51 results about "Pagerank algorithm" patented technology

The invention discloses a knowledge question-answering method and system based on topic knowledge graph retrieval enhancement, and the method comprises the steps: firstly extracting a local topic represented in a triple form based on an original document through employing a large language model, carrying out the clustering, and generating a global topic triple set representing the global perspective of the whole document; secondly, on the basis of the global topic triple set, topic-guided entity and relation extraction is adopted, and a mixed knowledge graph is constructed; secondly, providing a semantic perception personalized PageRank algorithm, matching query semantics with semantics of edges in the mixed knowledge graph, and dynamically adjusting the weight of score propagation between nodes; and finally, designing a three-level progressive retrieval mechanism, retrieving multi-level information related to user query from the mixed knowledge graph, and inputting the multi-level information into the large language model to generate a final answer. According to the method, the semantic integrity and retrieval precision of the knowledge graph are remarkably improved, and the accuracy, comprehensiveness and enabling performance of generated answers are ensured.

The invention discloses an intelligent reasoning knowledge base construction method based on a knowledge graph, and relates to the technical field of government affair information processing and intelligent reasoning. Accurate extraction of strategy elements and formalized expression of logic relations are realized by constructing a structured strategy clause knowledge graph; a reliable data basis is provided for conflict detection; a method of combining a self-adaptive threshold mechanism and constraint satisfiability solution is adopted, so that the accuracy of conflict detection is ensured, and the method can adapt to the language characteristics and time evolution characteristics of a strategy text; clear decision support is provided for a strategy making department by establishing a complete conflict report generation mechanism; the timeliness and the consistency of the knowledge base are ensured by an incremental graph updating and version management function, and the department cooperation capability is further enhanced by the construction of the government affair responsibility sub-graph; the application of the multi-task learning model improves the intellectualization level of demand processing, and the personalized PageRank algorithm realizes accurate department recommendation.

The invention discloses a lateral movement detection method and device for deploying a time sequence perception graph neural network based on edge optimization, and the method comprises the steps: S1, capturing a host authentication log in real time through a log collection agent deployed on edge equipment, carrying out the event filtering and key field extraction through a rule matching engine, and forming a standardized entity data set; s2, designing an authentication graph structure by adopting an ontology graph modeling language, and definitely defining node types of a user, a host, a process and the like and a time sequence edge type containing a timestamp and an authentication type attribute; s3, mapping the entities into heterogeneous nodes based on a dynamic graph modeling technology, and constructing a complete transverse movement heterogeneous authentication graph through weighted time sequence edges; s4, importing the constructed authentication graph into a lightweight graph database Neo4j Embedded, and realizing dynamic topology maintenance by adopting an incremental PageRank algorithm; s5, defining each identity verification event as a multi-tuple structure, designing a time perception sub-graph generator, performing optimized and accelerated local neighborhood sampling by setting a time window, and generating a time sequence sub-graph sample required by training; s6, constructing a multi-scale attention coding framework, and integrating a local graph attention network and a global Transform encoder to realize multi-level feature fusion; and S7, aiming at the resource limitation of the edge equipment, implementing model cutting, a hierarchical quantification strategy and a streaming sub-graph loading mechanism, and ensuring the efficient operation of the system in a resource-limited environment.

The invention relates to the technical field of data retrieval, in particular to a meat detection data management system and method, and the system comprises a data model dynamic adjustment module, a data relation mapping module, a database architecture evolution module, a data merging optimization module, a history tracking and analysis module, a data integrity recovery module and a system performance optimization module. According to the method, the decision tree algorithm and the Bayesian network play a key role in dynamic adjustment of the data model, the optimal data model structure is effectively recognized, the graph database technology and the PageRank algorithm are used for constructing and optimizing the data relation network graph in data relation mapping, the effectiveness of the data storage structure is enhanced, and the data storage efficiency is improved. The clustering analysis method and the K-means algorithm improve the efficiency of data processing, the data recovery algorithm and the transaction log analysis technology ensure the accuracy and integrity of data in data integrity recovery, and the overall performance and stability of the system are remarkably improved through reasonable resource allocation and load balancing of the system monitoring and tuning strategy.

