The Sylow graph of a finite group originates from recent investigations on certain classes of groups, defined in terms of normalizers of Sylow subgroups. The connectivity of this graph has been proved only last year w...The Sylow graph of a finite group originates from recent investigations on certain classes of groups, defined in terms of normalizers of Sylow subgroups. The connectivity of this graph has been proved only last year with the use of the classification of finite simple groups (CFSG). A series of interesting questions arise naturally. First of all, it is not clear whether it is possible to avoid CFSG or not. On the other hand, what happens for infinite groups? Since the status of knowledge of the non-commuting graph and of the prime graph is satisfactory, is it possible to find relations between these two graphs and the Sylow graph? In the present note we make the point of the situation and formulate the above questions in appropriate way.展开更多
Let S and K be two subrings of a finite ring R. Then the generalized non- commuting graph of subrings S, K of R, denoted by [＇S,K, is a simple graph whose vertex set is （S U K）／（CK（S） U Cs（K））, and where two...Let S and K be two subrings of a finite ring R. Then the generalized non- commuting graph of subrings S, K of R, denoted by [＇S,K, is a simple graph whose vertex set is （S U K）／（CK（S） U Cs（K））, and where two distinct vertices a, b are adjacent if and only if a E S or b E S and ab ≠ ba. We determine the diameter, girth and some dominating sets for FS, K. Some connections between Fs,K and Pr（S, K） are also obtained. Further, Z-isoclinism between two pairs of finite rings is defined, and we show that the generalized non-commuting graphs of two Y_~isoclinic pairs are isomorphic under some conditions.展开更多
基于深度学习的网络攻击检测是对欧几里得数据进行建模,无法学习攻击数据中的结构特征。为此,提出一种基于改进图采样与聚合(graph sample and aggregate,GraphSAGE)的网络攻击检测算法。首先,将攻击数据从平面结构转换为图结构数据。其...基于深度学习的网络攻击检测是对欧几里得数据进行建模,无法学习攻击数据中的结构特征。为此,提出一种基于改进图采样与聚合(graph sample and aggregate,GraphSAGE)的网络攻击检测算法。首先,将攻击数据从平面结构转换为图结构数据。其次,对GraphSAGE算法进行了改进,包括在消息传递阶段融合节点和边的特征,同时在消息聚合过程中考虑不同源节点对目标节点的影响程度,并在边嵌入生成时引入残差学习机制。在两个公开网络攻击数据集上的实验结果表明,在二分类情况下,所提算法的总体性能优于E-GraphSAGE、LSTM、RNN、CNN算法;在多分类情况下,所提算法在大多数攻击类型上的F1值高于对比算法。展开更多
In this paper,we first give a sufficient condition for a graph being fractional ID-[a,b]-factor-critical covered in terms of its independence number and minimum degree,which partially answers the problem posed by Sizh...In this paper,we first give a sufficient condition for a graph being fractional ID-[a,b]-factor-critical covered in terms of its independence number and minimum degree,which partially answers the problem posed by Sizhong Zhou,Hongxia Liu and Yang Xu(2022).Then,an A_(α)-spectral condition is given to ensure that G is a fractional ID-[a,b]-factor-critical covered graph and an(a,b,k)-factor-critical graph,respectively.In fact,(a,b,k)-factor-critical graph is a graph which has an[a,b]-factor for k=0.Thus,these above results extend the results of Jia Wei and Shenggui Zhang(2023)and Ao Fan,Ruifang Liu and Guoyan Ao(2023)in some sense.展开更多
In this paper, a new class of rings, called FIC rings, is introduced for studying quasi-zero-divisor graphs of rings. Let R be a ring. The quasi-zero-divisor graph of R, denoted by Г＊（R）, is a directed graph defin...In this paper, a new class of rings, called FIC rings, is introduced for studying quasi-zero-divisor graphs of rings. Let R be a ring. The quasi-zero-divisor graph of R, denoted by Г＊（R）, is a directed graph defined on its nonzero quasi-zero-divisors, where there is an arc from a vertex x to another vertex y if and only if xRy = 0. We show that the following three conditions on an FIC ring R are equivalent： （1） χ（R） is finite; （2） ω（R） is finite; （3） Nil＊ R is finite where Nil.R equals the finite intersection of prime ideals. Furthermore, we also completely determine the connectedness, the diameter and the girth of Г＊ （R）.展开更多
This paper introduces an ideal-boyed zero-divisor graph of non-commutative rings,denoted ΓI（R）.ΓI（R） is a directed graph.The properties and possible structures of the graph is studied.
