21 results about "Influence propagation" patented technology

The invention relates to the technical field of organization transformation management and data analysis, and discloses a knowledge graph-based influence prediction method and system, and the method comprises the steps: constructing a double-layer knowledge graph which integrates an organization formal hierarchy and an informal social network; processing the double-layer atlas structure by applying a hierarchical graph convolutional network, and calculating multi-dimensional influence features; analyzing based on the multi-dimensional influence features, capturing an organization structure evolution rule by applying a time-varying graph structure learning algorithm, and identifying key opinion leaders; the key opinion leader information is utilized, the organization influence propagation process is simulated based on the Agent technology, and different intervention strategy effects are evaluated; generating a multi-dimensional influence prediction result; compared with a traditional method with a single network structure, the method has the advantages that the group influence prediction accuracy is improved, the key opinion leader recognition accuracy is improved, and the time sequence prediction error is reduced.

The invention discloses an agent fusing social contact and a recommendation simulation environment construction method, and relates to the technical field of recommendation systems. Firstly, through feature vector construction of three dimensions of activeness, conformity and diversity, in combination with an interaction threshold filtering mechanism, data quality is ensured. Secondly, a time sequence weighting mechanism is innovatively designed, and dynamic evolution modeling of user interests is achieved. And thirdly, providing a dynamic updating mechanism of double weights, describing the social connection strength between the users through the combination of a similarity threshold and a feature similarity, and carrying out adaptive updating through a fixed time window. Fourthly, in the aspect of social influence modeling, degree centrality, feature vector centrality and betweenness centrality are fused, and accurate influence calculation is achieved through an influence propagation matrix and a damping coefficient. And fifthly, in the aspect of intelligent agent environment construction, a standardized large language model interface is designed, and the consistency and controllability of intelligent agent decision making are ensured through an input template and an output specification.

The invention provides an e-commerce brand promotion system based on social network influence propagation, and belongs to the technical field of e-commerce brand promotion systems. The system comprises a data acquisition module, a node identification module, a propagation dynamics model construction module, a promotion strategy generation module and an execution and feedback module. The data acquisition module obtains social and e-commerce data in a compliance manner and carries out standardization processing. The node identification module screens and classifies KOL through a multi-dimensional index, and determines potential propagation nodes in combination with K-means clustering; the propagation model module optimizes a'social network-SEIR 'model based on SEIR, and selects an optimal path by using a Dijkstrar + maximum flow algorithm; the strategy module generates a hierarchical KOL cooperation and user incentive scheme; the feedback module collects data per hour, dynamic adjustment is performed within 24 hours, and the method is executed in five steps corresponding to five modules. 30 days after application, the brand popularity is improved by more than or equal to 60%, the cost waste rate of small and medium-sized brands is reduced to 15% or below, the market sinking reach rate is improved to 38%, and popularization requirements of industries such as beauty makeup and the like are met.

Method, storage medium and apparatus associated with content preparation and / or selection are disclosed herewith. In embodiments, an apparatus may include a content preparation or selection module to receive, as part of a content preparation or selection, a representation of a concept; and determine, for the content preparation or selection, topical relevance of a content item (N) of a content hierarchy to the concept. The content hierarchy may include a plurality of content items, including N, and determining the topical relevance of N to the concept includes determination of propagation of influence among content items of the content hierarchy subject to constraints that bound the number of operations performed by the computing system to determine the propagation of influence among content items of the content hierarchy with a chosen constant. Other embodiments may be disclosed and / or claimed.

According to the scholar cooperation influence assessment method based on dynamic contribution weighting provided by the invention, firstly, through fusion of multi-dimensional features such as scholar roles, participation degrees, technical contributions and time-varying reference approval, refined and dynamic measurement of scholar contributions in a single cooperative literature is innovatively realized; the limitation of static distribution according to a simple signature rule in the traditional method is broken through; combining the dynamic contribution weight with literature quality to construct a weighted scholar cooperation network which more accurately depicts cooperation strength and quality; on the basis, a network influence propagation model based on dynamic weighting is introduced, the direct contribution influence of scholars is calculated, and the indirect influence obtained through cooperative network propagation is quantified, so that the real influence of the scholars in an academic community is reflected more comprehensively; by integrating direct and indirect influences and introducing time decay and subject normalization processing, the finally output comprehensive influence index has both time sequence dynamics and interdisciplinary comparability.

