Random Walk Algorithm Research Articles

A complex network community detection algorithm based on random walk and label propagation

AbstractThe community structure is proving to have a very important role in the understanding of complex networks, but discovering them remains a very difficult problem despite the existence of several methods. In this article, we propose a novel algorithm for discovering communities in complex networks based on a modified random walk (RW) and label propagation algorithm (LPA). First, we calculate the similarity between nodes based on the new formula of RW. Then, the labels are propagated by the obtained similarity of the first step using LPA. Finally, the third step will be a new measure to find the optimal partitioning of communities. Experimental results obtained on several real and synthetic networks reveal that our algorithm outperforms existing methods in finding communities.

A new analytical solution of contaminant transport along a single fracture connected with porous matrix and its time domain random walk algorithm

In this work, a new solution is developed for the problem of contaminant transport in a single fracture-matrix system, where the first-order reaction rate constants are different in both fracture and matrix. It takes a form of convolution with three functions as a basis to consider different transport mechanisms separately. The statistical nature of the three functions, as well as the interpretation of the solution as a marginal probability distribution in the case of no first-order reactions, allows us to develop a simple Time Domain Random Walk (TDRW) algorithm to calculate the breakthrough curves at a given point of observation downstream the fracture. Compared with the existing versions of the TDRW algorithm, it is superior not only in the physical reasoning and statistical interpretations but also in its numerical implementations. In addition, the developed algorithm can not only be used to estimate the distribution profile of the contaminant concentration along the fracture but also the concentration within the matrix, since the analytical solution to contaminant concentration in the matrix also takes a convolution form of three functions. Also, the distribution profile of contaminant concentration within the matrix can readily be determined by the use of our TDRW algorithm. To validate the developed algorithm, three benchmark cases are considered for either nuclide or colloid transport through a fractured rock. The results show that TDRW algorithm is superior to the Gaussian quadrature solution, but similar to inverse Laplace transform solution, in computational expense when nearly identical results are obtained. However, the Monte Carlo nature of the TDRW algorithm implies that the accuracy of the computational result is related to the number of particles applied in the simulation, which might make the obtained results fluctuated.

Design and implementation of a cost effective vacuum cleaner robot

Robot vacuum cleaners are among the first service robots to enter daily life. However, robot vacuum cleaners are currently inaccessible to many due to their high cost. In order for these robots to become widespread, they must be cheap and functional. In this study, the design and production of a low-cost, high-performance vacuum cleaner robot that can be controlled by smartphones is presented. This autonomous robot moves around the obstacles, vacuums the dust from the floor, performs basic navigation and also it is manually controllable. Designing of a vacuum cleaner robot is divided into three parts which are the mechanical, electrical and software design. In the mechanical design; the robot chassis and cleaning system have been designed and realized. In the electrical design; the electrical components such as batteries, motors, drivers, sensors and microcontroller have been selected and the power system has been designed by considering electrical requirements. In the software design; an easy to use Android application for remote control has been created, autonomous algorithms such as random walk and snake algorithms have been designed. Remote controlling of the robot has been provided by using Bluetooth connection between the Raspberry Pi and Android smartphone.

Identifying common driver modules by equilibrating coverage and mutual exclusivity across pan-cancer data

It is of significance to identify common driver modules from pan-cancer data to interpret heterogeneity of cancer, and the accumulated omic data have made it into reality. In this paper, the pan-cancer common driver module identification problem is formulated, which takes the frequency difference among various cancers into account. For solving this problem, a K-nearest neighbors based imputation algorithm KNNImp is firstly devised to infer the variation values for some potential significant missing genes. Secondly, a harmonic mean of coverage and mutual exclusivity based random walk algorithm HMCEwalk is proposed. It weights the integrated PPI network with the harmonic mean of gene coverage scores and mutual exclusion scores among various cancer types, and extracts modules through a random walk process. Experiments were implemented on both simulated data and real biological data. The experimental results on simulated data indicate that given two types of cancers, the HMCEwalk algorithm has a stronger tendency to identify a set of modules which not only mutate in a large proportion of samples of these cancers, but have close proportion of mutated samples for each cancer. The experimental results on biological data show that the presented imputation algorithm does play roles in regaining some important cancer related genes. In comparison with two state-of-the-art identification methods MEXCOwalk and DriveWays, the presented one exhibits competitive performance in most instances in terms of revealing the known cancer genes, producing modules having satisfied coverage and mutual exclusivity for each cancer. Many detected modules engage in the known cancer-related biological pathways. In addition, the presented method does recognize many cancer-associated genes omitted by methods MEXCOwalk and DriveWays.

