Hierarchical Graph Model Research Articles

Missile Fault Detection and Localization Based on HBOS and Hierarchical Signed Directed Graph

The rudder surfaces and lifting surfaces of a missile are utilized to acquire aerodynamic forces and moments, adjust the missile’s attitude, and achieve precise strike missions. However, the harsh flying conditions of missiles make the rudder surfaces and lifting surfaces susceptible to faults. In practical scenarios, there is often a scarcity of fault data, and sometimes, it is even difficult to obtain such data. Currently, data-driven fault detection and localization methods heavily rely on fault data, posing challenges for their applicability. To address this issue, this paper proposes an HBOS (Histogram-Based Outlier Score) online fault-detection method based on statistical distribution. This method generates a fault-detection model by fitting the probability distribution of normal data and incorporates an adaptive threshold to achieve real-time fault detection. Furthermore, this paper abstracts the interrelationships between the missile’s flight states and the propagation mechanism of faults into a hierarchical directed graph model. By utilizing bilateral adaptive thresholds, it captures the first fault features of each sub-node and determines the fault propagation effectiveness of each layer node based on the compatibility path principle, thus establishing a fault inference and localization model. The results of semi-physical simulation experiments demonstrate that the proposed algorithm is independent of fault data and exhibits high real-time performance. In multiple sets of simulated tests with randomly parameterized deviations, the fault-detection accuracy exceeds 98% with a false-alarm rate of no more than 0.31%. The fault-localization algorithm achieves an accuracy rate of no less than 97.91%.

Attention-based hierarchical random graph model for structural inference of real-world networks

Attention-based hierarchical random graph model for structural inference of real-world networks

Document keyword extraction based on semantic hierarchical graph model

Document keyword extraction based on semantic hierarchical graph model

Stability and dynamics of a spectral graph model of brain oscillations.

We explore the stability and dynamic properties of a hierarchical, linearized, and analytic spectral graph model for neural oscillations that integrates the structural wiring of the brain. Previously, we have shown that this model can accurately capture the frequency spectra and the spatial patterns of the alpha and beta frequency bands obtained from magnetoencephalography recordings without regionally varying parameters. Here, we show that this macroscopic model based on long-range excitatory connections exhibits dynamic oscillations with a frequency in the alpha band even without any oscillations implemented at the mesoscopic level. We show that depending on the parameters, the model can exhibit combinations of damped oscillations, limit cycles, or unstable oscillations. We determined bounds on model parameters that ensure stability of the oscillations simulated by the model. Finally, we estimated time-varying model parameters to capture the temporal fluctuations in magnetoencephalography activity. We show that a dynamic spectral graph modeling framework with a parsimonious set of biophysically interpretable model parameters can thereby be employed to capture oscillatory fluctuations observed in electrophysiological data in various brain states and diseases.

Security and privacy preservation using constructive hierarchical data-sharing approach in cloud environment

ABSTRACT With the vast development in the cloud computing environment, many cloud users intend to outsource the data to a remote location and share the data with multiple users. The hierarchical model is an extensively used data organization process. It is highly complex to guarantee integrity, privacy, and confidentiality of the data and the structure of the model when the sensitive data is held in this manner. This research attempts to give a solution to hierarchically ensure the security and privacy of the information while data sharing occurs. A constructive hierarchical data sharing (CHDS) method is proposed to adopt symmetric encryption over the rooted hierarchical graph structure. The hierarchical graph model deals with incoming data features to establish the model’s privacy and authenticity. Based on this model, the proposed CHDS is known to be transparent, secure, and confident in the public environment. Here, performance metrics like computational complexity, key generation, prediction accuracy, and execution time are evaluated. The outcomes provide the security of the multi-party environment without forfeiting sensible resources when the hierarchical model grows to a more significant number of siblings, edges, and vertices. The proposed CHDS gives a better trade-off when compared with various existing approaches.

