18 results about "Citation network" patented technology

This invention relates to the field of computer complex network optimization technology, and discloses a method for mining key nodes in a network based on a discount strategy and an improved discrete crow search algorithm. The method includes: first, preprocessing the citation network to convert it into an adjacency matrix, and then reversing the network to obtain a reverse network; then using the LRDiscount algorithm to initially screen the nodes in the reverse network to obtain a candidate node set. C Next, the candidate node set is optimized according to the local optimization process of the improved discrete crow search algorithm. C Optimization is performed; finally, the optimal set is selected from the optimized node set, and the node influence is evaluated to obtain the final result. k Key seed nodes. Compared with existing technologies, this invention combines the influence discount strategy of network nodes with an improved discrete crow search algorithm. It spreads influence by updating node positions in the process of mimicking crow search, and finds key nodes by the marginal gain generated by individual crows walking in the citation network.

The invention discloses a community discovery method of a graph neural network based on multi-view information fusion, and belongs to the technical field of data mining. The method comprises the following steps: preprocessing data, performing PCA dimension reduction on an attribute matrix, and normalizing an adjacent matrix; inputting the correlation matrix combination into an automatic encoder and a graph attention automatic encoder to obtain a new matrix representation and fusing the new matrix representation into a final node representation matrix; then back-propagating the optimization model by using various loss functions; and finally, community division is realized by using a k-means clustering algorithm. According to the method, the structure and attribute information of the original data are fully utilized, multi-view information is effectively fused, the accuracy of community discovery is improved, and the method has great significance in the fields of research on citation networks, recommendation systems and the like.

The application provides a keyword reverse propagation algorithm based on a citation network structure, and comprises the following steps: a spring charge model is established, force-directed layout processing is performed, and a force-directed layout graph is established; a keyword propagation model is established by using a reverse propagation algorithm, and a keyword weight change contrast curve is obtained; a citation network model is constructed by using the force-directed layout graph; iterative calculation is performed on the force-directed layout graph until the energy state in the force-directed layout graph reaches a minimum value; while the citation network is iteratively calculated, the keyword weight in the citation network model is adjusted, and a converged citation network layout graph is calculated. The application has the beneficial effects that the entire network is taken as a main body, keywords owned by cited documents are selected at a certain probability and are propagated backward along the network to the citing documents, the weight of the keywords is increased while the text clustering idea is retained, and clear visual data effects are obtained through force-directed layout.

The invention discloses an educational academic literature tracing method and system based on retrieval enhancement generation. The method comprises the following steps: performing fine-grained analysis on the multi-source literature, constructing a document object containing fields such as a title, an abstract, a methodology, an experimental result and a conclusion, and establishing a field-level index and a citation network feature score; identifying an academic intention queried by a user by utilizing a large model, dynamically loading a weight mapping table according to the academic intention, and performing weighted rearrangement on multiple paths of retrieval results to obtain candidate core literatures; generating a traceability text with an explicit label based on the candidate literature; and executing reference consistency verification by using the natural language inference model, and executing illusion correction according to a verification result. According to the method, through structured field analysis and dynamic rearrangement of intention driving, the problems of semantic fragmentation and reference fictition when a general RAG is used for processing complex academic literatures are solved, and the academic traceability preciseness and accuracy are remarkably improved.

The application provides a core set calculation method for a graph structure, relates to the field of graph structure, and constructs a citation network graph and generates an adjacency vector of each node, calculates a DOPH signature of each adjacency vector, and then performs clustering division based on the DOPH signature, calculates by using a GA error, and accelerates by using a clustering-based method, and meanwhile, the application combines a reverse reachable set of the node, selects the node to form a core set, so that the application constructs the core set capable of representing the information of the whole graph structure based on the propagation characteristics, local structure similarity and global role information of the nodes in the graph. Further, the framework introduces a graph adaptive sampling strategy and an adjustable covering mechanism, can dynamically balance the calculation efficiency and the representation accuracy according to the task requirement, and thus realizes the automatic balance between the effect and the cost.

Embodiments of the present application provide a text analysis method and device, electronic equipment and storage medium, and relate to the technical field of data mining. The method comprises: constructing a text citation network corresponding to a set of texts to be analyzed based on the citation relationship between each text in the set of texts to be analyzed in a target field; selecting a plurality of candidate paths from the text citation network; performing at least one clustering operation on the plurality of candidate paths until a preset ending condition is met, and at least two clustering centers obtained when the preset ending condition is met are taken as knowledge evolution paths corresponding to at least two subfields in the target field respectively. Through analysis of the set of texts to be analyzed in the target field, embodiments of the present application obtain the development context corresponding to each subfield in the target field, and the analysis result is more comprehensive; the text semantic information and network structure information of the candidate paths are considered, the information utilization is more comprehensive, and the analysis result is more accurate.