Using data mining as a tool for anomaly detection in food safety audit data

The integrity of third-party food safety audits has been constantly challenged by food safety incidents of certified food businesses. Integrity programs have been established for food safety certification program owners (CPOs) to monitor the involved parties’ performance and find anomalies in audit data. To find such anomalies in a large amount of data is labour intensive, and no standard approach has been established. This paper explored data mining approaches and leveraged algorithms to automate integrity checks. Furthermore, this paper provides initial validation of a suitable algorithm. Out of three potentially suitable algorithms, the couple-biased random walk (CBRW) algorithm was chosen as the basic algorithm to find anomalies in audit data. This algorithm was adjusted and expanded to show contributing factors for a potential anomaly enabling integrity managers to find the reason for potential anomalies faster. Three experts validated the sample findings of the algorithm and discussed these findings in detail. The validation showed an 80% accuracy of the algorithm and brought up findings that were not known before by the experts. The findings justify further exploration of data mining for anomaly detection in food safety audits.

Efficient Techniques for Handling Large Nets in Capacitance Extraction Based on Floating Random Walk

An approach for fast generation of virtual Gaussian surface in floating random walk algorithm is proposed, which resolves the difficulty of performing capacitance extraction on large wire nets including over one hundred thousand conductor blocks. Firstly, computational complexity of the existing virtual Gaussian surface sampling technique is analyzed. Based on it, techniques are proposed to improve the distance calculation among conductor blocks and the intersection calculation of block Gaussian surfaces. The techniques are based on space management and reduce the computational time for generating the Gaussian surfaces, achieving better performance than the approach based on parallel computing. The efficiency and correctness of the proposed approach is verified with the experiments carried out on a server with 32 cores. The experimental results show that, for a large structure including about one million conductor blocks the proposed approach accelerates generating the Gaussian surface for 372 times, which is much better than the acceleration brought by paralleling computing.

Characterization and Validation of ceRNA-Mediated Pathway-Pathway Crosstalk Networks Across Eight Major Cardiovascular Diseases.

Pathway analysis is considered as an important strategy to reveal the underlying mechanisms of diseases. Pathways that are involved in crosstalk can regulate each other and co-regulate downstream biological processes. Furthermore, some genes in the pathways can function with other genes via the relationship of the competing endogenous RNA (ceRNA) mechanism, which has also been demonstrated to play key roles in cellular biology. However, the comprehensive analysis of ceRNA-mediated pathway crosstalk is lacking. Here, we constructed the landscape of the ceRNA-mediated pathway–pathway crosstalk of eight major cardiovascular diseases (CVDs) based on sequencing data from ∼2,800 samples. Some common features shared by numerous CVDs were uncovered. A fraction of the pathway–pathway crosstalk was conserved in multiple CVDs and a core pathway–pathway crosstalk network was identified, suggesting the similarity of pathway–pathway crosstalk among CVDs. Experimental evidence also demonstrated that the pathway crosstalk was functioned in CVDs. We split all hub pathways of each pathway–pathway crosstalk network into three categories, namely, common hubs, differential hubs, and specific hubs, which could highlight the common or specific biological mechanisms. Importantly, after a comparison analysis of the hub pathways of networks, ∼480 hub pathway-induced common modules were identified to exert functions in CVDs broadly. Moreover, we performed a random walk algorithm on the hub pathway-induced sub-network and identified 23 potentially novel CVD-related pathways. In summary, our study revealed the potential molecular regulatory mechanisms of ceRNA crosstalk in pathway–pathway crosstalk levels and provided a novel routine to investigate the pathway–pathway crosstalk in cardiology. All CVD pathway–pathway crosstalks are provided in http://www.licpathway.net/cepathway/index.html.

Scenic Tourist Route Planning Algorithm Based on Mobile Computing and Grey Entropy Decision Model

Since the concept of “Smart Earth” was put forward, countries have attached great importance to the research and introduction of smart tourism. With the increasing updating of information technology, the tourism industry is also constantly trying to transform intelligently, and the personalized recommendation of its travel routes has become the most important development in smart tourism, one of the hot topics. Tourism agencies create traditional travel itineraries based on the preferences of the majority of people and the characteristics of tourist attractions. These lines are primarily used for team travel. People’s expectations for travel route planning are increasing as their living standards rise, and the traditional route planning algorithm based on a static road network data model has struggled to describe the complex and changing scenic environment. The improved random walk algorithm suggests suitable travel routes to users, allowing for more accurate route recommendations and effectively addressing the problem of new route recommendation difficulty. This paper proposes a scenic tourist route planning algorithm using the grey entropy decision-making model and mobile computing. The content of the recommended results is more closely related to the target recommendation user’s tendency after personalized adjustment, which has a better effect on the personalization of the recommended results.

