the community base station within a preset time period; a special node positioning module, which identifies special network nodes in the community by dividing special service IP addresses and monitors the network behavior of special network nodes by setting data acquisition rules; a monitoring terminal connection module, which obtains data of each monitoring terminal by connecting to the community monitoring network; and a comprehensive management module, which determines whether there is a security vulnerability in community management by analyzing the matching degree between various terminal access characteristics, special node data flow characteristics and personnel flow characteristics, and outputs an alarm signal.

The present disclosure provides a method of facilitating personalized recommendation within a community membership-based marketplace. Further, the method may include receiving a user profile data from a client communication device. Further, the method may include receiving a product metadata from an asset manager device. Further, the product metadata may be associated with a product. Further, the method may include transforming the user profile data into a first vector representation. Further, the method may include transforming the product metadata into a second vector representation. Further, the method may include identifying a recommendation data by calculating a similarity between the first vector representation and the second vector representation. Further, the method may include storing the recommendation data. Further, the method may include transmitting the recommendation data to the client communication device.

The invention discloses an epidemic disease early warning system and method based on college volunteer participation, and relates to the technical field of epidemic disease early warning. The epidemic disease early warning method based on college volunteer participation comprises the following steps: acquiring related data of a corresponding community based on college volunteers; solving an optimal solution of the speculation model by using a trust region algorithm; finishing feature processing, mapping a feature value into an output value, and obtaining a final prediction result of the corresponding community; and completing the precision evaluation of the corresponding community prediction result. According to the method, the early warning time is greatly shortened, independent early warning of each independent community in a regional range is completed in a point form, the early warning condition of each independent community in the region is combined, a prediction result is fused, an existing relatively mature neural network speculation model is combined, external data such as weather and medical treatment of the region are imported, and the early warning efficiency is improved. The operation of the direct data end is simple, and the subsequent speculation efficiency is improved.

In examples, a first Anycast advertisement is received from a first server identifying the first server as a primary address for a service. In addition, a second Anycast advertisement is received from the first server identifying the first server as a secondary address for the service. Further, a third Anycast advertisement is received from a second server identifying the second server as the secondary address for the service. A first community advertisement is also from the first server identifying the first server as a member of a first community. Thereafter, a first request is received from a client including the primary address, and the request is routed to the first server. When a second request from the client is received including the secondary address, based at least on the community advertisement, the first server is ignored, and the second request is routed to the second server.

This invention discloses a method for community detection in social networks with unknown community cardinality. The method first constructs an encoder, trains it using an arbitrary unsupervised method, maps the input social network to Euclidean space, and obtains the representation vectors of the vertices. It then calculates the similarity matrix of vertex pairs, sorts the similarities, selects vertex pairs with high similarity as positive samples, and selects vertex pairs with low similarity as negative samples, assigning pseudo-labels to each to form the training set. Next, it constructs a community relationship prediction model, trains it using a binary cross-entropy loss function, predicts the community relationships of vertex pairs, and optimizes hyperparameters. Finally, it constructs a structure graph based on the model's prediction results, inputs the structure graph into the Liu-Wen algorithm to obtain the final community detection result. Experiments show that, without providing community cardinality, this method can accurately detect the community structure in social networks.

The invention discloses a graph analysis-based government and enterprise customer cross-marketing method and system, and belongs to the technical field of computers. The method comprises the following steps: acquiring basic data; constructing a knowledge graph according to the basic data; performing community division on the knowledge graph to obtain a plurality of communities; based on average or similarity calculation, screening high-quality communities from all communities; and recommending the high-quality community. Through cross marketing, an enterprise can provide more product and service combinations for government and enterprise customers, and the diversified requirements of the government and enterprise customers are met. For example, while an informatization system is provided for government customers, related data security services, cloud computing resources and the like can be provided, so that the customers are more dependent on an overall solution of an enterprise, and the stickiness and loyalty of the customers are improved.

Techniques are disclosed for community-based federated training by leader node representation. An example system includes a memory having instructions, and a processor communicatively coupled to the memory and configured to execute the instructions. The instructions can include: using a community detection (CD) algorithm to partition a network of nodes into a plurality of communities; using graph-based measurements of the network to elect a leader node for each community; and performing federated learning within and between the communities through the elected leader nodes.

The present disclosure provides an object identification method, which can be applied to the field of artificial intelligence and big data, and includes: converting transaction data into graph data, wherein the nodes of the graph data are transaction objects contained in the transaction data, and the edges of the nodes are transaction relationships contained in the transaction data; determining an initial community containing a target node based on the graph data; determining a core node set in the initial community and an attribute initial weight of the core node set; determining a quality index of the initial community, the quality index including a community density and a community attribute entropy, the community density being used to represent a dense degree of node pairs in the initial community, and the community attribute entropy being used to represent a confusion degree of nodes in the initial community; expanding the core node set of the initial community based on the attribute initial weight, the community density and the community attribute entropy to obtain a local community; and identifying transaction objects corresponding to core nodes included in the local community as target objects. The present disclosure also provides an object identification device, equipment, storage medium and program product.

Since IP network topology often has a clear hierarchical structure, using community detection algorithm in graph theory can divide the topology into several regions with high cohesion and low coupling, and selectively spread the routing update in the region to other regions, which can effectively avoid the calculation amount of full routing simulation, and realize fast and accurate IP network routing simulation reasoning. The application proposes an IP network routing simulation reasoning method based on community detection, which first divides the whole network into multiple cluster structures based on the community detection algorithm, and the shortest path between the nodes in the cluster does not pass through other clusters, and uses this division result as the basis for calculating the routing update. Then, the change operation of the network topology is accurately identified, and according to the network elements involved in the change point and the cluster information to which they belong, the network element node pairs that need to be recalculated are further arranged, and the SPT tree of the node pairs is recalculated and the routing update is performed. At the same time, an IP network routing simulation reasoning device based on community detection is proposed, which includes a topology management module, a network configuration module, a network segmentation module, a change analysis module and a routing calculation module, to realize the topology segmentation and routing simulation reasoning process of the IP network.