The application discloses a power grid power transmission section identification method based on an improved PageRank algorithm and network conversion, first, an improved PageRank algorithm is used to calculate a node criticality index, and a network constraint coefficient is calculated according to a network topological structure and is used to correct a restart vector; second, a key power transmission section search topological graph is constructed through network conversion and node criticality index calculation, and identification of the key power transmission section is converted into search of a shortest path; finally, according to energy transaction between a generator node and a load node, a search range of the key power transmission section is set, a related cut set with the highest criticality is found, and through checking and correction, an identification result of the key power transmission section is obtained. The application can well improve the deficiency of the original PageRank algorithm when applied to the power grid, can accurately identify the power transmission section of the power grid, provides a key monitoring guide for power grid monitoring and dispatch personnel, and better guarantees the safe and stable operation of the power grid.

The application relates to the computer technology field, in particular to a processing method and device of an open source component, electronic equipment and a storage medium. The method comprises the following steps: receiving an open source project to be processed; the open source project comprises a plurality of open source components; core developer determination processing is performed on the plurality of open source components, and a core developer corresponding to each open source component is determined; based on a preset webpage ranking Pagerank algorithm and the core developer corresponding to each open source component, analysis component centrality determination processing is performed on each open source component, and a target core open source component is obtained; the analysis component centrality determination processing is used for comprehensively scoring the core developer corresponding to the open source component and a dependency relationship, and the target core open source component meeting a condition in comprehensive scoring is screened out. In this way, through refined analysis processing on the core developer in each open source component and the dependency relationship, the target core open source component of the open source project can be accurately determined.

This invention discloses a PageRank-based agent discovery method and system. The method involves initializing the agent discovery system's agent list, comprehensive scoring weight parameters, and system configuration; associating basic information with agent skill information to form a skill candidate list; calling an API to generate semantic vectors for candidate skills and task descriptions in the skill candidate list through semantic pre-screening, calculating cosine similarity as the basic matching degree, adding a matching bonus score to the basic matching degree to obtain a semantic enhancement score, and sorting the semantic enhancement scores; calculating and weighting the PageRank score, call frequency score, call success rate score, and recent activity score; sorting the scores and returning the top M results to the user. The method models the agent's historical call records as a weighted directed graph and uses the PageRank algorithm to quantify the global importance of nodes, transforming the retrieval process into a graph traversal guided by a "importance-relevance" dual-factor approach, significantly reducing the inclusion rate of irrelevant results.

The invention discloses a knowledge graph semantic enhancement generation method based on a large language model, and the method comprises the steps: evaluating the importance of an entity in a knowledge graph, calculating the structural importance of the entity in the knowledge graph based on a PageRank algorithm, extracting and capturing the feature of the entity and a complex semantic relation through the semantic importance, taking a PageRank value as an additional feature, and carrying out the semantic enhancement generation of the knowledge graph based on the PageRank value. Fusing with a graph attention mechanism to evaluate entity importance; dividing the entities into two levels according to an entity importance evaluation result and a knowledge graph long-tail distribution feature; carrying out multi-hop entity expansion and K-neighbor relation search on the entities of the important hierarchy to retrieve and obtain entity associated sub-graph information, and carrying out sub-graph simplification optimization structure overhead on the retrieved important entity sub-graph information; different Prompt templates are adopted for entities of different important levels to generate differentiated entity description content. According to the method, the entity description generation cost is reduced, and the problem that the entity of the knowledge graph lacks semantic description information is solved.