The invention provides a graph structure-oriented core set calculation method, and relates to the field of graph structures, and the method comprises the steps: constructing a citation network graph, generating an adjacent vector of each node, calculating a DOPH signature of each adjacent vector, carrying out the clustering division based on the DOPH signatures, carrying out the calculation through a GA error, and carrying out the acceleration through a clustering-based method. And meanwhile, a core set is formed by combining a reverse reachable set of the nodes and the selected nodes, so that the core set capable of representing total graph structure information is constructed on the basis of propagation characteristics, local structure similarity and global role information of the nodes in the graph. Furthermore, a graph self-adaptive sampling strategy and an adjustable coverage mechanism are introduced into the framework, the calculation efficiency and the representation precision can be dynamically balanced according to task requirements, and therefore automatic balance between the effect and the cost is achieved.

The application relates to the technical field of document management, in particular to a scientific research document management system and method based on core document identification and front prediction, which comprises a data acquisition and preprocessing module, a core document identification module, a front document supplement module, a trend analysis and prediction module and an error processing and adaptive engine; the data acquisition and preprocessing module is used for realizing automatic acquisition, cleaning and standardization of document data. The application combines citation network structure analysis and front semantic modeling to construct a scientific research document management system with high accuracy, interpretability and foresight, which not only improves the document screening efficiency, but also provides an intelligent, visual and predictive scientific research auxiliary tool for scientific researchers, and can be widely applied to scientific research activities such as topic design, review writing and research trend judgment.

The application relates to the technical field of natural language processing, and particularly discloses a paper classification method for a citation network, a device and a medium, the method comprising the following steps: graph data preprocessing of the citation network, node centroid topology enhancement for the majority class and the minority class, and multi-period centroid node information fusion for the minority class. The method combines the node centroid topology enhancement and the multi-period centroid node information fusion mechanism, improves the classification performance of the graph neural network for the minority class nodes on the unbalanced graph, and improves the classification accuracy of different types of papers in the citation network.

The application provides a geoscience hotspot extraction and visualization method and system based on literature, comprising the following steps: step 1, constructing a literature citation network according to geoscience journal literature; step 2, clustering the geoscience journal literature based on the citation network; step 3, extracting hot keywords based on text statistics by adopting a theme construction method; step 4, extracting hot keywords based on text semantic information by adopting text embedding representation clustering; step 5, fusing and screening the extracted keywords, and obtaining geoscience research hot keywords in combination with paper clustering; and step 6, constructing a hot core paper set and a citation network thereof, and performing visual display. The application constructs a geoscience field hotspot mining and visualization scheme, can comprehensively utilize text statistics and semantic information to mine research hotspots compared with existing methods, can reveal the research focus and front direction of the current field, and helps researchers better understand the discipline development trend and grasp the front dynamic.

The application provides a citation network node classification method and system fusing GCN and multiple classifiers, wherein the classification method comprises the following steps: regarding the whole citation network as a non-directional graph G comprising node attributes and topological structures; calculating an adjacency matrix and a feature matrix of the non-directional graph G; initializing an edge weight matrix of the non-directional graph G, and calculating the edge weight of the non-directional graph G according to the feature matrix; converging the calculated edge weight to the nodes at both ends of the edge, updating the feature matrix; filtering and amplifying the features in the updated feature matrix; inputting the adjacency matrix and the updated feature matrix into n classifiers respectively for prediction and fusing the prediction results to obtain the final classification result. The classification method comprehensively considers the effective semantic information in the network, can obtain more accurate and stable classification effect through the prediction of multiple classifiers and the fusion by using fuzzy integration.

The application discloses an expert influence maximization method based on graph convolution and DBSCAN algorithm, and relates to the field of influence maximization research under a citation network. First, each expert is regarded as a node, and a citation network is constructed according to the citation relationship between the experts, a graph convolution neural network is used to extract features of the citation network, and a feature representation of each expert node is obtained; then, a matrix is clustered by using a DBSCAN algorithm, different communities are obtained, and a spectral clustering method is used to divide the expert nodes into different clustering clusters, and each clustering cluster represents an expert group with high influence; finally, the expert nodes are sorted according to influence scores, and thus a list of experts with the most influence is obtained. Compared with the prior art, the method provided in the application can better identify experts with high influence in the citation network, and is a relatively excellent influence maximization algorithm.