The application discloses a minimum community search method, device and system and a storage medium, and is applied to the field of minimum community search, and comprises the following steps: receiving an adjacent index and a core tree index which are encrypted by a BGN algorithm and are sent by a data end; wherein the data end constructs a corresponding Bloom filter array for each tree node in the core tree index; when receiving a query trapdoor token generated by a client, locating an initial community in the core tree index according to the Bloom filter array; generating a target community based on the initial community and the adjacent index by using an array comparison protocol; and sending the target community to the client, so that the client decrypts the target community to obtain a target result. The method avoids the privacy leakage problem caused by the minimum community search in a cloud server in the prior art by encrypting the minimum community search, and protects data security.

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.

This invention relates to the field of computer network security technology, specifically to a CWE (Cost Evidence-Based Weapon) segmentation method, device, and storage medium based on a community discovery algorithm, aiming to improve the benefits of vulnerability classification for vulnerability intelligence analysis. The proposed CWE segmentation method based on a community discovery algorithm includes: constructing a knowledge graph based on the relationships between different CWEs; assigning each CWE in the knowledge graph to a separate community; and using a modularity-based community discovery algorithm to progressively merge communities until the modularity no longer increases, resulting in the final segmented CWE communities. Furthermore, this invention proposes, from an attack benefit perspective, constructing a joint CAPEC and CWE knowledge graph, and using a community discovery algorithm to segment CWEs into communities, significantly improving the benefits of vulnerability classification for vulnerability intelligence analysis.

The present disclosure includes embodiments of a systems and methods of a community-based project platform for providing a collaborative project platform for projects. The embodiments of the methods may include determining one or more available projects for a first set of one or more users, based on a tag for each of the first set of one or more users and a type of the one or more available projects. In response to the determining of the one or more available projects for the first set of one or more users, the methods may additionally include, displaying the one or more available projects to the first set of one or more users.

The method includes: grouping multiple cloud service components in a cloud platform into communities based on call relationships, with intra-community call closeness no more less than a preset value and inter-community closeness less than the value; if at least one target component in a first community has performance below a preset level (meeting the first preset condition), creating a second community identical to the first to share part of its load.

The invention discloses a differentiated privacy protection method and system based on social propagation force perception, and the method comprises the following steps: S1, constructing a social graph, and carrying out the preprocessing and community division of the graph, and obtaining community tags; s2, calculating the propagation degree, the influence and the propagation weight of the node based on the community label; s3, grouping the nodes according to the comprehensive risk score, and presetting an initial differential privacy parameter for each group; s4, based on a grouping result, adaptively adjusting the noise intensity of each group through a water level type strategy, and carrying out differential training; and S5, after training is completed, constructing a strong black box node member to infer attacks, calculating node-level attack advantages and spreading weighted privacy risks, and evaluating and visualizing a privacy protection effect. Under the same global privacy budget, the effective noise variance needing to be superposed is lower than that of an independent Gaussian noise scheme, so that the model training stability can still be maintained in a strong privacy scene.

The invention discloses an intelligent scheduling system and method for community flexible employment resources, and the system supports double ends of an applet and an APP, the core is a community flexible employment personnel management module, multiple APIs verify qualification (related department interfaces), and based on a community scene, an improved KNN algorithm (40% of radius weight of 1 kilometer and old people demand tag matching) is used to realize accurate order sending. And settlement is carried out through a WeChat payment / Alipay interface T + 0. And in combination with the trial data (the old ordering ratio is 62% and the response time is 12 minutes) of the stone house deerspring area, the problems of difficult standardization and slow settlement of the zero workers are solved, the 15-minute service of the residents and the nearby employment of the zero workers are realized, and the method has high practicability.

This invention provides an online retail method, apparatus, device, and storage medium based on community group buying. The method includes: providing community group buying product information to a user on a community group buying client; receiving the user's operation on the community group buying product on the community group buying client, generating a community group buying order or adding the community group buying product to a shopping cart; responding to the user's self-pickup address having a retail store identifier, providing the user with retail product information associated with the self-pickup address on the community group buying client, wherein the user's self-pickup address is the self-pickup address of the community group buying order; receiving the user's operation on the retail product on the community group buying client, generating a retail product order, and marking the retail product order as a self-pickup order. This application combines the retail business of retail stores with community group buying online.

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 invention discloses a community discovery method combining local expansion and importance sequence updating rules, and aims to solve the problems of oscillation, randomness and local optimum of a traditional label propagation algorithm in a complex network. The network is subjected to preliminary community division in combination with the influence of the nodes and the weight information of the edges, and the influence of the nodes is calculated through the characteristics of degree centrality, betweenness centrality, graph embedding and the like. And node labels are updated by using asynchronous label propagation, so that the oscillation phenomenon in the traditional LPA is avoided. The obtained community is optimized through spectral clustering, the community structure is further refined from a global perspective by using a Laplacian matrix and a feature vector, and the accuracy and stability of community division are improved. According to the method, the community division precision in a large-scale and dynamic network can be effectively improved, and noise interference and random influence of a traditional algorithm are overcome. A more accurate, stable and explainable solution is provided for a community discovery task, and deep development of complex network analysis is promoted.