MATLAB software and optimal complete subgraph algorithm were used to extract and reveal the microsatellite distribution features in the complete genomes of the tobacco vein clearing virus （NC-003 378.1） from the NCB...MATLAB software and optimal complete subgraph algorithm were used to extract and reveal the microsatellite distribution features in the complete genomes of the tobacco vein clearing virus （NC-003 378.1） from the NCBI database.The results showed that the repetitions number and their location of the N-base group has been extracted and displayed.The largest repetitions of N-base group in the complete genomes of the tobacco vein clearing virus was decreased as the exponential function with the increasing of N.The method used in this study could be applied to the extraction and revealing of the microsatellite distribution features in the complete genomes of other viruses,thereby provided a basis for the research of the structure and the law of function,inheritance and variation by the using of the microsatellite distribution features.展开更多
gStore is an open-source native Resource Description Framework （RDF） triple store that answers SPARQL queries by subgraph matching over RDF graphs. However, there are some deficiencies in the original system design,...gStore is an open-source native Resource Description Framework （RDF） triple store that answers SPARQL queries by subgraph matching over RDF graphs. However, there are some deficiencies in the original system design, such as answering simple queries （including one-triple pattern queries）. To improve the efficiency of the system, we reconsider the system design in this paper. Specifically, we propose a new query plan generation module that generates different query plans according to the structures of query graphs. Furthermore, we re-design our vertex encoding strategy to achieve more pruning power and a new multi-join algorithm to speed up the subgraph matching process. Extensive experiments on synthetic and real RDF datasets show that our method outperforms the state-of-the-art algorithms significantly.展开更多
Subgraph matching problem is identifying a target subgraph in a graph. Graph neural network (GNN) is an artificial neural network model which is capable of processing general types of graph structured data. A graph ma...Subgraph matching problem is identifying a target subgraph in a graph. Graph neural network (GNN) is an artificial neural network model which is capable of processing general types of graph structured data. A graph may contain many subgraphs isomorphic to a given target graph. In this paper GNN is modeled to identify a subgraph that matches the target graph along with its characteristics. The simulation results show that GNN is capable of identifying a target sub-graph in a graph.展开更多
An element may have heterogeneous semantic interpretations in different ontologies. Therefore, understanding the real local meanings of elements is very useful for ontology operations such as querying and reasoning, w...An element may have heterogeneous semantic interpretations in different ontologies. Therefore, understanding the real local meanings of elements is very useful for ontology operations such as querying and reasoning, which are the foundations for many applications including semantic searching, ontology matching, and linked data analysis. However, since different ontologies have different preferences to describe their elements, obtaining the semantic context of an element is an open problem. A semantic subgraph was proposed to capture the real meanings of ontology elements. To extract the semantic subgraphs, a hybrid ontology graph is used to represent the semantic relations between elements. An extracting algorithm based on an electrical circuit model is then used with new conductivity calculation rules to improve the quality of the semantic subgraphs. The evaluation results show that the semantic subgraphs properly capture the local meanings of elements. Ontology matching based on semantic subgraphs also demonstrates that the semantic subgraph is a promising technique for ontology applications.展开更多
Currently,most existing inductive relation prediction approaches are based on subgraph structures,with subgraph features extracted using graph neural networks to predict relations.However,subgraphs may contain disconn...Currently,most existing inductive relation prediction approaches are based on subgraph structures,with subgraph features extracted using graph neural networks to predict relations.However,subgraphs may contain disconnected regions,which usually represent different semantic ranges.Because not all semantic information about the regions is helpful in relation prediction,we propose a relation prediction model based on a disentangled subgraph structure and implement a feature updating approach based on relevant semantic aggregation.To indirectly achieve the disentangled subgraph structure from a semantic perspective,the mapping of entity features into different semantic spaces and the aggregation of related semantics on each semantic space are updated.The disentangled model can focus on features having higher semantic relevance in the prediction,thus addressing a problem with existing approaches,which ignore the semantic differences in different subgraph structures.Furthermore,using a gated recurrent neural network,this model enhances the features of entities by sorting them by distance and extracting the path information in the subgraphs.Experimentally,it is shown that when there are numerous disconnected regions in the subgraph,our model outperforms existing mainstream models in terms of both Area Under the Curve-Precision-Recall(AUC-PR)and Hits@10.Experiments prove that semantic differences in the knowledge graph can be effectively distinguished and verify the effectiveness of this method.展开更多