Let G be a finite group. A nonempty subset X of G is said to be noncommuting if xy≠yx for any x, y ∈ X with x≠y. If ｜X｜ ≥ ｜Y｜ for any other non-commuting set Y in G, then X is said to be a maximal non-commutin...Let G be a finite group. A nonempty subset X of G is said to be noncommuting if xy≠yx for any x, y ∈ X with x≠y. If ｜X｜ ≥ ｜Y｜ for any other non-commuting set Y in G, then X is said to be a maximal non-commuting set. In this paper, we determine upper and lower bounds on the cardinality of a maximal non-commuting set in a finite p-group with derived subgroup of prime order.展开更多
为解决财务人员数字技术应用能力不足、传统财务流程中数据采集质量差导致重复返工、人工数据处理效率低等问题,设计开发了财务共享辅助系统。采用机器人流程自动化(RPA,Robotic Process Automation)和图检索增强生成(Graph RAG,Graph-b...为解决财务人员数字技术应用能力不足、传统财务流程中数据采集质量差导致重复返工、人工数据处理效率低等问题,设计开发了财务共享辅助系统。采用机器人流程自动化(RPA,Robotic Process Automation)和图检索增强生成(Graph RAG,Graph-based Retrieval-Augmented Generation)技术,实现数据填报收集、RPA自动化处理、智能问答等功能,显著提升财务报账效率,为铁路局集团公司财务共享中心的建设提供支撑。展开更多
In the international shipping industry, digital intelligence transformation has become essential, with both governments and enterprises actively working to integrate diverse datasets. The domain of maritime and shippi...In the international shipping industry, digital intelligence transformation has become essential, with both governments and enterprises actively working to integrate diverse datasets. The domain of maritime and shipping is characterized by a vast array of document types, filled with complex, large-scale, and often chaotic knowledge and relationships. Effectively managing these documents is crucial for developing a Large Language Model (LLM) in the maritime domain, enabling practitioners to access and leverage valuable information. A Knowledge Graph (KG) offers a state-of-the-art solution for enhancing knowledge retrieval, providing more accurate responses and enabling context-aware reasoning. This paper presents a framework for utilizing maritime and shipping documents to construct a knowledge graph using GraphRAG, a hybrid tool combining graph-based retrieval and generation capabilities. The extraction of entities and relationships from these documents and the KG construction process are detailed. Furthermore, the KG is integrated with an LLM to develop a Q&A system, demonstrating that the system significantly improves answer accuracy compared to traditional LLMs. Additionally, the KG construction process is up to 50% faster than conventional LLM-based approaches, underscoring the efficiency of our method. This study provides a promising approach to digital intelligence in shipping, advancing knowledge accessibility and decision-making.展开更多
The increasing popularity of the Internet and the widespread use of information technology have led to a rise in the number and sophistication of network attacks and security threats.Intrusion detection systems are cr...The increasing popularity of the Internet and the widespread use of information technology have led to a rise in the number and sophistication of network attacks and security threats.Intrusion detection systems are crucial to network security,playing a pivotal role in safeguarding networks from potential threats.However,in the context of an evolving landscape of sophisticated and elusive attacks,existing intrusion detection methodologies often overlook critical aspects such as changes in network topology over time and interactions between hosts.To address these issues,this paper proposes a real-time network intrusion detection method based on graph neural networks.The proposedmethod leverages the advantages of graph neural networks and employs a straightforward graph construction method to represent network traffic as dynamic graph-structured data.Additionally,a graph convolution operation with a multi-head attention mechanism is utilized to enhance the model’s ability to capture the intricate relationships within the graph structure comprehensively.Furthermore,it uses an integrated graph neural network to address dynamic graphs’structural and topological changes at different time points and the challenges of edge embedding in intrusion detection data.The edge classification problem is effectively transformed into node classification by employing a line graph data representation,which facilitates fine-grained intrusion detection tasks on dynamic graph node feature representations.The efficacy of the proposed method is evaluated using two commonly used intrusion detection datasets,UNSW-NB15 and NF-ToN-IoT-v2,and results are compared with previous studies in this field.The experimental results demonstrate that our proposed method achieves 99.3%and 99.96%accuracy on the two datasets,respectively,and outperforms the benchmark model in several evaluation metrics.展开更多
