Random Walk Algorithm Research Articles

Hyperspectral Anomaly Detection With Morphological Random Walker

This work proposes a new morphological random walker (MRW) method for hyperspectral anomaly detection. The proposed method introduces a morphology-based objective function into a random walker (RW) algorithm, sufficiently exploiting spatial morphological property and spatial similarity of HSIs for detection. The MRW method comprises two major stages. Firstly, we employ the extended morphological profiles (EMPs) and different operations to extract the spatial morphological property of HSIs. Second, according to the morphological property, we construct a morphology-based objective function. This function is incorporated into the RW-based optimization model, encoding the spatial similarity of HSIs in a weighted graph. Two factors determine the class of test pixels, including the spatial morphological information learned by EMPs, and the spatial correlation among adjoining pixels modeled by the weighted graph. Since the two factors are well considered in the MRW method, the proposed method illustrates outstanding detection performances for several widely used real HSIs.

Implementation of quantum random walk on a real quantum computer

A random walk is one of the widely adopted random processes for simulation and approximation in multiple areas of science and engineering. Quantum random walk is an analog version of the classical random walk. It was first introduced in 1993 by Y. Aharonov et al. They presented that, with the nature of quantum characteristic, the average length of the walking path on a line is possible to larger than a classical random walk can produce. Therefore, the quantum random walk can be used as a tool to construct many other quantum algorithms. Moreover, it can solve graph problems that many real-world problems can be formulated. However, quantum algorithms are only useful in practice if we can implement them efficiently on a quantum computer. In this study, we are then interested in designing and implementing a quantum circuit that can be run on a real quantum computing device. We firstly focus on the most straightforward, which is a one-dimension, quantum random walk algorithm. Then, the quantum circuits are developed and implemented on a real quantum computer and a quantum computing simulator using software development kits provided by IBM. Lastly, the performance and results of the quantum circuits tested on both computing platforms are presented.

Density Sensitive Random Walk for Local Community Detection

Given a network, local community detection aims at finding the community that contains a set of query nodes (seed nodes). Random walk (RW) based algorithms have shown great success in various local community detection scenarios. Starting from the seed nodes, RW based algorithms continuously sample random walk paths to get the clustering result. However, current RW based algorithms for local clustering are faced with the following two problems. The random walker is insensitive to the community boundary and might have an unbalanced walk. These problems would have a negative effect on the clustering result of RW based algorithms. In this paper, we propose a density sensitive random walk algorithms (DSRW) for local community detection. By integrating the graph density information into the random walk process, the problems are resolved and the clustering result is improved. We provide the convergence proof of DSRW algorithm and perform extensive experiments on both real and synthetic datasets. Results show that our DSRW algorithm has achieved the state-of-the-art result in most scenarios.

MNBDR: A Module Network Based Method for Drug Repositioning.

Drug repurposing/repositioning, which aims to find novel indications for existing drugs, contributes to reducing the time and cost for drug development. For the recent decade, gene expression profiles of drug stimulating samples have been successfully used in drug repurposing. However, most of the existing methods neglect the gene modules and the interactions among the modules, although the cross-talks among pathways are common in drug response. It is essential to develop a method that utilizes the cross-talks information to predict the reliable candidate associations. In this study, we developed MNBDR (Module Network Based Drug Repositioning), a novel method that based on module network to screen drugs. It integrated protein–protein interactions and gene expression profile of human, to predict drug candidates for diseases. Specifically, the MNBDR mined dense modules through protein–protein interaction (PPI) network and constructed a module network to reveal cross-talks among modules. Then, together with the module network, based on existing gene expression data set of drug stimulation samples and disease samples, we used random walk algorithms to capture essential modules in disease development and proposed a new indicator to screen potential drugs for a given disease. Results showed MNBDR could provide better performance than popular methods. Moreover, functional analysis of the essential modules in the network indicated our method could reveal biological mechanism in drug response.

