327 results about "Information propagation" patented technology

The invention relates to the technical field of social information, in particular to a social network influence prediction method and system. According to the method, brand-related users, content and interaction data are obtained from multiple platforms, a multi-dimensional influence scoring model is constructed, and initial influence scores of the users are calculated. And in combination with a social network cascade propagation theory, constructing a propagation probability calculation model, and predicting an information propagation range and user influence changes. And finally, generating an influence prediction report containing KOL ranking, hot topic analysis and optimal intervention opportunity suggestions. According to the method and system, through organic combination of a plurality of innovation points, the accuracy, comprehensiveness and practicability of prediction are remarkably improved, the existing technical problems are effectively solved, and a powerful support tool is provided for a brand to formulate a precision marketing strategy in a complex social media environment.

The invention discloses a clinical research data analysis method based on machine learning, and the method comprises the steps: constructing a multi-modal variable structured causal map, and building a direction adjustable mechanism of a causal path; constructing a bidirectional nested structure attention mechanism, and capturing a cross-modal dependency and dynamic evolution relationship between variables; recording each layer of information propagation path and variable participation degree, and realizing reverse reconstruction of a model decision path in a reasoning stage; target-oriented attribution path regularization is introduced to carry out regularization constraint on an attribution path set of the key target variables; and constructing a nested attribution graph visualization system, and realizing interactive presentation of interpretation sub-graphs corresponding to prediction results so as to improve cognitive trust of model output. According to the method, from structure expression, path tracing and causal constraint to visual presentation, the core problems that a deep model is poor in interpretability, clinicians are not trusted, and existing interpretation tools are insufficient in applicability are solved in a full-link mode.

The invention relates to a gated multi-graph convolution perception modeling method for traffic flow prediction. The method integrates multi-graph structure construction, gating graph convolution and time feature extraction, and aims to solve the problems of strong time fluctuation and heterogeneous spatial relationship in traffic data. The method comprises the following steps of: firstly, respectively constructing a geographic map and a semantic map according to the maximum mutual information measurement between the spatial distribution information of a sensor and historical traffic data; and then, designing a dual-adaptive gating graph convolution module, and dynamically adjusting an information propagation path of a multi-graph structure by introducing an attention mechanism and a gating factor, thereby improving the modeling performance of the model on spatial isomerism dependence. On the time dimension, a time sequence interactive sensing module is constructed in combination with multi-scale causal convolution and an attention mechanism, time dependence characteristics of a short period and a long period are captured, and fusion and expression of time characteristics are completed. According to the method, the modeling precision and stability of the traffic prediction model in a complex traffic scene can be effectively enhanced, and the method has relatively high practical application value.

The embodiment of the invention provides a computer communication method and system based on the Internet of Things, and the method comprises the steps: constructing a multi-level system architecture, carrying out the preprocessing and feature extraction of an original data flow, recognizing the data characteristics through a time sequence analysis method, and constructing a dynamic data model; deploying a monitoring agent at a transmission node to collect network performance indexes in real time, and constructing a network quality evaluation model; for an incomplete sensor data flow, prior probability distribution is constructed based on a dynamic data model and a network quality grade, and an optimal estimation value of missing data is calculated by adopting a Bayesian reasoning framework and an iterative algorithm; establishing a mapping relation between a network state and an optimal parameter through reinforcement learning to realize self-adaptive adjustment and optimization; and grading the data according to reliability, extracting high-reliability data points as anchor points, designing an iterative refinement algorithm to realize information propagation, and fusing to obtain a complete sensor data stream. According to the method, the problems of poor data recovery accuracy, static parameter configuration and insufficient adaptability in a complex network environment are solved.

The invention relates to the technical field of network information propagation prediction, and discloses a topic propagation prediction method and system based on multi-dimensional feature fusion, and the method comprises the steps: extracting multi-dimensional features, inputting the multi-dimensional features to a decoder layer of Transform for fusion, and constructing a prediction model for topic propagation prediction; the multi-dimensional features comprise user comprehensive influence, text emotion features, time dynamic features and user interaction behaviors. According to the method, through collaborative optimization of the dynamic user modeling module, the multi-modal feature fusion module and the intelligent time sequence analysis module, the social network propagation prediction accuracy is remarkably improved, and accurate prediction of the topic propagation trend in the social network is achieved.