With the emphasis on user privacy and communication security, encrypted traffic has increased dramatically, which brings great challenges to traffic classification. The classification method of encrypted traffic based...With the emphasis on user privacy and communication security, encrypted traffic has increased dramatically, which brings great challenges to traffic classification. The classification method of encrypted traffic based on GNN can deal with encrypted traffic well. However, existing GNN-based approaches ignore the relationship between client or server packets. In this paper, we design a network traffic topology based on GCN, called Flow Mapping Graph (FMG). FMG establishes sequential edges between vertexes by the arrival order of packets and establishes jump-order edges between vertexes by connecting packets in different bursts with the same direction. It not only reflects the time characteristics of the packet but also strengthens the relationship between the client or server packets. According to FMG, a Traffic Mapping Classification model (TMC-GCN) is designed, which can automatically capture and learn the characteristics and structure information of the top vertex in FMG. The TMC-GCN model is used to classify the encrypted traffic. The encryption stream classification problem is transformed into a graph classification problem, which can effectively deal with data from different data sources and application scenarios. By comparing the performance of TMC-GCN with other classical models in four public datasets, including CICIOT2023, ISCXVPN2016, CICAAGM2017, and GraphDapp, the effectiveness of the FMG algorithm is verified. The experimental results show that the accuracy rate of the TMC-GCN model is 96.13%, the recall rate is 95.04%, and the F1 rate is 94.54%.展开更多
Traditional Chinese medicine(TCM)features complex compatibility mechanisms involving multicomponent,multi-target,and multi-pathway interactions.This study presents an interpretable graph artificial intelligence(GraphA...Traditional Chinese medicine(TCM)features complex compatibility mechanisms involving multicomponent,multi-target,and multi-pathway interactions.This study presents an interpretable graph artificial intelligence(GraphAI)framework to quantify such mechanisms in Chinese herbal formulas(CHFs).A multidimensional TCM knowledge graph(TCM-MKG;https://zenodo.org/records/13763953)was constructed,integrating seven standardized modules:TCM terminology,Chinese patent medicines(CPMs),Chinese herbal pieces(CHPs),pharmacognostic origins(POs),chemical compounds,biological targets,and diseases.A neighbor-diffusion strategy was used to address the sparsity of compound-target associations,increasing target coverage from 12.0%to 98.7%.Graph neural networks(GNNs)with attention mechanisms were applied to 6,080 CHFs,modeled as graphs with CHPs as nodes.To embed domain-specific semantics,virtual nodes medicinal properties,i.e.,therapeutic nature,flavor,and meridian tropism,were introduced,enabling interpretable modeling of inter-CHP relationships.The model quantitatively captured classical compatibility roles such as“monarch-minister-assistant-guide”,and uncovered TCM etiological types derived from diagnostic and efficacy patterns.Model validation using 215 CHFs used for coronavirus disease 2019(COVID-19)management highlighted Radix Astragali-Rhizoma Phragmitis as a high-attention herb pair.Mass spectrometry(MS)and target prediction identified three active compounds,i.e.,methylinissolin-3-O-glucoside,corydalin,and pingbeinine,which converge on pathways such as neuroactive ligand-receptor interaction,xenobiotic response,and neuronal function,supporting their neuroimmune and detoxification potential.Given their high safety and dietary compatibility,this herb pair may offer therapeutic value for managing long COVID-19.All data and code are openly available(https://github.com/ZENGJingqi/GraphAI-for-TCM),providing a scalable and interpretable platform for TCM mechanism research and discovery of bioactive herbal constituents.展开更多
文摘The Sylow graph of a finite group originates from recent investigations on certain classes of groups, defined in terms of normalizers of Sylow subgroups. The connectivity of this graph has been proved only last year with the use of the classification of finite simple groups (CFSG). A series of interesting questions arise naturally. First of all, it is not clear whether it is possible to avoid CFSG or not. On the other hand, what happens for infinite groups? Since the status of knowledge of the non-commuting graph and of the prime graph is satisfactory, is it possible to find relations between these two graphs and the Sylow graph? In the present note we make the point of the situation and formulate the above questions in appropriate way.
文摘Let S and K be two subrings of a finite ring R. Then the generalized non- commuting graph of subrings S, K of R, denoted by [＇S,K, is a simple graph whose vertex set is （S U K）／（CK（S） U Cs（K））, and where two distinct vertices a, b are adjacent if and only if a E S or b E S and ab ≠ ba. We determine the diameter, girth and some dominating sets for FS, K. Some connections between Fs,K and Pr（S, K） are also obtained. Further, Z-isoclinism between two pairs of finite rings is defined, and we show that the generalized non-commuting graphs of two Y_~isoclinic pairs are isomorphic under some conditions.