The invention discloses a graph self-encoding mask strategy optimization system and method for a link prediction task. The system comprises a mask generation module, a self-adaptive mask strategy module and a graph self-encoding module, the mask generation module is used for calculating an importance score of a reference edge connecting two literatures in the original literature reference network; the self-adaptive mask strategy module is used for dynamically adjusting a mask strategy according to the importance score and generating a mask edge set; and the graph self-encoding module is used for performing mask processing on the original literature reference network according to the mask edge set to obtain a masked graph structure, performing node representation learning and link prediction on the masked graph structure, and optimizing model parameters by using a loss function.

The application discloses a kind of construction methods and devices of high-speed rail technology evolution graph.The construction method includes: obtaining patent data set related to high-speed rail technology and classifying according to technical field, obtain multiple technical subsets;Based on PatentsBERTa model and cosine similarity calculation formula, obtain multiple similarity data sets;Obtain the similarity threshold of each technical subset, and based on the similarity threshold, obtain multiple candidate patent groups corresponding to each technical subset respectively;Based on the candidate patent group corresponding to each technical subset, build the time evolution graph and citation network graph corresponding to each technical subset, and convert into network data using graph projection, construct multiple evolution knowledge graphs;Obtain the target technology of pre-set, and based on the evolution knowledge graph corresponding to the technical subset of target technology, construct the evolution subgraph of target technology.The construction method provided by the application can identify the evolution path of each sub-technology in high-speed rail technology.

The invention specifically discloses a medical literature citation link tracking method and system, relates to medical health information processing, and solves the problems of large citation network scale, data isomerism, incomplete metadata analysis and low tracking efficiency in clinical evidence-based decision and drug safety monitoring. The method comprises the steps that heterogeneous medical literatures are collected and normalized, and evidence-based medical evidence levels are marked; analyzing the reference literature list, and complementing the DOI / PMID metadata; constructing a reference topology network of the graph database, clustering nodes by using a MeSH tree hierarchical structure, and extracting PICO elements; citation intensity is calculated by integrating citation frequency, position weight, context motivation, evidence level and PICO similarity, and key paths are screened; traceability and diffusion tracking are supported, draft withdrawal events and adverse drug reaction signals are dynamically monitored, and real-time early warning is updated through streaming increment. According to the method, visual tracking and risk assessment of the reference relationship of the medical literature are realized, and efficient and accurate evidence tracing support is provided for clinical evidence-based decision and drug safety assessment.

The application discloses a BOW graph matching method and system based on spectral clustering, and the method comprises the following processes: extracting node features and topological features of a citation network graph; using an optimized K-means++ algorithm obtained by combining a spectral clustering algorithm with a genetic algorithm to optimize K values, to convert node features and topological feature descriptors of the citation network graph into words, and realizing construction of a dictionary; using a local constraint coding mode to code features of the dictionary, to obtain a visual vocabulary histogram; and classifying the visual vocabulary histogram, to realize the BOW graph matching method based on spectral clustering. The application uses the spectral clustering algorithm to cluster high-dimensional data sets, and then uses the K-means algorithm to perform two-stage clustering in a low-dimensional solution space, so that the problems of poor processing effect on high-dimensional data and low classification effect are solved.

The invention provides a dual-information cross community detection method based on a high-order perception encoder, and the method comprises the steps: carrying out the enhancement processing of a sparse original graph through a high-order heuristic graph, employing a trained MLP to recognize a potential high-order structure, carrying out the dual screening through combining community consistency and node feature similarity, and obtaining an enhanced adjacency matrix. And a double-information cross fusion module based on an attention mechanism is designed, so that the hidden layer representations of the automatic encoder and the graph attention automatic encoder are alternately propagated and adaptively fused, and the node representation capability is improved. Meanwhile, a triple self-supervised learning module is introduced, soft distribution is synchronously calculated in the fusion embedding space and the two sub-network embedding spaces, and the clustering process is optimized through KL divergence loss. And finally, community division is realized through K-means. The method effectively improves the accuracy and stability of community detection, and is suitable for complex network structures such as citation networks and social networks.

The invention discloses a literature classification method based on a heterogeneous graph comparison mask auto-encoder, and relates to the field of graph self-supervised learning. Firstly, a citation network in a certain field is abstracted into a heterogeneous graph, and feature spaces of different types of nodes such as papers, authors and keywords in the citation network are unified through feature projection; constructing a target node view and a non-target node view based on the node type mask to form a global complementary view; the two views are input into an online encoder and a momentum encoder respectively, the output of the online encoder is used for mask feature reconstruction, and the output of the momentum encoder is used for comparative learning, so that the discrimination and consistency of node representation are enhanced. An encoder and a decoder both adopt a relational graph neural network, and global structure and semantic information can be modeled without sub-graph decomposition. According to the method, the distinguishability of node representation in the citation network can be effectively improved, so that the accuracy of citation network academic literature classification is remarkably improved.