The invention relates to the technical field of artificial intelligence and information propagation analysis, and discloses a deep learning-based network information trend prediction method and system.The deep learning-based network information trend prediction method comprises the steps of performing neural architecture search through combination of an evolutionary algorithm and reinforcement learning; an optimal neural network structure suitable for different types of network information is automatically found; dynamic reconstruction of a model structure is realized through an environment perception and event triggering mechanism; according to a deployment environment resource constraint, adopting an importance-perceived neuron self-adaptive pruning technology; turning point features in network information propagation are specially extracted and enhanced; knowledge migration from a large-scale high-precision model to a lightweight model is realized; an online learning and continuous optimization mechanism is adopted to prevent disastrous forgetting; according to the method, the key turning point of network information propagation can be accurately predicted, the prediction accuracy is improved, the early warning time is shortened, and the computing resource consumption is reduced.

The invention discloses a small target detection network based on sparse feature enhancement fusion. The method comprises the following steps: extracting multi-level features of different scales through a backbone network; the sparse feature enhancement module is used for performing deconvolution operation on shallow features to restore image details to the greatest extent, further improving the detection precision of the small target through feature integration from global to local, and enhancing the distinguishing capability of the small target in a complex background; the gradual gradient enhanced detection head is adopted, and the information transmission mode in the training process is dynamically adjusted, so that the network performance is effectively optimized through programmable information, and the detection effect is improved. The invention provides a small target detection network based on sparse feature enhancement fusion. A sparse feature enhancement module, a hierarchical feature fusion module and a progressive gradient enhancement detection head are provided, and a programmable gradient information mechanism is introduced to improve and optimize the detection head.

The invention relates to an information-oriented network malicious behavior message backtracking generation method, and relates to the technical field of network security, and the method comprises the steps: constructing a network node topology according to a target power grid network; constructing a node anomaly identifier of each network node and carrying out node anomaly detection to obtain a node detection result; when an abnormal node exists, analyzing information guidance from the abnormal node based on an information flow direction, and determining an information propagation path; and based on the information propagation path, reconstructing a complete attack chain of the malicious behavior according to a time sequence, and generating a malicious behavior message backtracking result. According to the method, the problems of inaccurate abnormal node positioning, incomplete information propagation path tracing and disordered attack chain time sequence in traditional network malicious behavior backtracking are solved, and the integrity, accuracy and timeliness of malicious behavior backtracking are improved.

The invention relates to the field of enterprise risk assessment, and discloses an enterprise risk assessment method and system based on a large language model and high-frequency graph convolution, and the method comprises the steps: S1, building an enterprise risk association graph: generating a structured joint weight between enterprises, and building the enterprise risk association graph; fusing the semantic weight of the large language model and the structured joint weight into a final edge weight; jointly inputting the fused risk map and the multi-modal feature into two-stage attention to generate a multi-modal attention feature; and S2, enterprise risk assessment: on the enterprise risk association graph endowed with the edge weight, carrying out information propagation and aggregation by adopting high-frequency graph convolution. According to the method, edge-level gating and fusion edge weight guide risk information to be preferentially diffused along a more credible and more relevant path, high-frequency signals such as financial mutation, negative news and major events can be quickly captured, and blind diffusion of the information in a network is reduced. And the amplification effect of market systematic impact is reduced.

The invention discloses a recommendation method based on semantic enhancement and a heterogeneous hypergraph network. The recommendation method comprises the following steps that semantic information in an explicit feedback text is coded and serves as an auxiliary signal of a recommendation task; classifying the articles into predefined categories by using LLM, constructing article-category association, and mining a potential co-occurrence relationship of the articles; constructing a heterogeneous hypergraph network; spreading and aggregating hypergraph information; performing semantic alignment and model training; and performing recommendation calculation based on the final representation of the user and the representation of the article, and outputting a recommendation result. According to the method, through technical paths of semantic coding, hypergraph modeling, information spreading and alignment supervision, comment semantics of LLM coding are aligned to the recommendation space through GAE, and the problem of degradation of LLM representation in the recommendation space is effectively solved.

A single-target tracking method and device based on a state space model and attention and a medium are characterized in that firstly, hierarchical feature extraction is carried out, hierarchical modeling is carried out on a template and a search area through a mixed attention mechanism and the state space model, and a time sequence token is introduced into each frame at the final stage of hierarchical feature extraction to carry out target information aggregation; and aggregating the time sequence tokens by using a state space model to realize cross-frame information propagation, multiplying the obtained time sequence tokens by corresponding frame feature maps to serve as feature enhancement, and finally predicting a bounding box of a target object by a prediction head to realize target representation. According to the method, the state space model and the attention mechanism are combined for single-target tracking, especially target tracking under a large-resolution video, the state space model is introduced into the field of single-target tracking, the advantage of linear complexity of the state space model is brought into full play, meanwhile, the disadvantage of limited retrieval capacity of the state space model is made up, and high-resolution video tracking is achieved. And finally, the consumption of computing resources is reduced while accurate tracking is realized.