Social robot accounts controlled by artificial intelligence or humans are active in social networks,bringing negative impacts to network security and social life.Existing social robot detection methods based on graph ...Social robot accounts controlled by artificial intelligence or humans are active in social networks,bringing negative impacts to network security and social life.Existing social robot detection methods based on graph neural networks suffer from the problem of many social network nodes and complex relationships,which makes it difficult to accurately describe the difference between the topological relations of nodes,resulting in low detection accuracy of social robots.This paper proposes a social robot detection method with the use of an improved neural network.First,social relationship subgraphs are constructed by leveraging the user’s social network to disentangle intricate social relationships effectively.Then,a linear modulated graph attention residual network model is devised to extract the node and network topology features of the social relation subgraph,thereby generating comprehensive social relation subgraph features,and the feature-wise linear modulation module of the model can better learn the differences between the nodes.Next,user text content and behavioral gene sequences are extracted to construct social behavioral features combined with the social relationship subgraph features.Finally,social robots can be more accurately identified by combining user behavioral and relationship features.By carrying out experimental studies based on the publicly available datasets TwiBot-20 and Cresci-15,the suggested method’s detection accuracies can achieve 86.73%and 97.86%,respectively.Compared with the existing mainstream approaches,the accuracy of the proposed method is 2.2%and 1.35%higher on the two datasets.The results show that the method proposed in this paper can effectively detect social robots and maintain a healthy ecological environment of social networks.展开更多
To meet the urgent requirement of enterprises for three-dimensional (3D) process models, an approach based on subgraph isomorphism is proposed to solve the matching problem between precursory 3D process model and 2D w...To meet the urgent requirement of enterprises for three-dimensional (3D) process models, an approach based on subgraph isomorphism is proposed to solve the matching problem between precursory 3D process model and 2D working procedure drawings. First, the projection drawings of the precursory 3D process model are obtained, then the primitives are extracted and the attributed adjacency graph (AAG) is constructed. Finally, by taking the 2D working procedure drawing as the AAG, and the projection drawing as the whole AAG, the matching problem between precursory 3D process model and 2D working procedure drawings is translated into the problem of subgraph isomorphism. To raise the matching efficiency, the AAG is partitioned, and the vertexes of the graph are classified effectively using the vertex’s attributes. Experimental results show that this method is able to support exact match and the matching efficiency can meet the requirement of practical applications.展开更多
基金Supported by the Eleventh Five-year Development Planning Project for Instructional Science in Hubei Province (2006B131)~~
文摘MATLAB software and optimal complete subgraph algorithm were used to extract and reveal the microsatellite distribution features in the complete genomes of the tobacco vein clearing virus （NC-003 378.1） from the NCBI database.The results showed that the repetitions number and their location of the N-base group has been extracted and displayed.The largest repetitions of N-base group in the complete genomes of the tobacco vein clearing virus was decreased as the exponential function with the increasing of N.The method used in this study could be applied to the extraction and revealing of the microsatellite distribution features in the complete genomes of other viruses,thereby provided a basis for the research of the structure and the law of function,inheritance and variation by the using of the microsatellite distribution features.
文摘gStore is an open-source native Resource Description Framework （RDF） triple store that answers SPARQL queries by subgraph matching over RDF graphs. However, there are some deficiencies in the original system design, such as answering simple queries （including one-triple pattern queries）. To improve the efficiency of the system, we reconsider the system design in this paper. Specifically, we propose a new query plan generation module that generates different query plans according to the structures of query graphs. Furthermore, we re-design our vertex encoding strategy to achieve more pruning power and a new multi-join algorithm to speed up the subgraph matching process. Extensive experiments on synthetic and real RDF datasets show that our method outperforms the state-of-the-art algorithms significantly.
文摘Subgraph matching problem is identifying a target subgraph in a graph. Graph neural network (GNN) is an artificial neural network model which is capable of processing general types of graph structured data. A graph may contain many subgraphs isomorphic to a given target graph. In this paper GNN is modeled to identify a subgraph that matches the target graph along with its characteristics. The simulation results show that GNN is capable of identifying a target sub-graph in a graph.