The invention relates to an online community creator feedback prediction method and system based on a dynamic reputation graph and text analysis. Relates to the technical field of feedback prediction. The method comprises the following steps: S1, acquiring and preprocessing historical interaction data containing comment texts, user IDs, timestamps and like and treading records; s2, constructing a like and treading double-view interaction map; s3, segmenting the data according to the day, and calculating a daily positive and negative reputation value through a PageRank algorithm; s4, introducing a time decay function, and carrying out weighted summation on the daily granularity reputation value in the close time window to obtain a dynamic reputation; s5, text semantic features are extracted through RoBERTa, the text semantic features and the dynamic reputation features are spliced and fused, and the full-connection neural network is input to output the like or treading probability. The method gives consideration to positive and negative reputation and time dynamics, makes up for the defects of plain text prediction, remarkably improves the feedback prediction precision, especially optimizes the click behavior prediction effect, and can provide support for community atmosphere guidance and network violent early warning.

The application relates to a knowledge graph question answering method combining a large model and a graph neural network, comprising a question decomposition module, a sub-question solving module and an answer reasoning module, wherein an iterative question decomposition method is proposed in the question decomposition module to solve the difficulty that the size of a subgraph in a multi-hop question cannot be determined by using a graph neural network method, the sub-question solving module is composed of a subgraph retrieval stage and a reasoning path construction stage, a similarity definition method is given in the subgraph retrieval stage, and a Personalized PageRank algorithm is used for calculation, and in order to improve the accuracy of GNN answers, the subgraph of a subsequent question is combined with the subgraph of a previous question to realize dynamic expansion of the subgraph. In the reasoning path construction stage, a candidate answer is obtained by using a pre-trained graph neural network, and a BFS algorithm is used to construct a reasoning path. Finally, the rationality of the reasoning path is judged by using the answer reasoning module to give a sub-question answer. The application provides a knowledge graph question answering framework combining a large model and a graph neural network, and effectively improves the performance of the model in a multi-hop question.

The invention provides an operation activity multi-account cheating identification method and system based on graph propagation. The method comprises the steps that a client side collects user behavior data and reports the data, a server side constructs a heterogeneous user relation graph based on the data and gives a cheating risk weight to an edge, the cheating probability is calculated by using a PageRank algorithm based on a seed blacklist user and is subjected to hierarchical disposal, and the client side dynamically adjusts operation activity interaction logic according to a hierarchical result; according to the system, the method is realized through cooperation of the client SDK module, the server graph construction and propagation module, the hierarchical decision module and the client anti-cheating execution module. According to the method, the defects that an existing supervision model is high in labeling cost, large in recognition delay and incapable of achieving front-end real-time intervention are effectively overcome, and the method is scientific, efficient and good in transverse transplantation capacity.

The invention discloses a self-adaptive multi-modal entity disambiguation method and system for enterprise big data, and relates to the technical field of enterprise big data analysis. The method comprises the following steps: constructing an influence graph by extracting stock right relation data and guarantee relation data, and screening core nodes by adopting a PageRank algorithm; adaptively adjusting the influence propagation weight based on the relation strength to obtain node score distribution; recognizing a fracture relationship in combination with historical name change, and repairing the atlas through multi-modal feature similarity; conflict intensity distribution is generated by comparing node score changes before and after restoration, and time sequence optimization is carried out on attenuation parameters in combination with enterprise business transformation events; and finally, enterprise entity identity determination is completed based on the optimized conflict intensity distribution. The entity disambiguation accuracy and stability in a complex enterprise network can be improved.

The invention discloses a power supply chain collaborative optimization method and device based on a knowledge graph, and a storage medium. The method comprises the following steps: performing knowledge extraction on heterogeneous data of a whole link of a power supply chain to obtain a first entity in a power field and a relationship between the entities; performing entity alignment on the first entity to obtain an aligned second entity; constructing a power supply chain knowledge graph based on the first entity, the relationship between the entities and the second entity; constructing a collaborative scheduling model; based on the power supply chain knowledge graph, calculating an equipment risk value of each equipment by using an improved PageRank algorithm; based on the power supply chain knowledge graph, performing output prediction by using a graph attention network model to obtain an output prediction result of an output node in the target area; and solving the collaborative scheduling model based on the target area, the target demand, the output prediction result and the equipment risk value to obtain a target scheduling decision. According to the invention, the real-time data intercommunication of the power supply chain is realized, and the collaborative optimization efficiency is improved.