The invention discloses an opinion-sensitive negative influence propagation minimization method and system, and the method comprises the following steps: constructing an opinion-sensitive negative influence propagation model, and describing the dynamic propagation process of a social network through introducing the initial opinion features of a user, the edge interaction probability and the front interaction probability; a hybrid calculation strategy is adopted, a node initial opinion contribution value and a subsequent opinion propagation contribution value are calculated in combination with dynamic planning, and a node blocking value is corrected through adaptive sampling; and iteratively selecting a target node set for blocking by using a greedy algorithm based on the calculated node blocking value. The method can accurately adapt to a topological structure and dynamic interaction characteristics of a complex social network, more accurate blocking of negative information is realized in a public opinion management and control scene, an interpretable decision basis is provided for blocking node selection by quantitatively evaluating the harm degree of a negative information propagation path, time consumption is reduced on the premise of keeping blocking precision, and the efficiency is improved. And balance optimization of precision and efficiency is realized.

The application discloses an initial user selection method for competitive influence propagation in a social network with attributes, constructs a social network with attributes according to data in the social network, constructs a kernel-attribute-tree according to the social network, and obtains a candidate user set CS B of a product A according to a known initial user set I A of a competitive product B in the social network. The marginal influence propagation value of each candidate user as an initial user is calculated based on a competitive influence propagation model, the initial user with the maximum marginal influence propagation value is selected from the candidate users, and the initial user selection is continued until the number of selected initial users meets the requirement.

The application provides a symbol graph-oriented influence maximization method and system, and the method comprises the following steps: acquiring symbol graph data, and performing representation learning on nodes by using a symbol-aware attention layer to generate symbol-aware node embedding; a seed set representation module based on a variational autoencoder is constructed, a conflict-aware regularization term is introduced to punish negative relationships in the seed set and encourage positive cooperation; a double-path diffusion model is used to simulate the propagation dynamics of positive and negative influences to obtain node net influence; the modeling capability of a complex teacher model is transferred to a lightweight student model through knowledge distillation; finally, a hybrid reasoning strategy is used to output a seed node set with high net influence and minimum internal conflict. The application can effectively process positive and negative edge heterogeneity information in the symbol graph, accurately simulate the inhibition effect in the influence propagation, generate a high-quality seed set, and has important application value in the fields of social network analysis, viral marketing and the like.

The invention discloses a heterogeneous network influence maximization method based on meta-path semantic perception, and the method comprises the following steps: constructing a heterogeneous network diagram according to heterogeneous network data; obtaining meta-paths of the heterogeneous network diagram, and performing simplification and integration to generate an effective path set; according to a specific heterogeneous network, setting a node path weight, determining a node sampling probability, and constructing a reverse sampling strategy; sampling the heterogeneous network diagram by adopting a reverse sampling strategy, and generating an effective reverse reachable set in combination with judgment of an effective path; and based on the effective reverse reachable set, calculating a node marginal influence propagation gain, and carrying out iterative screening by taking a marginal gain as an index to obtain a target seed set so as to realize influence maximization. According to the method, the reverse sampling strategy is constructed, node semantics and path semantics in the heterogeneous network are combined in the reverse sampling process, semantic perception is considered, key nodes can be recognized and selected more accurately, and therefore the influence of the heterogeneous network is maximized.

The application discloses a two-stage topic-aware influence maximization method using deep learning enhancement in the field of social network information propagation technology, and comprises the following steps: S1, establishing a propagation model and problem modeling; S2, defining a node importance measurement index SRC; S3, step 3: candidate node set construction; S4, a training process of a node importance estimator NIE; S5, a seed optimization process based on an improved hybrid frog leap algorithm; and S6, optimal seed set output. The application forms a synergistic mechanism in three key links of propagation mechanism modeling, candidate node screening and influence estimation, significantly improves the calculation efficiency under the premise of guaranteeing the influence propagation effect. Through experiments on multiple typical network data sets, the method of the application shows performance advantages superior to existing methods in influence diffusion capacity and running efficiency, and provides a feasible solution for efficient seed selection problems in large-scale social networks.