A Hierarchical Random Graph Efficient Sampling Algorithm Based on Improved MCMC Algorithm

A hierarchical random graph (HRG) model combined with a maximum likelihood approach and a Markov Chain Monte Carlo algorithm can not only be used to quantitatively describe the hierarchical organization of many real networks, but also can predict missing connections in partly known networks with high accuracy. However, the computational cost is very large when hierarchical random graphs are sampled by the Markov Chain Monte Carlo algorithm (MCMC), so that the hierarchical random graphs, which can describe the characteristics of network structure, cannot be found in a reasonable time range. This seriously limits the practicability of the model. In order to overcome this defect, an improved MCMC algorithm called two-state transitions MCMC (TST-MCMC) for efficiently sampling hierarchical random graphs is proposed in this paper. On the Markov chain composed of all possible hierarchical random graphs, TST-MCMC can generate two candidate state variables during state transition and introduce a competition mechanism to filter out the worse of the two candidate state variables. In addition, the detailed balance of Markov chain can be ensured by using Metropolis–Hastings rule. By using this method, not only can the convergence speed of Markov chain be improved, but the convergence interval of Markov chain can be narrowed as well. Three example networks are employed to verify the performance of the proposed algorithm. Experimental results show that our algorithm is more feasible and more effective than the compared schemes.

Структурное согласование алгоритмов глобальной оптимизации с архитектурой графических процессоров

Graphics processing units (GPU) deliver a high execution efficiency for modern metaheuristic algorithms with a high computation complexity. It is crucial to have an optimal task mapping of the optimization algorithm to the parallel system architecture which strongly affects the efficiency of the optimization process. The paper proposes a novel task mapping algorithm of the parallel metaheuristic algorithm to the GPU architecture, describes problem statement for the mapping of algorithm graph model to the GPU model, and gives a formal definition of graph mapping and mapping restrictions. The algorithm graph model is a hierarchical graph model consisting of island parallel model and metaheuristic optimization algorithm model. A set of feasible mappings using mapping restrictions makes it possible to formalize GPU architecture and parallel model features. The structural mapping algorithm is based on cooperative solving of the optimization problem and the discrete optimization problem of the structural model mapping. The study outlines the parallel efficiency criteria which can be evaluated both experimentally and analytically to predict a model efficiency. The experimental section introduces the parallel optimization algorithm based on the proposed structural mapping algorithm. Experimental results for parallel efficiency comparison between parallel and sequential algorithms are presented and discussed

Assessment of Reliability of the Transport Information Systems

In this paper is given the definition of e reliability of a transport information system, with features of transport information systems listed. Equations for calculating reliability indicators are given, as well. The developed hierarchical graph model for assessing the reliability of transport information systems and an algorithm for analyzing the graph model, which allows taking into account the specifics of the reliability of the operation of system elements at different levels of the hierarchy, are presented. Using the graph model and the graph model analysis algorithm, it is possible to reasonably predict the strategy for the creation and development of transport information systems.

Hierarchical Distribution Network Topology Formulation and Dimensionality Reduction Using Homeomorphism Transformation

The scales of the power distribution networks in real-world power grids expand quickly while the network structures are becoming more and more complex. The power grid companies analyze the power distribution networks in different business scenarios with different topology models. In this work, we propose a hierarchical graph model to describe the medium-voltage distribution network (which is a typical power distribution network in power grids) based on homeomorphic transformation. The hierarchical graph model preserves the basic network topology described by the traditional Common Information Model (CIM). Firstly, the nodes in the distribution network topology are classified according to graph theory. Secondly, three typical business scenarios of distribution network topology analysis are summarized, and the original model is simplified by progressive dimensionality reduction method to meet the analysis requirements of different scenarios, the simplified method consists of three abstract levels: critical path, core path and minimal path, and can effectively reduce the space complexity of the model while maintaining the topological properties. Thirdly, a multi-level distribution network topology construction and mapping method based on the graph database is proposed. It is used to realize the rapid conversion and traceability between different levels of topology. Finally, a practical distribution network in a county is used as an example to verify the effectiveness of the proposed method in the aspects such as topology rendering and path searching. The evaluation indicates that the proposed model can visualize the distribution network intuitively. The model can also speed up the visualization and path searching significantly.

Attention-Based Hierarchical Random Graph Model for Structural Inference of Real-World Networks

Attention-Based Hierarchical Random Graph Model for Structural Inference of Real-World Networks

Methods and Tools for Visualization of Graphs and Graph Algorithms

Graphs are the most common abstract structure encountered in computer science and are widely used for structural information visualization. In the paper, we consider practical and general graph formalism of so called hierarchical graphs and present the Higres and ALVIS systems aimed at supporting of structural information visualization on the base of hierarchical graph models.