The invention discloses a power system key hub identification method based on degree centrality and density. The method comprises the following steps: S1, defining and calculating a degree centrality index based on the number of branches directly connected with nodes; s2, based on the degree centrality indexes, performing static structure inspection on the nodes, and screening out the nodes of which the degree centrality indexes meet a preset reliability standard; s3, weighting parameters are set based on node voltage levels, and weighting processing is carried out on the degree centrality indexes; s4, evaluating the degree that the node is referenced by other nodes by adopting a PageRank algorithm; and S5, fusing the weighted degree centrality index and the PageRank value to generate a comprehensive key index, sorting all candidate key nodes, and outputting a key hub identification result. According to the method, the comprehensiveness and accuracy of key hub node identification are remarkably improved, and a comprehensive and accurate decision basis is provided for differential prevention and control and planning of a power grid.

The invention discloses a historical painter dynamic relation network calculation method for historical learning, which comprises the following steps: firstly, obtaining multi-source ancient literature basic data and event information, realizing data enhancement and event expansion through a large language model, filling a data gap and removing repeated data; constructing a basic relationship strength model (BaseWave) by adopting Beta distribution, constructing an event pulse model (EventPulse) by adopting a Gaussian pulse function, and determining the initial influence of the figure in combination with an extended dynamic PageRank algorithm; splitting the positive and negative relation intensities, carrying out vectorization calculation, and describing the dynamic propagation intensity of the social relation through an adjacent matrix; and finally, outputting dynamic network data including relationship evolution, influence change and culture carrier association. According to the method, the crossing of the relation strength from a static label to a spatio-temporal dynamic curve is realized, the social link effect of a short-term culture interaction effect and a culture carrier is quantified, and a scientific and efficient quantitative analysis tool is provided for history learning and literature creation.

The invention provides a community recommendation method based on interest induction propagation. According to the method, the accuracy and reliability of group recommendation are improved. The method comprises the following steps: firstly, carrying out preprocessing, interest intensity calculation and interest stability calculation on interest tags of community users, and then carrying out social behavior analysis; thirdly, calculating the self-sensing trust degree and mutual sensing trust degree of the user, obtaining the interest sensing trust degree through fusion calculation, building a trust degree network according to the interest sensing trust degree, calculating the interest influence, simulating the influence of behavior interaction in the community on the interest of the user by utilizing a sorting algorithm, and in the trust degree network, calculating the interest sensing trust degree of the user; and simulating a transmission process of interest influence by adopting a PageRank algorithm, calculating group interestingness in the community, and realizing community information recommendation through the group interestingness. According to the method, individual selection and group influence factors of community user interest decision making are innovatively combined, and the accuracy and reliability of group recommendation are remarkably improved.

The invention discloses a power network-communication network coupled node state evaluation method and system and a storage medium, which consider the topological relation between nodes in the network, realize the topological importance calculation of power and communication nodes by constructing an internal weighted incidence matrix and combining with an improved PageRank algorithm, and provide an effective basis for subsequent node state evaluation. According to the method, the coupling relation between a power network and a communication network is considered, the cross-network coupling relation is captured by constructing a cross-network coupling matrix and iteratively updating matrix elements in combination with the node states of the two networks, and the vacancy of a traditional method in coupling evaluation of the node states is made up. And calculating node state scores of the power network and the communication network by combining a multi-dimensional quantization and graph convolutional neural network method, and finally screening out weak nodes of the network. Network node state evaluation can be realized by integrating multi-dimensional features, and the accuracy and reliability of node state evaluation are improved.