基金Supported by the National High-Tech Research and Development (863) Program of China (No.2009AA01Z147)the National Natural Science Foundation of China (Nos.61003156 and 90818027)the National Key Basic Research and Development (973) Program of China (No.2009CB320703)
文摘An element may have heterogeneous semantic interpretations in different ontologies. Therefore, understanding the real local meanings of elements is very useful for ontology operations such as querying and reasoning, which are the foundations for many applications including semantic searching, ontology matching, and linked data analysis. However, since different ontologies have different preferences to describe their elements, obtaining the semantic context of an element is an open problem. A semantic subgraph was proposed to capture the real meanings of ontology elements. To extract the semantic subgraphs, a hybrid ontology graph is used to represent the semantic relations between elements. An extracting algorithm based on an electrical circuit model is then used with new conductivity calculation rules to improve the quality of the semantic subgraphs. The evaluation results show that the semantic subgraphs properly capture the local meanings of elements. Ontology matching based on semantic subgraphs also demonstrates that the semantic subgraph is a promising technique for ontology applications.
基金supported by the National Natural Science Foundation of China(No.U19A2059)the 2022 Research Foundation of Chengdu Textile College(No.X22032161).
文摘Currently,most existing inductive relation prediction approaches are based on subgraph structures,with subgraph features extracted using graph neural networks to predict relations.However,subgraphs may contain disconnected regions,which usually represent different semantic ranges.Because not all semantic information about the regions is helpful in relation prediction,we propose a relation prediction model based on a disentangled subgraph structure and implement a feature updating approach based on relevant semantic aggregation.To indirectly achieve the disentangled subgraph structure from a semantic perspective,the mapping of entity features into different semantic spaces and the aggregation of related semantics on each semantic space are updated.The disentangled model can focus on features having higher semantic relevance in the prediction,thus addressing a problem with existing approaches,which ignore the semantic differences in different subgraph structures.Furthermore,using a gated recurrent neural network,this model enhances the features of entities by sorting them by distance and extracting the path information in the subgraphs.Experimentally,it is shown that when there are numerous disconnected regions in the subgraph,our model outperforms existing mainstream models in terms of both Area Under the Curve-Precision-Recall(AUC-PR)and Hits@10.Experiments prove that semantic differences in the knowledge graph can be effectively distinguished and verify the effectiveness of this method.
基金This work was supported in part by the National Natural Science Foundation of China under Grants 62273272,62303375 and 61873277in part by the Key Research and Development Program of Shaanxi Province under Grant 2023-YBGY-243+2 种基金in part by the Natural Science Foundation of Shaanxi Province under Grants 2022JQ-606 and 2020-JQ758in part by the Research Plan of Department of Education of Shaanxi Province under Grant 21JK0752in part by the Youth Innovation Team of Shaanxi Universities.
文摘Social robot accounts controlled by artificial intelligence or humans are active in social networks,bringing negative impacts to network security and social life.Existing social robot detection methods based on graph neural networks suffer from the problem of many social network nodes and complex relationships,which makes it difficult to accurately describe the difference between the topological relations of nodes,resulting in low detection accuracy of social robots.This paper proposes a social robot detection method with the use of an improved neural network.First,social relationship subgraphs are constructed by leveraging the user’s social network to disentangle intricate social relationships effectively.Then,a linear modulated graph attention residual network model is devised to extract the node and network topology features of the social relation subgraph,thereby generating comprehensive social relation subgraph features,and the feature-wise linear modulation module of the model can better learn the differences between the nodes.Next,user text content and behavioral gene sequences are extracted to construct social behavioral features combined with the social relationship subgraph features.Finally,social robots can be more accurately identified by combining user behavioral and relationship features.By carrying out experimental studies based on the publicly available datasets TwiBot-20 and Cresci-15,the suggested method’s detection accuracies can achieve 86.73%and 97.86%,respectively.Compared with the existing mainstream approaches,the accuracy of the proposed method is 2.2%and 1.35%higher on the two datasets.The results show that the method proposed in this paper can effectively detect social robots and maintain a healthy ecological environment of social networks.
基金supported by the National Natural Science Foundation of China (Grant No. 51075336)the National High Technology Research and Development Program of China (Grant No. 2007AA04Z137)
文摘To meet the urgent requirement of enterprises for three-dimensional (3D) process models, an approach based on subgraph isomorphism is proposed to solve the matching problem between precursory 3D process model and 2D working procedure drawings. First, the projection drawings of the precursory 3D process model are obtained, then the primitives are extracted and the attributed adjacency graph (AAG) is constructed. Finally, by taking the 2D working procedure drawing as the AAG, and the projection drawing as the whole AAG, the matching problem between precursory 3D process model and 2D working procedure drawings is translated into the problem of subgraph isomorphism. To raise the matching efficiency, the AAG is partitioned, and the vertexes of the graph are classified effectively using the vertex’s attributes. Experimental results show that this method is able to support exact match and the matching efficiency can meet the requirement of practical applications.