The invention provides an Internet information propagation effect analysis method based on multi-dimensional data, and belongs to the technical field of information. The problem that the information spreading effect and influence cannot be comprehensively evaluated is solved; the method specifically comprises the following steps: S1, collecting Internet propagation information, S2, classifying propagation files according to a propagation region and propagation time, calculating regional propagation force, and constructing a regional propagation force diagram; analyzing the type of the propagation node; constructing a propagation node type graph; s3, analyzing the content of the propagation file, and judging the expression tendency of the propagation file to obtain an expression tendency graph; s4, calculating an influence value of the propagation file, and constructing an influence figure graph; s5, constructing a multi-dimensional analysis graph according to the regional propagation force graph, the propagation node type graph, the expression tendency graph and the influence character graph; according to the method, a plurality of propagation dimensions are displayed, so that a user can intuitively understand and analyze the propagation effect of information.

The invention discloses an intelligent decision-making method and system based on deep learning, and relates to the field of data processing. The method comprises the steps of obtaining place data and corresponding attribute data of a to-be-decided project; establishing a fuzzy relation matrix between the places and the attributes; generating a network model reflecting trust relationship strength among users through trust propagation operation of the graph neural network; identifying a community structure containing a community overlapping degree through a community discovery algorithm; and according to the trust relationship strength between the users and the community overlapping degree, calculating an influence weight of a decision maker, forming a group consensus through a robust optimization method, and generating a decision result of the project to be decided. Aiming at low network relation modeling precision caused by multi-source heterogeneous data in bus station layout decision making in the prior art, the method and the device have the advantages that the network relation modeling precision is low through accurate modeling of an information propagation path in a complex trusted network, effective dimension reduction representation of a high-dimensional feature space and robust optimization solution in an uncertain environment; therefore, the calculation precision and robustness of the bus station layout intelligent decision-making system are improved.

The invention discloses a multi-modal fine-grained semantic alignment method and device based on a graph neural network, and relates to the field of multi-modal deep learning. Firstly, deep feature extraction is performed on input multi-modal original data, and then word-level text features and local image features are constructed into a cross-modal graph structure. And performing weighted aggregation on node neighborhood information of the cross-modal graph structure through the graph attention network. And finally, carrying out weighted fusion on the text alignment features and the image alignment features. According to the cross-modal feature fusion method, the word-level text features and the local image features are uniformly abstracted into the graph structure nodes for refined alignment, a more accurate cross-modal semantic corresponding relation can be captured, the heterogeneity problem in expression modes and semantic structures is effectively relieved, and the accuracy and reliability of cross-modal feature fusion are improved. The graph attention network can adaptively adjust the weight distribution of information propagation, highlights the effect of key features in the alignment process, and ensures that the model makes full use of important semantic relationships.

The invention relates to a solubility prediction method for coupling a directional message passing neural network (DMPNN) and a hybrid expert (MoE) model. According to the method, a molecular structure is represented as a graph, information propagation and feature extraction are carried out by using DMPNN, and the prediction capability of a model is enhanced in combination with MoE. Compared with an existing solubility prediction method, the solubility prediction method has the advantages that accurate prediction can be carried out under various solute-solvent systems and different temperature conditions, the application range of solubility prediction is expanded, and the solubility prediction method has high generalization ability and accuracy and is widely applied to the fields of medicine research and development, new material design and the like.

The invention discloses an intelligent community service management system based on big data, relates to the technical field of community service management, and realizes breakthrough improvement through multi-dimensional dynamic perception-self-evolution decision chain design: a cold start unit triggers the highest-level response at the moment of message release based on extreme event marks and geofences, and sends the highest-level response to a server; fatal delay of waiting for heat accumulation in a traditional scheme is thoroughly avoided; the space-time sensitivity quantification module adaptively adjusts the space-time contribution weight through a dynamic trade-off coefficient, and ensures that the night low-activity period does not report mistakenly and the information surge period does not miss; in the disaster evolution graph, a space-time attenuation model of node urgency degree coupling replaces an artificial rule, and a disaster diffusion path is automatically identified; and the multi-modal verification module calls an official disaster situation API and image recognition to block a false information propagation chain.