The application discloses a fault repair method and device, equipment, a storage medium and a program product, and relates to the technical field of software operation and maintenance. The method comprises the following steps: determining a plurality of first candidate paths matched with a current fault event from a decision graph, wherein the decision graph is a knowledge graph obtained by fusing an influence propagation graph and an expert knowledge graph, and the influence propagation graph is a knowledge graph generated according to pre-collected historical multi-modal data; determining a comprehensive confidence of each first candidate path according to a path confidence, a path causal weight and a safety coefficient of the first candidate path; determining a first target path corresponding to the highest comprehensive confidence; and if the comprehensive confidence of the first target path is greater than or equal to a first comprehensive confidence threshold, performing a first target treatment action represented by a treatment action node of the first target path. The method improves the fault repair efficiency and the fault repair accuracy.

The invention discloses a competitive overlapping community influence maximization method, which comprises the following steps of: constructing a competitive independent cascade expansion model, introducing an inter-node activation probability calculation mechanism, and comprehensively considering node similarity, interaction strength and user interest preference to realize multi-information competitive propagation modeling; meanwhile, based on an overlapping community structure and reverse reachable set sampling, a node comprehensive influence evaluation method is provided, and high-influence nodes are accurately identified in combination with the number of third-order neighbors and the community overlapping degree; through important community screening and a seed budget allocation strategy, seed node selection is optimized, and the information spreading efficiency is improved; the method is remarkably superior to an existing algorithm on the aspect of a real social network data set, shows excellent performance in the aspects of influence propagation range and calculation efficiency, and is suitable for the fields of social network analysis, information popularization, recommendation systems and the like.

The invention provides an influence propagation method and device between a front end and a rear end, equipment, a medium and a product, and the method comprises the steps: responding to a first modification operation for a rear end code, and updating a pre-constructed influence propagation relation graph; in the updated influence propagation relation graph, positioning a target back-end method node involved in the first modification operation; in the updated influence propagation relation graph, searching all target front-end pages unidirectionally communicated with a starting node by taking the target back-end method node as the starting node; all the target front-end pages are marked, so that a management end executes a test. According to the method, the influence propagation model for modifying the back-end code to the front-end page is constructed, so that the influence caused by the modification of the back-end code can be propagated to the front-end page in time, and the probability of project integration failure caused by the fact that front and back-end developers do not perceive the change and do not successfully test is reduced; developers before and after are promoted to perceive code change and test in time, so that the test efficiency is improved.

The invention provides a dynamic social network-oriented adaptive influence propagation path planning method and system, and the method comprises the steps: carrying out the fusion processing of multi-source heterogeneous social network data collected in real time based on a dynamic graph neural network, and generating a dynamic embedded vector which can represent the structure position, activity and interest preference of a user; constructing a propagation probability prediction model, and combining user dynamic embedding and information content embedding to output a personalized propagation probability; modeling an information propagation process into a sequence decision problem, constructing a reinforcement learning strategy model, and selecting a propagation path according to a current network state and a prediction probability; and updating the propagation probability prediction model and the reinforcement learning strategy model on line according to actual propagation feedback, and performing dynamic adaptive adjustment on a subsequent propagation path. According to the method, the accuracy and the dynamic adaptability of influence propagation path planning are improved, the range and the efficiency of information diffusion are improved, and a better propagation effect can be played in a real and complex social network environment.

The application provides a kind of electric drive system multidisciplinary collaborative development process management and control method and system, method includes establishing the unified associated data model with product bill of materials as core;At least one key technical index is defined for project milestone, and index information is configured for each key technical index;Based on the unified associated data model and through the rule engine, the current value of the key technical index is calculated from the simulation and test data, and the milestone readiness is generated according to the current value;When any parameter in the unified associated data model is changed, the change content is semantically analyzed and the influence propagation analysis of the dependency graph is triggered;The influence propagation algorithm is executed, the dependency graph is traversed and the influence analysis result is output;At least one mitigation scheme is generated based on the influence analysis result, and an approval flow is integrated to support the selection and execution of mitigation scheme, the application can promote the deep integration of BI and business, improve the efficiency and decision quality of research and development.