文摘基于深度学习的网络攻击检测是对欧几里得数据进行建模,无法学习攻击数据中的结构特征。为此,提出一种基于改进图采样与聚合(graph sample and aggregate,GraphSAGE)的网络攻击检测算法。首先,将攻击数据从平面结构转换为图结构数据。其次,对GraphSAGE算法进行了改进,包括在消息传递阶段融合节点和边的特征,同时在消息聚合过程中考虑不同源节点对目标节点的影响程度,并在边嵌入生成时引入残差学习机制。在两个公开网络攻击数据集上的实验结果表明,在二分类情况下,所提算法的总体性能优于E-GraphSAGE、LSTM、RNN、CNN算法;在多分类情况下,所提算法在大多数攻击类型上的F1值高于对比算法。
基金Supported by the National Natural Science Foundation of China(Grant Nos.11961041,12261055)the Key Project of Natural Science Foundation of Gansu Province(Grant No.24JRRA222)the Foundation for Innovative Fundamental Research Group Project of Gansu Province(Grant No.25JRRA805).
文摘In this paper,we first give a sufficient condition for a graph being fractional ID-[a,b]-factor-critical covered in terms of its independence number and minimum degree,which partially answers the problem posed by Sizhong Zhou,Hongxia Liu and Yang Xu(2022).Then,an A_(α)-spectral condition is given to ensure that G is a fractional ID-[a,b]-factor-critical covered graph and an(a,b,k)-factor-critical graph,respectively.In fact,(a,b,k)-factor-critical graph is a graph which has an[a,b]-factor for k=0.Thus,these above results extend the results of Jia Wei and Shenggui Zhang(2023)and Ao Fan,Ruifang Liu and Guoyan Ao(2023)in some sense.
基金Supported by the National Natural Science Foundation of China(Grant Nos.1137134311161006+4 种基金1166101411171142)the Guangxi Science Research and Technology Development Project(Grant No.1599005-2-13)the Scientic Research Fund of Guangxi Education Department(Grant No.KY2015ZD075)the Natural Science Foundation of Guangxi(Grant No.2016GXSFDA380017)
文摘In this paper, a new class of rings, called FIC rings, is introduced for studying quasi-zero-divisor graphs of rings. Let R be a ring. The quasi-zero-divisor graph of R, denoted by Г＊（R）, is a directed graph defined on its nonzero quasi-zero-divisors, where there is an arc from a vertex x to another vertex y if and only if xRy = 0. We show that the following three conditions on an FIC ring R are equivalent： （1） χ（R） is finite; （2） ω（R） is finite; （3） Nil＊ R is finite where Nil.R equals the finite intersection of prime ideals. Furthermore, we also completely determine the connectedness, the diameter and the girth of Г＊ （R）.
基金Supported by Guangxi Natural Sciences Foundation(0575052,0640070)Supported byInnovation Project of Guangxi Graduate Education(2006106030701M05)Supported Scientific Research Foun-dation of Guangxi Educational Committee
文摘This paper introduces an ideal-boyed zero-divisor graph of non-commutative rings,denoted ΓI（R）.ΓI（R） is a directed graph.The properties and possible structures of the graph is studied.
基金The NSF(11301150,11371124)of Chinathe NSF(142300410134)of Henan ProvincePlan for Scientific Innovation Talent(11CXRC19)of Henan University of Technology
文摘Let G be a finite group. A nonempty subset X of G is said to be noncommuting if xy≠yx for any x, y ∈ X with x≠y. If ｜X｜ ≥ ｜Y｜ for any other non-commuting set Y in G, then X is said to be a maximal non-commuting set. In this paper, we determine upper and lower bounds on the cardinality of a maximal non-commuting set in a finite p-group with derived subgroup of prime order.
文摘In the international shipping industry, digital intelligence transformation has become essential, with both governments and enterprises actively working to integrate diverse datasets. The domain of maritime and shipping is characterized by a vast array of document types, filled with complex, large-scale, and often chaotic knowledge and relationships. Effectively managing these documents is crucial for developing a Large Language Model (LLM) in the maritime domain, enabling practitioners to access and leverage valuable information. A Knowledge Graph (KG) offers a state-of-the-art solution for enhancing knowledge retrieval, providing more accurate responses and enabling context-aware reasoning. This paper presents a framework for utilizing maritime and shipping documents to construct a knowledge graph using GraphRAG, a hybrid tool combining graph-based retrieval and generation capabilities. The extraction of entities and relationships from these documents and the KG construction process are detailed. Furthermore, the KG is integrated with an LLM to develop a Q&A system, demonstrating that the system significantly improves answer accuracy compared to traditional LLMs. Additionally, the KG construction process is up to 50% faster than conventional LLM-based approaches, underscoring the efficiency of our method. This study provides a promising approach to digital intelligence in shipping, advancing knowledge accessibility and decision-making.