The application discloses a kind of intelligent reasoning knowledge base construction method based on knowledge graph, it is related to government affair information processing and intelligent reasoning technical field, the present application realizes the accurate extraction of policy element and the formalized representation of logical relationship by constructing structured policy clause knowledge graph, provides reliable data basis for conflict detection;Adopt the method that adaptive threshold mechanism and constraint satisfiability solution are combined, both ensure the accuracy of conflict detection, and can adapt to the language characteristics and time evolution characteristics of policy text;By establishing complete conflict report generation mechanism, clear decision support is provided for policy making department;Incremental graph updating and version management function ensure the timeliness and consistency of knowledge base, and the construction of government affair responsibility sub-graph further enhances department cooperation capability;The application of multi-task learning model improves the intelligent level of appeal processing, and personalized PageRank algorithm realizes accurate department recommendation.

The application discloses a power grid-communication network coupling node state evaluation method and system, and a storage medium. The topological relationship between the nodes in the network is considered, an internal weighted correlation matrix is constructed, and an improved PageRank algorithm is combined to realize the calculation of the topological importance of the power and communication nodes, thereby providing an effective basis for subsequent node state evaluation. The coupling relationship between the power grid and the communication network is considered, a cross-network coupling matrix is constructed, and the matrix elements are iteratively updated in combination with the node state of the two networks, thereby realizing the capture of the cross-network coupling relationship and making up for the vacancy of the traditional method in the coupling evaluation of the node state. The multi-dimensional quantization and the graph convolutional neural network method are combined to calculate the node state score of the power grid and the communication network, and finally the weak nodes of the network are screened out. The multi-dimensional features can be comprehensively used to realize the network node state evaluation, and the accuracy and reliability of the node state evaluation are improved.

The invention discloses a dynamic space-time population migration network aggregation analysis method based on big data. The method comprises the following steps: step 1, constructing a multi-dimensional space-time data set, 2, expanding a traditional PageRank algorithm into a spatialization PageRank model, and synchronously executing the steps when a node PR value is calculated; 3, adopting a hierarchical network modeling technology to carry out modeling on the PageRank model according to the PageRank model; 4, optimizing a distance attenuation coefficient k through reinforcement learning, establishing a feedback mechanism, and carrying out result verification and analysis; and 5, outputting a three-dimensional aggregation thermodynamic diagram with a space-time identifier, and labeling a core migration path and a bottleneck region. The method has the advantages that dynamic space-time modeling is carried out, on the basis of existing static analysis, a spatialization time decay function, a real-time traffic distance and an economic bearing capacity factor are newly added, and the modeling problem of migration mode time-varying characteristics is solved. And result comparison is performed in combination with a density zone quotient (LQ), so that the credibility of an analysis result is enhanced. Through core layer / edge layer division and an LSTM prediction algorithm, the robustness of sparse data is improved, and compared with the prior art, the accuracy is improved by more than 20%.

The invention discloses an intelligent quality control method suitable for a laboratory, and relates to the technical field of laboratory management, and the method comprises the steps: obtaining and storing the historical quality control management data of the laboratory; analyzing and calculating an experiment quality influence value of each quality control parameter; collecting environment and equipment monitoring data of a laboratory in real time; performing comparative analysis to obtain a current quality control level value of each quality control parameter; judging whether quality management measure evaluation is carried out or not; when quality management measure evaluation needs to be carried out, evaluating a level improvement index of the quality management measure for the experiment quality in the management measure database; an optimal laboratory quality management measure is comprehensively decided; and outputting and executing the optimal quality management measure. The method has the advantages that by constructing the quality control parameter association map and applying the improved PageRank algorithm, the accurate quantitative evaluation of the laboratory quality risk is realized, and the transformation of the intelligent quality control of the laboratory from post-remedy to pre-precaution is realized.