The invention relates to a graph convolution multi-modal dialogue emotion recognition method based on emotion dimension compensation. The method belongs to the field of multi-modal emotion recognition. Comprising the following steps: respectively extracting original features of texts, audios and visual modalities by utilizing a pre-training model; detecting a mode missing condition, and generating a pseudo feature by using an available mode feature and context information; the multi-modal features are mapped to a VAD three-dimensional space, and prediction and enhancement are carried out; the global level is based on VAD similarity to connect cross-utterance nodes, and emotional consistency is quantified to capture long-distance interaction. In the local level, information spreading and node updating are carried out by calculating the similarity between different modal features in the same utterance; neighbor information is accumulated through multiple layers of image volumes, node weights are dynamically adjusted to suppress noise propagation, and finally utterance-level emotional representation is generated by fusing multi-modal features through mean pooling operation. According to the method, the emotion recognition performance in a modal missing scene is remarkably improved.

The invention discloses an efficient graph comparison learning method for social recommendation. As an emerging self-supervised learning normal form, graph contrast learning is excellent in response to data sparseness and cold start due to the fact that the graph contrast learning can effectively capture similarity and heterogeneity characteristics in a graph structure, although the learning normal form achieves a good effect in a recommendation system, the graph contrast learning can be used for solving the problems of data sparseness and cold start. However, the method still faces three defects: (1) average neighbor aggregation and a non-adaptive representation reading mechanism are adopted in a message propagation process, and high-quality node representation is difficult to learn; (2) a visual angle is enhanced by depending on a random disturbance generation graph during intervention of comparative learning, which may destroy the inherent structure of graph data and further weaken the accuracy of the model; and (3) equally treating all observation samples during parameter optimization, and neglecting the difference influence of positive samples in different training stages. Specifically, aiming at the problems, the invention provides an efficient graph contrast learning method (EGCL for short). The method comprises the following steps: firstly, designing a graph adaptive propagation module, improving an information propagation rule of a graph neural network by referring to a thermonuclear thought and an attention mechanism, and realizing differentiated aggregation of neighbor nodes by adopting a learnable weight distribution strategy; secondly, designing a double contrast learning normal form which does not need graph enhancement, and realizing mutual promotion of node characterization through intra-domain contrast learning (inter-CL) and inter-domain contrast learning (inter-CL); and finally, introducing a sample weight adaptive efficient optimization algorithm, converting the training process into a double-layer optimization problem, and adaptively adjusting the contribution degree of each sample to model optimization in different stages.

The invention relates to the field of influence measurement, and discloses a social media-based content influence measurement method, which comprises the following steps of: constructing a social network graph according to collected user social relation data; each user is a node, interaction between the users is an edge, and features in the social network are analyzed; modeling a propagation path of the information by adopting a propagation model, and weighting the weight of the information propagation according to the social relationship between the users; establishing a weight model, endowing each edge in the propagation path with different weights according to the strength of different social relationships, and strengthening the influence of the core social group on content propagation; evaluating the propagation breadth and propagation speed of the content in the social network, analyzing the participation behavior of the user, and evaluating the propagation effects of the content in different social circles; and training the data by using a machine learning algorithm, and optimizing the propagation effect prediction model. The method has the advantage of accurately evaluating the actual propagation effect and influence of the content.

The invention relates to a social information networking monitoring and early warning method and system, and relates to the technical field of information early warning, and the method comprises the steps: collecting and analyzing the multi-source information of an information source, and determining the information source weight of the information source; according to feature vectors and feature weights extracted from the multi-source information by the distributed semantic model, obtaining semantic similarity of cross-time semantics; obtaining an event association strength network according to the information source weight and the semantic similarity; according to the distribution characteristics of the depth of each network node in the event association strength network, obtaining a risk propagation index of the influence of the assessment information propagation path on risk amplification; according to the risk propagation index, the information source weight, the semantic similarity and the association strength among the events in the event association strength network, generating an early warning comprehensive index of social information networking monitoring; and according to a preset threshold interval, determining an early warning grade result corresponding to the early warning comprehensive index. According to the invention, the accuracy and timeliness of social information networking monitoring and early warning can be improved.