A circular layout algorithm for attributed hierarchical graphs with ports

Information visualization based on graph models is a key component of support tools for many applications in science and engineering. The Visual Graph system is intended for visualization of big amounts of complex information on the basis of attributed hierarchical graph models. In this paper, a circular layout algorithm for attributed hierarchical graphs with ports and its effective implementation in the Visual Graph system are presented.

A Three-Level Hierarchical Graph Model for Conflict Resolution

A novel hierarchical graph model is developed to analyze conflicts interrelated on three levels. As an extension of the two-level hierarchical graph model, this new structure contains several smaller graph models, called local graphs, nested at three levels. Decision makers (DMs), states, moves, and preference relations in the three-level hierarchical graph model (3LHGM) are defined. The interrelationships between stabilities in local graph models and the overall graph model are investigated and utilized in developing algorithms to calculate stabilities in the hierarchical graph model. This novel methodology is then illustrated using a generic model of hierarchical climate change governance disputes. Stability calculations can uncover profitable courses of action. The 3LHGM aims to provide insightful resolutions for DMs with a broader vision of the hierarchical conflict they are participating in.

The method of inductive synthesis of hierarchical knowledge graphs of telecommunication networks based on statistical data

The article is devoted to the development of the method for synthesizing hierarchical knowledge graphs of telecommunication networks using network statistical data and available models or their fragments. Existing graphs or their fragments, monitoring data, descriptions of data structures, reverse engineering artifacts, etc. can be used as initial data. The paper presents analysis of relevance of the problem for telecommunication network operators and considers the advantages of the proposed method of inductive synthesis for building integrated network models. The method allows restore hierarchical graph models from source data, including the elements of the models and the relations between them. Based on extracted elements and relations hierarchical knowledge graphs are build. The mathematical models and algorithms that describe the considered method for synthesizing hierarchical knowledge graphs and quality indicators of the resulting model are presented in the paper. To confirm the method, the program was developed. This program uses a sample of initial data as a source, restores an integral fragment of a hierarchical knowledge graph of a telecommunication network. Also, as results of this work, the areas of its application and directions for further research are formulated.

Differential Privacy for Evolving Network Based on GHRG

Releasing evolving networks which contain sensitive information could compromise individual privacy. In this paper, we study the problem of releasing evolving networks under differential privacy. We explore the possibility of designing a differentially private evolving networks releasing algorithm. We found that the majority of traditional methods provide a snapshot of the networks under differential privacy over a brief period of time. As the network structure only changes in local part, the amount of required noise entirely is large and it leads to an inefficient utility. To this end, we propose GHRG-DP, a novel differentially private evolving networks releasing algorithm which reduces the noise scale and achieves high data utility. In the GHRG-DP algorithm, we learn the online connection probabilities between vertices in the evolving networks by generalized hierarchical random graph (GHRG) model. To fit the dynamic environment, a dendrogram structure adjusting method in local areas is proposed to reduce the noise scale in the whole period of time. Moreover, to avoid the unhelpful outcome of the connection probabilities, a Bayesian noisy probabilities calculating method is proposed. Through formal privacy analysis, we show that the GHRG-DP algorithm is ε -differentially private. Experiments on real evolving network datasets illustrate that GHRG-DP algorithm can privately release evolving networks with high accuracy.

Attack Graph-Based Moving Target Defense in Software-Defined Networks

Moving target defense (MTD) has emerged as a proactive defense mechanism aiming to thwart a potential attacker. The key underlying idea of MTD is to increase uncertainty and confusion for attackers by changing the attack surface (i.e., system or network configurations) that can invalidate the intelligence collected by the attackers and interrupt attack execution; ultimately leading to attack failure. Recently, the significant advance of software-defined networking (SDN) technology has enabled several complex system operations to be highly flexible and robust; particularly in terms of programmability and controllability with the help of SDN controllers. Accordingly, many security operations have utilized this capability to be optimally deployed in a complex network using the SDN functionalities. In this paper, by leveraging the advanced SDN technology, we developed an attack graph-based MTD technique that shuffles a host’s network configurations (e.g., MAC/IP/port addresses) based on its criticality, which is highly exploitable by attackers when the host is on the attack path(s). To this end, we developed a hierarchical attack graph model that provides a network’s vulnerability and network topology, which can be utilized for the MTD shuffling decisions in selecting highly exploitable hosts in a given network, and determining the frequency of shuffling the hosts’ network configurations. The MTD shuffling with a high priority on more exploitable, critical hosts contributes to providing adaptive, proactive, and affordable defense services aiming to minimize attack success probability with minimum MTD cost. We validated the out performance of the proposed MTD in attack success probability and MTD cost via both simulation and real SDN testbed experiments.