The present application relates to the technical field of wisdom emergency management, knowledge graph analysis and algorithm fusion, and particularly relates to an emergency knowledge graph construction method based on bidirectional empowerment of PageRank algorithm and Louvain algorithm. The method constructs an "entity-relation-entity" triple graph, calculates the global importance score of the entity by the PageRank algorithm, converts it into the Louvain algorithm weight to divide the emergency theme community, and outputs the "community-entity-priority" three-dimensional result through bidirectional empowerment optimization. The method innovatively realizes bidirectional empowerment of the two algorithms, solves the problem of insufficient adaptability of a single algorithm and disconnection between the graph and application, improves the accuracy of entity analysis, supports intelligent generation of emergency disposal reports, and greatly improves the efficiency of emergency disposal and the scientific nature of decision-making.

The invention provides an anti-network asset affiliation association analysis method based on node data perturbation, and belongs to the technical field of network space surveying and mapping. The method comprises the steps of performing data cleaning on original crawling data, judging landmark nodes in combination with a large language model, and constructing a knowledge graph; on the basis of input network assets needing to be protected, complete path information from the assets to organization nodes is found through a depth-first search algorithm; carrying out score calculation and ranking on the extracted paths and nodes based on a pagerank algorithm so as to find key nodes on the paths; and applying webpage mirroring based on transparent reverse proxy, mapping resistance based on response body content rewriting and mapping interference based on adversarial content addition to key nodes, and changing the organization attribution under the minimum overhead. According to the method, the network asset node under a certain organization is searched, so that leakage protection of private network assets under relatively low overhead is realized.

The invention discloses a network data collection method, device and equipment based on big data and a storage medium, and belongs to the technical field of data collection. Receiving an acquisition task demand input by a user, monitoring network flow data according to the acquisition task demand, and dynamically setting a network data acquisition frequency; evaluating a plurality of preset collected webpages through a PageRank algorithm, and determining an importance degree score of each webpage; executing a collection task based on the network data collection frequency, sequentially sending access requests to corresponding webpages from high to low by utilizing a network crawler program according to a webpage importance degree sorting result, capturing successfully accessed webpage contents, and storing the successfully accessed webpage contents in a data warehouse; and traversing the data warehouse to obtain the captured webpage content, expressing the webpage content and the theme keyword as vectors, judging the correlation degree of the webpage content and the theme by calculating the cosine similarity between the vectors, and obtaining target network data.

The invention provides a social relationship intimacy calculation method and system under social media privacy propagation, and relates to the technical field of social network analysis, and the method comprises the steps: building a directed weighted graph based on user privacy propagation behaviors, taking a user as a node, taking a directed propagation relationship as an edge, and fusing an edge weight with a propagation frequency, a content average sensitivity and a topological distance adjustment factor; secondly, a Personalized PageRank algorithm is improved, a path distance Gaussian attenuation transfer matrix is introduced, and a global trust matrix is iteratively calculated; a double-exponential attenuation function is adopted for historical interaction, short-term fast attenuation and long-term slow attenuation are modeled, and a time-varying weight is obtained; quantifying privacy content sensitivity in combination with information entropy and a propagation risk model; identifying a user community and quantifying a community contribution degree; global trust, time-varying weight and sensitivity multiplication are fused, normalization is carried out after community contribution degree correction, and a final intimacy score is generated. According to the method, the relationship intimacy under privacy propagation is accurately described, and dynamic evaluation is supported.

The application discloses a kind of knowledge question and answer method and system based on theme knowledge graph retrieval enhancement, this method is first based on original document using large language model extraction triad form representation local theme, and clustering, generate the global theme triad set of global perspective representing whole document.Secondly based on global theme triad set, adopt theme-guided entity and relationship extraction, construct hybrid knowledge graph.Then propose semantic perception's personalized PageRank algorithm, the semantics of query and the semantics of the edge in hybrid knowledge graph are matched, and the weight of the score propagation between nodes is dynamically adjusted;Finally, a three-level progressive retrieval mechanism is designed to retrieve multi-level information related to user queries from the hybrid knowledge graph, and input into the large language model to generate the final answer.The application significantly improves the semantic integrity and retrieval accuracy of the knowledge graph, ensuring the accuracy, comprehensiveness and empowerment of the generated answer.