The invention belongs to the technical field of pipeline safety assessment, and discloses a multi-dimensional information interconnection and autonomous evolution collaborative pipeline full-state safety assessment method. And capturing a high-order relationship of data through double hypergraph reasoning of the instance-level hypergraph and the modal-level hypergraph to realize efficient interconnection. According to the method, mode-level and instance-level hypergraph information features are extracted through hypergraph information propagation, high-order correlation is mined through double-graph information aggregation, cross-mode and cross-instance consistency information and exclusive information are output after feature recombination, multi-dimensional data deep fusion is promoted, and high-quality data support is provided for follow-up pipeline full-state safety assessment. A two-stage autonomous evolution mechanism of intra-class progressive calibration and inter-class knowledge migration is respectively adapted to slight fluctuation and significant change scenes of the deep sea environment: precise adaptation of environment perturbation is realized through dual-branch feature extraction and dynamic weight adjustment in a domain; model parameter dynamic optimization is completed between domains through spatial-temporal feature clustering and cross-domain knowledge migration, and dynamic environment self-adaption can be achieved without manual intervention.

The invention relates to a rumor detection method based on dual-domain perception structure feature fusion learning, and belongs to the technical field of natural language processing and information spreading. The method comprises the following steps: acquiring a social media message event data set, constructing a post propagation network graph and a user social network graph, and extracting post propagation features and user social features; identifying a rumor diffusion key time window by using a propagation density peak value and slope division method, and extracting substructure features; processing the sub-features through a mutual attention mechanism to obtain fusion features; the method comprises the following steps: constructing a projection matrix through fusion features, carrying out weighted summation on the fusion features by adopting the projection matrix to obtain interaction features, further generating first weighted post propagation features and first weighted user social features, carrying out homogeneous interaction information modeling to obtain a final homogeneous interaction comprehensive representation, splicing the final homogeneous interaction comprehensive representation with original tweet features, and carrying out classification. And obtaining a rumor detection result. The rumor detection precision and robustness can be improved.

The invention relates to the technical field of epilepsy detection, in particular to a self-supervised graph neural network epilepsy detection method based on Transform. The method comprises the following steps: S1, preprocessing original EEG data, and constructing an EEG graph; s2, building a graph neural network based on DCTran, and respectively capturing a space-time dependency relationship of the EEG signal through diffusion convolution and a Transform structure; and S3, training the model through spatio-temporal joint complementary double-branch pre-training based on the self-supervised prediction pre-training task and the mask reconstruction pre-training task. According to the epilepsy detection method based on the self-supervised graph neural network of the Transform, provided by the invention, EEG data is modeled into a graph structure, and a diffusion convolution space-time network DCTran based on the Transform is provided; the diffusion convolution accurately captures a complex spatial relationship between the electrodes through multi-order neighbor information propagation; and meanwhile, global context information of the EEG signal is fully utilized, and DCTran is introduced into a Transform structure to model a time sequence, so that the long-distance time dependency relationship in the signal is effectively captured.

The invention provides a multi-mode sentiment analysis method and device based on sentiment dynamic tracking. The method and device can be used for automatic quality inspection of call scenes in the electric power customer service industry. According to the method, a multi-mode sentiment analysis technology is adopted, the defect of a single mode is overcome, sentiment features in voice and sentiment information in a text are fused through a graph neural network (GNN), and a graph structure effectively integrates internal relations and interaction among different modes by means of a unique topological structure and a dynamic information spreading mechanism. In order to fit a customer service-customer multi-round dialogue scene, an emotion dynamic tracking module is added, splicing features between utterance pairs are constructed, the module can effectively and dynamically track changes of customer emotional states, and even if the customer emotional states suddenly change, flexible recognition can be achieved. The module not only deepens the understanding of the model on the mood dynamics, but also restrains the network parameters of the main task through a strategy similar to comparative learning.

The invention provides a low-code and component intelligent collaborative application construction method based on a dual-drive architecture, and belongs to the technical field of computer programming. The method comprises the following steps: establishing a component dependency digraph, optimizing a component collaborative relationship by adopting topological sorting, graph coloring and a minimum spanning tree algorithm, collecting multi-dimensional performance indexes to establish a monitoring matrix, processing monitoring data by utilizing a collaborative optimization model containing a gating information propagation mechanism, and generating a dynamic scheduling decision vector; the load change of the system is judged through a change rate threshold equation, quick or slow response adjustment is executed, and finally, a low-code component library and a visual configuration interface are established to realize intelligent collaboration and dynamic resource allocation among components. The technical problems that the collaboration efficiency among low-code platform components is low and intelligent dynamic scheduling cannot be achieved according to system load changes are solved.