Early Change Detection in Dynamical Bearing Degradation Process Based on Hierarchical Graph Model and Adaptive Inputs Weighting Fusion

Early detection of changes, that are from normal to abnormal in dynamical degradation process, is essential to enhance the function of bearings during their long-term service time. This article presents a new and practical method for this problem based on hierarchical graph model (HGM) coupled with adaptive inputs weighting (AIW) fusion. Hierarchical decomposition (HD) is first adopted on the raw vibration signal, such that the inspection of bearing health condition can be allowed over different multiple scales. HGMs are then constructed from each decomposed components that are resulted from HD. AIW is employed to aggregate all constructed HGMs by taking into account their difference in characterization of dynamical bearing health condition. A common hypothesis testing is finally adopted to make decision. Experiments are conducted on two publicly available datasets. Results demonstrate the effectiveness of the proposed method; meanwhile, comparisons with benchmarking methods suggest its good potential in real applications.

A Two-Level Hierarchical Graph Model for Conflict Resolution with Application to International Climate Change Negotiations

A novel two-level hierarchical graph model is developed to analyze international climate change negotiations with hierarchical structures: the negotiations take place between two nations and between each nation and its provincial governments. The two national government are two decision makers at the top level. Within each nation, the two provincial governments negotiate with the national government at the lower level. The theoretical structure of this novel model, including decision makers, options, moves, and preference relations, are developed. The interrelationship between the stabilities in the two-level hierarchical graph model and the stabilities in local models are investigated by theorems. These theorems can be utilized to calculate complete stabilities in the two-level hierarchical graph model when the stabilities in local graph models are known. The international climate change negotiations as the illustrative example is then investigated in detail. The extra equilibrium, uniquely obtained by this novel methodology, suggests that opposition may still be from one provincial government when the national government does not sign the international climate agreement and implements existing environmental laws. Compared with other approaches, this novel methodology is an effective and flexible tool in analyzing hierarchical conflicts at two levels by providing decision makers with strategic resolutions with broader vision.

Mining Keywords from Short Text Based on LDA-Based Hierarchical Semantic Graph Model

Extracting keywords from a text set is an important task. Most of the previous studies extract keywords from a single text. Using the key topics in the text collection, the association relationship between the topic and the topic in the cross-text, and the association relationship between the words and the words in the cross-text has not played an important role in the previous method of extracting keywords from the text collection. In order to improve the accuracy of extracting keywords from text collections, using the semantic relationship between topics and topics in texts and highlighting the semantic relationship between words and words under the key topics, this article proposes an unsupervised method for mining keywords from short text collections. In this method, a two level semantic association model is used to link the semantic relations between topics and the semantic relations between words, and extract the key words based on the combined action. First, the text is represented with LDA; the authors used word2vec to calculate the semantic association between topic and topic, and build a semantic relation graph between topics, that is the upper level graph, and use a graph ranking algorithm to calculate each topic score. In the lower layer, the semantic association between words and words is calculated by using the topic scores and the relationship between topics in the upper network allow a graph to be constructed. Using a graph sorting algorithm sorts the words in short text sets to determine the keywords. The experimental results show that the method is better for extracting keywords from the text set, especially in short articles. In the text, the important topics, the relationship between topics and the correlation between words can improve the accuracy of extracting keywords from the text set.

A hierarchical spatial-temporal graph-kernel for high-resolution SAR image change detection

ABSTRACTEffective utilization of structural information is important for high-resolution synthetic aperture radar (SAR) image change detection. For comprehensively utilizing the local and global structures in SAR images, a hierarchical spatial-temporal graph kernel (STGK) method is proposed in this paper for high-resolution SAR image change detection. First, the bi-temporal hierarchical graph models are constructed for extracting the local-global structures in the bi-temporal SAR images. Then, a STGK function, which measures the spatial and temporal similarities between the local-global structures, is constructed for indicating the change levels between the bi-temporal images. Finally, a support vector machine (SVM) is implemented with the STGK function for producing the final change detection results. Experimental results on real GaoFen-3 SAR data sets demonstrate the effectiveness of the proposed method, and prove that the STGK method is capable of detecting changed areas with relatively complex structures.