Study on Identification Method of Pulmonary Nodules: Improved Random Walk Pulmonary Parenchyma Segmentation and Fusion Multi-Feature VGG16 Nodule Classification.

PurposeThe purpose of this study was to realize automatic segmentation of lung parenchyma based on random walk algorithm to ensure the accuracy of lung parenchyma segmentation. The explicable features of pulmonary nodules were added into VGG16 neural network to improve the classification accuracy of pulmonary nodules.Materials and MethodsLIDC-IDRI, a public dataset containing lung Computed Tomography images/pulmonary nodules, was used as experimental data. In lung parenchyma segmentation, the maximum Between-Class Variance method (OTSU), corrosion and expansion methods were used to automatically obtain the foreground and background seed points of random walk algorithm in lung parenchyma region. The shortest distance between point sets was added as one of the criteria of prospect probability in the calculation of random walk weight function to achieve accurate segmentation of pulmonary parenchyma. According to the location of the nodules marked by the doctor, the nodules were extracted. The texture features and grayscale features were extracted by Volume Local Direction Ternary Pattern (VLDTP) method and gray histogram. The explicable features were input into VGG16 network in series mode and fused with depth features to achieve accurate classification of nodules. Intersection of Union (IOU) and false positive rate (FPR) were used to measure the segmentation results. Accuracy, Sensitivity, Specificity, Accuracy and F1 score were used to evaluate the results of nodule classification.ResultsThe automatic random walk algorithm is effective in lung parenchyma segmentation, and its segmentation efficiency is improved obviously. In VGG16 network, the accuracy of nodular classification is 0.045 higher than that of single depth feature classification.ConclusionThe method proposed in this paper can effectively and accurately achieve automatic segmentation of lung parenchyma. In addition, the fusion of multi-feature VGG16 network is effective in the classification of pulmonary nodules, which can improve the accuracy of nodular classification.

ALTERNATIVE ROBUST ESTIMATORS FOR PARAMETERS OF THE LINEAR REGRESSION MODEL

This paper considers parameter estimation of the linear regression model with Ramsay-Novick (RN) distributed errors, focusing on its use as an aid to robustness. Positioning within the class of heavy tailed distributions, RN distribution can be defined as the modification of unbounded influence function of a non-robust density so that it has more resistance to outliers. Potential use of this robust density have so far been assessed in Bayesian settings on real data examples and there is a lack of performance assessment for finite samples in classical approach. This study therefore explores its robustness properties when used as error distribution in comparison to normal as well as other alternating heavy-tailed distributions like Laplace and Student-t. An extensive simulation study was conducted for this purpose under different settings of sample size, model parameters and outlier percantages. An efficient data generation of this distribution through random-walk Metropolis algorithm is here also suggested. The results were supported by a real world application on famously known as Brownlee’s stack loss plant data.

A UAV Visual Relocalization Method Using Semantic Object Features Based on Internet of Things

Unmanned Air Vehicle (UAV) has the advantages of high autonomy and strong dynamic deployment capabilities. At the same time, with the rapid development of the Internet of Things (IoT) technology, the construction of the IoT based on UAVs can break away from the traditional single-line communication mode of UAVs and control terminals, which makes the UAVs more intelligent and flexible when performing tasks. When using UAVs to perform IoT tasks, it is necessary to track the UAVs’ position and pose at all times. When the position and pose tracking fails, relocalization is required to restore the current position and pose. Therefore, how to perform UAV relocalization accurately by using visual information has attracted much attention. However, the complex changes in light conditions in the real world have brought huge challenges to the visual relocalization of UAV. Traditional visual relocalization algorithms mostly rely on artificially designed low-level geometric features which are sensitive to light conditions. In this paper, oriented to the UAV-based IoT, a UAV visual relocalization method using semantic object features is proposed. Specifically, the method uses YOLOv3 as the object detection framework to extract the semantic information in the picture and uses the semantic information to construct a topological map as a sparse description of the environment. With prior knowledge of the map, the random walk algorithm is used on the association graphs to match the semantic features and the scenes. Finally, the EPnP algorithm is used to solve the position and pose of the UAV which will be returned to the IoT platform. Simulation results show that the method proposed in this paper can achieve robust real-time UAVs relocalization when the scene lighting conditions change dynamically and provide a guarantee for UAVs to perform IoT tasks.