The invention discloses a complex multi-step network attack detection method based on spatio-temporal fusion features, which comprises a model training stage and a multi-step attack detection stage, and is characterized in that spatio-temporal features of complex multi-step attacks are comprehensively analyzed by combining a graph neural network, time weights and LSTM (Long Short Term Memory); and complex associations among and in the multi-step attack chains can be effectively captured. A flow graph structure is constructed through quintuple fingerprints, node attributes are fused with protocol types, timestamps and load characteristics, and a designed edge generation algorithm can effectively represent time-space correlation characteristics of multi-step attacks. An adjacent matrix updating algorithm with a time attenuation characteristic is designed, information propagation intensity between nodes is dynamically adjusted through a configurable attenuation factor, and interference of time sequence confusion noise on attack chain division is effectively suppressed. A three-layer composite network structure is designed, a'space-global-time 'progressive feature learning path is formed, and the problem of division of a space-time overlapped multi-step attack chain is solved.

The invention relates to the technical field of thermal power plant abnormal data processing, in particular to a thermal power plant production monitoring system network security data set abnormal data identification and cleaning method, which comprises the following steps: mapping a spatial-temporal feature vector to a preset thermal power plant multi-level causal graph to obtain a data causal graph; performing information propagation and node updating on the data causal graph, and calculating an abnormal score of each node in a preset thermal power plant multilevel causal graph; identifying abnormal data according to the abnormal score and the network security data set; and cleaning the abnormal data according to the abnormal type and a preset data cleaning strategy. According to the method, correlation modeling of data on time and a system topological structure is realized through a multi-level cause and effect graph; an abnormal score is calculated through information spreading and node updating, and an abnormal event in the network security data can be accurately identified; the abnormal data is cleaned based on the preset data cleaning strategy, redundant, wrong and abnormal data can be effectively removed, and meanwhile key risk information is reserved.

The invention relates to the field of network public opinion management and control, in particular to a cross-modal network emergency command system based on a network space model. The method comprises the following steps: acquiring a network public opinion multi-modal data stream, positioning a malicious public opinion propagation source account based on the network public opinion multi-modal data stream, and performing network space cross-layer holographic portraying based on the malicious public opinion propagation source account to generate a source account holographic portraying information set; on the basis, multi-modal feature extraction and network space behavior model construction are carried out on hidden negative guidance behaviors, and an implicit negative information propagation mode model is generated; on the basis, potential malicious account active tracking and risk propagation network mining are carried out, and a potential risk propagation network information set is generated; on the basis, multi-level differential intervention strategies are generated and executed, and a network public opinion intervention strategy instruction set is generated. In the network public opinion emergency command process, pollution of false information to network space is reduced, and construction of healthy and sustainable network ecology is assisted.

The invention discloses a method for maximizing the influence of information competition propagation in a social network, and relates to the technical field of information propagation. Comprising the following steps: constructing an extended independent cascade model; aiming at each initial information seed node, constructing an influence subgraph taking the initial seed information node as a root node; constructing an alternative target node set by taking the initial information seed node as a starting node; according to the frequency of occurrence of the root node and the non-root node in the influence subgraph of the originating node, obtaining a contribution degree; selecting an initial information seed node having the maximum influence gain on adjacent nodes in the three-hop range from the alternative node set to obtain a plurality of intermediate information seed nodes, and determining the comprehensive influence of each intermediate information seed node; and arranging the comprehensive influence of the intermediate information seed node in a descending order, and selecting a final information seed node. According to the method, the target node in the competitive environment can be accurately identified, and the propagation of competitors is restrained while the information of the target node is maximized.

The invention relates to the technical field of social network information propagation prediction, in particular to an event propagation prediction method based on group influence. The method comprises the following steps: constructing a social network graph of target event participating users, calculating an activation probability matrix among the users by using historical propagation data, and performing social graph structure enhancement based on a threshold value to obtain a de-noised social graph; applying a graph neural network on the de-noised social graph and introducing information bottleneck constrained self-supervised contrast learning optimization node embedding, and reserving features useful for group division and propagation prediction; dynamically dividing user groups related to propagation according to the similarity and propagation context of de-noising node embedding to form a group-level propagation sequence; based on the group representation, utilizing an attention mechanism and a group relation graph to carry out modeling on propagation influence among the groups to obtain global group representation of the event; global group representation is combined with candidate user embedding, propagation probability distribution is calculated, and future participating users of corresponding events are predicted.