文摘The increasing popularity of the Internet and the widespread use of information technology have led to a rise in the number and sophistication of network attacks and security threats.Intrusion detection systems are crucial to network security,playing a pivotal role in safeguarding networks from potential threats.However,in the context of an evolving landscape of sophisticated and elusive attacks,existing intrusion detection methodologies often overlook critical aspects such as changes in network topology over time and interactions between hosts.To address these issues,this paper proposes a real-time network intrusion detection method based on graph neural networks.The proposedmethod leverages the advantages of graph neural networks and employs a straightforward graph construction method to represent network traffic as dynamic graph-structured data.Additionally,a graph convolution operation with a multi-head attention mechanism is utilized to enhance the model’s ability to capture the intricate relationships within the graph structure comprehensively.Furthermore,it uses an integrated graph neural network to address dynamic graphs’structural and topological changes at different time points and the challenges of edge embedding in intrusion detection data.The edge classification problem is effectively transformed into node classification by employing a line graph data representation,which facilitates fine-grained intrusion detection tasks on dynamic graph node feature representations.The efficacy of the proposed method is evaluated using two commonly used intrusion detection datasets,UNSW-NB15 and NF-ToN-IoT-v2,and results are compared with previous studies in this field.The experimental results demonstrate that our proposed method achieves 99.3%and 99.96%accuracy on the two datasets,respectively,and outperforms the benchmark model in several evaluation metrics.
基金supported by the National Key Research and Development Program of China No.2023YFA1009500.
文摘With the emphasis on user privacy and communication security, encrypted traffic has increased dramatically, which brings great challenges to traffic classification. The classification method of encrypted traffic based on GNN can deal with encrypted traffic well. However, existing GNN-based approaches ignore the relationship between client or server packets. In this paper, we design a network traffic topology based on GCN, called Flow Mapping Graph (FMG). FMG establishes sequential edges between vertexes by the arrival order of packets and establishes jump-order edges between vertexes by connecting packets in different bursts with the same direction. It not only reflects the time characteristics of the packet but also strengthens the relationship between the client or server packets. According to FMG, a Traffic Mapping Classification model (TMC-GCN) is designed, which can automatically capture and learn the characteristics and structure information of the top vertex in FMG. The TMC-GCN model is used to classify the encrypted traffic. The encryption stream classification problem is transformed into a graph classification problem, which can effectively deal with data from different data sources and application scenarios. By comparing the performance of TMC-GCN with other classical models in four public datasets, including CICIOT2023, ISCXVPN2016, CICAAGM2017, and GraphDapp, the effectiveness of the FMG algorithm is verified. The experimental results show that the accuracy rate of the TMC-GCN model is 96.13%, the recall rate is 95.04%, and the F1 rate is 94.54%.
基金supported by the National Natural Science Foundation of China(Grant No.:82230117).
文摘Traditional Chinese medicine(TCM)features complex compatibility mechanisms involving multicomponent,multi-target,and multi-pathway interactions.This study presents an interpretable graph artificial intelligence(GraphAI)framework to quantify such mechanisms in Chinese herbal formulas(CHFs).A multidimensional TCM knowledge graph(TCM-MKG;https://zenodo.org/records/13763953)was constructed,integrating seven standardized modules:TCM terminology,Chinese patent medicines(CPMs),Chinese herbal pieces(CHPs),pharmacognostic origins(POs),chemical compounds,biological targets,and diseases.A neighbor-diffusion strategy was used to address the sparsity of compound-target associations,increasing target coverage from 12.0%to 98.7%.Graph neural networks(GNNs)with attention mechanisms were applied to 6,080 CHFs,modeled as graphs with CHPs as nodes.To embed domain-specific semantics,virtual nodes medicinal properties,i.e.,therapeutic nature,flavor,and meridian tropism,were introduced,enabling interpretable modeling of inter-CHP relationships.The model quantitatively captured classical compatibility roles such as“monarch-minister-assistant-guide”,and uncovered TCM etiological types derived from diagnostic and efficacy patterns.Model validation using 215 CHFs used for coronavirus disease 2019(COVID-19)management highlighted Radix Astragali-Rhizoma Phragmitis as a high-attention herb pair.Mass spectrometry(MS)and target prediction identified three active compounds,i.e.,methylinissolin-3-O-glucoside,corydalin,and pingbeinine,which converge on pathways such as neuroactive ligand-receptor interaction,xenobiotic response,and neuronal function,supporting their neuroimmune and detoxification potential.Given their high safety and dietary compatibility,this herb pair may offer therapeutic value for managing long COVID-19.All data and code are openly available(https://github.com/ZENGJingqi/GraphAI-for-TCM),providing a scalable and interpretable platform for TCM mechanism research and discovery of bioactive herbal constituents.