Application of random walk algorithm into finite element numerical groundwater model for capture zone depiction

Application of random walk algorithm into finite element numerical groundwater model for capture zone depiction

Biased random walkers and extreme events on the edges of complex networks

Extreme events have low occurrence probabilities and display pronounced deviation from their average behavior, such as earthquakes or power blackouts. Such extreme events occurring on the nodes of a complex network have been extensively studied earlier through the modeling framework of unbiased random walks. They reveal that the occurrence probability for extreme events on nodes of a network has a strong dependence on the nodal properties. Apart from these, a recent work has shown the independence of extreme events on edges from those occurring on nodes. Hence, in this work, we propose a more general formalism to study the properties of extreme events arising from biased random walkers on the edges of a network. This formalism is applied to biases based on a variety network centrality measures including PageRank. It is shown that with biased random walkers as the dynamics on the network, extreme event probabilities depend on the local properties of the edges. The probabilities are highly variable for some edges of the network, while they are approximately a constant for some other edges on the same network. This feature is robust with respect to different biases applied to the random walk algorithm. Further, using the results from this formalism, it is shown that a network is far more robust to extreme events occurring on edges when compared to those occurring on the nodes.

A computational framework for biomaterials containing three-dimensional random fiber networks based on the affine kinematics.

Understanding the structure-function relationship of biomaterials can provide insights into different diseases and advance numerous biomedical applications. This paper presents a finite element-based computational framework to model biomaterials containing a three-dimensional fiber network at the microscopic scale. The fiber network is synthetically generated by a random walk algorithm, which uses several random variables to control the fiber network topology such as fiber orientations and tortuosity. The geometric information of the generated fiber network is stored in an array-like data structure and incorporated into the nonlinear finite element formulation. The proposed computational framework adopts the affine fiber kinematics, based on which the fiber deformation can be expressed by the nodal displacement and the finite element interpolation functions using the isoparametric relationship. A variational approach is developed to linearize the total strain energy function and derive the nodal force residual and the stiffness matrix required by the finite element procedure. Four numerical examples are provided to demonstrate the capabilities of the proposed computational framework, including a numerical investigation about the relationship between the proposed method and a class of anisotropic material models, a set of synthetic examples to explore the influence of fiber locations on material local and global responses, a thorough mesh-sensitivity analysis about the impact of mesh size on various numerical results, and a detailed case study about the influence of material structures on the performance of eggshell-membrane-hydrogel composites. The proposed computational framework provides an efficient approach to investigate the structure-function relationship for biomaterials that follow the affine fiber kinematics.

Two-stage superstructure model for optimization of distributed energy systems (DES) part I: Model development and verification

The optimization of distributed energy systems (DES) is challenging because of the diversity of the types of energies involved and the complexity of the structure. Mathematically, the optimization of DES is a mixed-integer non-linear programming problem (MINLP). The optimal tradeoff between precision and computational efficiency, to find the global optimal solution, is a core issue that needs to be solved. This present work proposes a two-stage superstructure model which is solved by the random walk algorithm with compulsive evolution (RWCE), to better approximate the global optimal solution of the MINLP. The paper is divided into two parts; the first focuses on the modeling methodology and model solving strategy. Moreover, to confirm the applicability and effectiveness of the recommended method, DESs for three case studies, i.e., business park, residential building, and hotel, were optimized from system planning point of view. On comparison with literatures, it was found that the proposed method had positive effects on further improving the economy of the system at different scales and configurations. The resulting decrease in the total annual cost of the three systems was 12%, 36%, and 2%, respectively. Further research on system operation optimization will be published as the second part of this paper.

