11 results about "Influence propagation" patented technology

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 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 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 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 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.