The invention relates to the technical field of graph processing, in particular to a high-influence information communicator identification method based on circle graph random walk. According to the method, the user relation network and the circle relation network are constructed, the circle graph is formed, and the node importance score is calculated in combination with multi-layer random walk, so that high-influence information propagators can be effectively identified. The method has the advantages that the user relation and the circle structure are deeply fused, the node propagation capacity is analyzed from the perspective of a multi-dimensional network, compared with a traditional single network model, the method can better capture the cooperative effect between the complex path of information propagation and the nodes in a social scene, and the accuracy and comprehensiveness of high-influence propagation node recognition are improved.

The invention discloses a dynamic NPRank algorithm based on authentication characteristics of multiple social platforms and a method for evaluating the influence of social network users by using the dynamic NPRank algorithm. In order to solve the problems of single influence evaluation dimension and insufficient algorithm precision in the prior art, the invention provides a dynamic NPRank algorithm based on multiple social platform authentication features, and on the basis of a PeopleRank algorithm, a damping factor is adjusted according to whether a user passes platform authentication or not so as to distinguish influence propagation weights of different types of users; the invention further provides a method for evaluating the influence of the social network users according to an NPRank algorithm. The method comprises the following steps: collecting public data of a target user from a social network platform; establishing an attention relation probability model; constructing an adjacent matrix of the user relationship; the influence is calculated in parallel; and fusing calculation results to obtain a comprehensive influence score. The method is suitable for social data analysis fields such as brand marketing, public opinion monitoring, content propagation research and key opinion screening.

A method for maximizing the influence in a social network based on the number of hops and the intimacy of local relationships, which relates to the field of social network technology. The influence propagation range of seed nodes is restricted within 2 hops, fully considering the finiteness of the propagation range of the social network. Moreover, this method introduces the intimacy of local relationships to measure the influence intensity of nodes. Compared with some classic greedy and heuristic methods, this method has a higher accuracy in evaluating the influence of nodes. Not only that, when selecting seed nodes, this method uses the influence coverage gain of nodes as the influence coverage, which can effectively avoid the rich club effect.

Method, storage medium and apparatus associated with content preparation and / or selection are disclosed herewith. In embodiments, an apparatus may include a content preparation or selection module to receive, as part of a content preparation or selection, a representation of a concept; and determine, for the content preparation or selection, topical relevance of a content item (N) of a content hierarchy to the concept. The content hierarchy may include a plurality of content items, including N, and determining the topical relevance of N to the concept includes determination of propagation of influence among content items of the content hierarchy subject to constraints that bound the number of operations performed by the computing system to determine the propagation of influence among content items of the content hierarchy with a chosen constant. Other embodiments may be disclosed and / or claimed.

The present invention discloses a method for maximizing the influence of a heterogeneous network based on meta-path semantic perception, comprising the following steps: constructing a heterogeneous network graph based on heterogeneous network data; obtaining the meta-path of the heterogeneous network graph, simplifying and integrating it, and generating a valid path set; setting node path weights based on a specific heterogeneous network graph, determining the node sampling probability, and constructing a reverse sampling strategy; adopting the reverse sampling strategy to sample the heterogeneous network graph, and generating a valid reverse reachable set in combination with the determination of the valid path; based on the valid reverse reachable set, calculating the node marginal influence propagation gain, iteratively screening with the marginal gain as an indicator, obtaining a target seed set, and achieving influence maximization. The present invention constructs a reverse sampling strategy, combines the node semantics and path semantics in the heterogeneous network in the reverse sampling process, takes semantic perception into account, and helps to more accurately identify and select key nodes, thereby achieving the maximization of the influence of the heterogeneous network.

The invention discloses a source-load collaborative carbon reduction method and system considering user social association, and the method comprises the steps: firstly building a source-load collaborative carbon reduction model, the model is at least based on consideration of a source side and a load side, the source side considers wind-light uncertainty and a carbon transaction mechanism, and the load side considers carbon emission responsibility visual allocation and low-carbon demand response; in low-carbon demand response of a load side, a same-group effect principle in social psychology is combined with a demand response context, a load-side same-group influence propagation model is proposed to quantify social relevance of users, and the same-group influence is considered as one of influence factors for user behavior decision making. And constructing a user response model considering social association by using a linear regression method. And finally, using a maximum likelihood estimation technology to learn user response model parameters online so as to cope with the randomness of user response behaviors. According to the method, the social network is utilized to improve the source-load collaborative carbon reduction efficiency, the bilateral carbon emission is effectively reduced, and the method has a good application effect.