Parallel optimization route promoted by accepting imperfect solutions for the global optimization of heat exchanger networks

The global optimization of heat exchanger network synthesis remains a hotspot and challenge in the field of chemical process integration. The random walk algorithm with compulsive evolution has shown good performance in avoiding entrapment in the local optima by accepting imperfect solutions with a certain mutation probability. This work first investigates the detailed effects of accepting imperfect solutions on the continuity of optimization and the efficiency of global optimization by analyzing the imperfect solutions generated in the optimization process. Then, a novel parallel optimization route is established to balance the global and local search ability by combing basic and fine-search optimization levels. Additionally, enhancing strategies promoted by accepting imperfect solutions and large step lengths are integrated into the parallel optimization route to further improve the efficiency of structure evolution and global optimization. Finally, three case studies are presented to verify the effectiveness of the proposed method and discuss the roles of each module and different module combinations in facilitating the global search. Many promising results with more structure possibilities are obtained in each case study, with the obtained optimal solutions being lower than most reported in the literature. This indicates the effectiveness of the proposed method in facilitating the structure evolution and global optimization for heat exchanger network synthesis.

An efficient strategy for identifying essential proteins based on homology, subcellular location and protein-protein interaction information.

High throughput biological experiments are expensive and time consuming. For the past few years, many computational methods based on biological information have been proposed and widely used to understand the biological background. However, the processing of biological information data inevitably produces false positive and false negative data, such as the noise in the Protein-Protein Interaction (PPI) networks and the noise generated by the integration of a variety of biological information. How to solve these noise problems is the key role in essential protein predictions. An Identifying Essential Proteins model based on non-negative Matrix Symmetric tri-Factorization and multiple biological information (IEPMSF) is proposed in this paper, which utilizes only the PPI network proteins common neighbor characters to develop a weighted network, and uses the non-negative matrix symmetric tri-factorization method to find more potential interactions between proteins in the network so as to optimize the weighted network. Then, using the subcellular location and lineal homology information, the starting score of proteins is determined, and the random walk algorithm with restart mode is applied to the optimized network to mark and rank each protein. We tested the suggested forecasting model against current representative approaches using a public database. Experiment shows high efficiency of new method in essential proteins identification. The effectiveness of this method shows that it can dramatically solve the noise problems that existing in the multi-source biological information itself and cased by integrating them.

Characterization of porous media by nuclear magnetic resonance beyond fast diffusion limit with numerical simulation

Nuclear magnetic resonance logging is an important formation detection technique in the petroleum industry, which is always used to detect formation pore structure and fluid identification. However, conventional nuclear magnetic resonance characterization methods assume that the formation pore medium are in fast diffusion regime. In complex formations, non-fast diffusion regimes (intermediate diffusion and slow diffusion) are also present. In the previous paper [13], a new method for characterizing porous media with non-fast diffusion regime and uniform pore size distribution by T2-T2 pulse sequence is proposed. In this research, the T2-T2 signals of periodic stacked pore models beyond fast diffusion regime are numerically simulated by the random walk algorithm, and the pore size - surface relaxivity maps are respectively inverted, the relationship between pore structure and correlation maps are discussed. The results provide theoretical foundation for characterizing pore structure beyond fast diffusion limit.

Quantum reinforcement learning method and application based on value function

The Multi-Arm bandit(MAB) problem is a classical problem in the field of reinforcement learning with only one state. The Grid problem is a multi-state problem for reinforcement learning. In this work, we focus on how to combine the classical value function method to quantum computation, and we propose three novel quantum reinforcement learning(QRL) algorithms for the MAB problem and one novel QRL algorithm, which is combined with the quantum random walk and Grover algorithm, for the Grid problem. From the experiments, the learning process is speed-up by combining the value function with quantum computation.

Hierarchical Random Walker Segmentation for Large Volumetric Biomedical Images.

The random walker method for image segmentation is a popular tool for semi-automatic image segmentation, especially in the biomedical field. However, its linear asymptotic run time and memory requirements make application to 3D datasets of increasing sizes impractical. We propose a hierarchical framework that, to the best of our knowledge, is the first attempt to overcome these restrictions for the random walker algorithm and achieves sublinear run time and constant memory complexity. The goal of this framework is- rather than improving the segmentation quality compared to the baseline method- to make interactive segmentation on out-of-core datasets possible. The method is evaluated quantitatively on synthetic data and the CT-ORG dataset where the expected improvements in algorithm run time while maintaining high segmentation quality are confirmed. The incremental (i.e., interaction update) run time is demonstrated to be in seconds on a standard PC even for volumes of hundreds of gigabytes in size. In a small case study the applicability to large real world from current biomedical research is demonstrated. An implementation of the presented method is publicly available in version 5.2 of the widely used volume rendering and processing software Voreen (https://www.uni-muenster.de/Voreen/).