The invention discloses a risk monitoring method and system based on a relation graph and electronic equipment, and the method comprises the steps: obtaining multi-source business data, carrying out the data integration of the multi-source business data, and obtaining integrated data; constructing a relation graph based on the integrated data to obtain a multivariate relation graph; analyzing high-risk nodes and abnormal behaviors based on the multivariate relation graph, and determining a comprehensive risk score of the high-risk nodes; and carrying out risk early warning based on the comprehensive risk score. According to the invention, the relation graph containing multiple entities such as employees, suppliers, purchase orders, travel records and the like is constructed, and the PageRank algorithm and time sequence analysis are combined, so that intelligent detection and real-time early warning of potential benefit transmission behaviors are realized, and the method is suitable for supply chain compliance management of multiple scenes such as the manufacturing industry, the retail industry, an e-commerce platform and the like.

The invention discloses a viral propagation model construction and strategy optimization method and system for e-commerce platform social fission marketing, and particularly relates to the technical field of e-commerce platform social contact. The method comprises the following steps: 1, executing a multi-dimensional data acquisition and preprocessing step to ensure the integrity and accuracy of data; 2, constructing a social network graph, and identifying key propagation nodes by using a PageRank algorithm; 3, generating a personalized recommendation list in combination with historical behaviors of the user; 4, constructing and dynamically adjusting a virus-type propagation model, and simulating a propagation process of marketing information; 5, collecting key indexes, designing an A / B test, and dynamically adjusting a fission marketing strategy; the system combines historical behaviors (browsing, purchasing and interacting) of a user to construct an interest portrait, a personalized recommendation list is generated, and content relevance is improved; meanwhile, the influence of different copywriting and reward mechanisms on the propagation effect is compared through A / B tests.

The invention discloses a supply chain optimization system management of a PageRank improved algorithm, the system comprises a data collection module, a data processing module and a decision support module, the invention relates to the technical field of supply chain optimization, and solves the technical problems of node unidirectional linking of a supply chain in a directed acyclic graph and an opinion leader identification method of the supply chain in a DAG (Directed Acyclic Graph). According to the method, the improved PageRank algorithm is used, the opinion leader is identified in the DAG, the nodes without chain outgoing in the DAG supply chain are analyzed, and the PageRank values of the nodes are equally divided, so that all the nodes can be subjected to ranking distribution, ranking loss caused by node aggregation is avoided, ranking leakage is prevented, and the ranking efficiency is improved. And secondly, the opinion leader is further identified by determining the centrality index in different modes, and the supply chain manager can make decisions and control more effectively by identifying the opinion leader, so that the overall efficiency of the supply chain is improved.

This invention discloses a GCN-based method for detecting stegchmakers in social networks, addressing the challenge of stegchmaker detection in these networks. First, a social network graph is constructed, with active users and comments designated as nodes, and user replies as directed edges with attributes (interaction frequency, content similarity, and reply time) to analyze social interactions and potential steganographic behavior. A personalized PageRank algorithm is employed, using known stegchmaker nodes as seeds to construct vectors. Weights are adjusted based on edge attributes, and the PageRank value of each node is iteratively calculated to determine the association between the node and the stegchmaker. Simultaneously, GCN is utilized to capture syntactic and semantic connections between words, and features are processed using ResNet to fuse local and global information, optimizing the understanding and processing capabilities for complex graph structures. Experiments determine the weights of text feature scores and PageRank values, comparing the performance of different methods on Reddit and Twitter datasets. This verifies that the proposed method effectively captures steganographic behavior and improves detection accuracy under different loads and encoding strategies, providing a new approach and technical support for steganalysis in social networks.