Identifying the essential nodes in network pharmacology based on multilayer network combined with random walk algorithm

Compared with the general complex network, the multilayer network is more suitable for the description of reality. It can be used as a tool of network pharmacology to analyze the mechanism of drug action from an overall perspective. Combined with random walk algorithm, it measures the importance of nodes from the entire network rather than a single layer. Here a four-layer network was constructed based on the data about the action process of prescriptions, consisting of ingredients, target proteins, metabolic pathways and diseases. The random walk algorithm was used to calculate the betweenness centrality of the protein layer nodes to get the rank of their importance. According to above method, we screened out the top 10% proteins that play a key role in treatment. Prescriptions Xiaochaihu Decoction was taken as example to prove our method. The selected proteins were measured with the ones that have been validated to be associated with the treated diseases. The results showed that its accuracy was no less than the topology-based method of single-layer network. The applicability of our method was proved by another prescription Yupingfeng Decoction. Our study demonstrated that multilayer network combined with random walk algorithm was an effective method for pre-screening vital target proteins related to prescriptions.

A Three-Dimensional Random Walk Algorithm for Estimating the Chloride Diffusivity of Concrete.

The chloride diffusivity of concrete is an important parameter for assessing the long-term durability of coastal concrete structures. The purpose of this paper is to present a three-dimensional random walk algorithm (RWA) for estimating the chloride diffusivity of concrete. By analyzing the size distribution of aggregates, the equivalent interfacial transition zone (ITZ) thickness is derived in an analytical manner. Each aggregate is combined with the surrounding ITZ to construct an equivalent aggregate model (EAM) and the chloride diffusivity is formulated. It is found that the equivalent ITZ thickness decreases with the increase of practical ITZ thickness and aggregate volume fraction. The aggregate gradation influences the equivalent ITZ thickness to a certain extent. The relative chloride diffusivity of the equivalent aggregate is almost directly and inversely proportional to the equivalent ITZ thickness and the aggregate radius, respectively. The numerical results show that, when the EAM is adopted, the computational time is greatly reduced. With the EAM, concrete can be modeled as a two-phase material and the chloride diffusivity is estimated by applying the RWA. It is shown that, with the increase of mean square displacement and number of Brownian particles, the average chloride diffusivity of concrete approaches a stable value. Finally, through comparison with experimental data, the validation of the RWA is preliminarily verified.

Fine-Tuning the Optical Design of the Advanced Virgo+ Gravitational-Wave Detector Using Binary-Neutron Star Signals

Advanced Virgo+ is a major upgrade of the Advanced Virgo gravitational-wave detector aiming to increase sensitivity in terms of binary neutron star (BNS) range by a factor 3–5 in the next few years. In this work, we present an optimization of the mirror transmittances for the second phase of the project (to be implemented for the O5 observation run) using a random walk algorithm implemented with the advGWINC software. In addition to BNS range, a post merger (PM) SNR is also used as a figure of merit to identify configurations that fine-tune the sensitivity curve, as a function of arm-cavity round trip losses.

A New Nodes-Based Model for Optimization of Heat Exchanger Network Synthesis

Searching the global optimal value in heat exchanger network synthesis (HENS) becomes more difficult along with the increasing scale of cases. In structural optimization model, such as stage-wise superstructure, solution domain can be enlarged by adding the stages of network, which would involve multiple integer and continuous variables in optimization and result in a decreasing computational efficiency. Thus, a new nodes-based model with stream splits is proposed, which can promote the quality of result and decrease time consumption at the same time. The characteristic of this model is that it changes the matching mode of heat exchangers by setting the number of nodes on streams to quantify the positions of heat exchangers. The proposed nodes-based model has more flexibility in generating and eliminating the heat exchangers and strong ductility in exploring the solution domain. The random walk with compulsive evolution is modified for enhancing its searching ability and applying modified random walk algorithm with compulsive evolution (RWCE) into the new proposed model. The obtained results of Case 1, Case 2 and Case 3 are superior to those reported in the literature, which decreases 106 USD·a−1, 1253 USD·a−1, 23 444 USD·a−1, respectively, verifying the robust effectiveness of this new model in solving HENS problem.

Pathological lung segmentation in chest CT images based on improved random walker

Background and ObjectivePathological lung segmentation as a pretreatment step in the diagnosis of lung diseases has been widely explored. Because of the complexity of pathological lung structures and gray blur of the border, accurate lung segmentation in clinical 3D computed tomography images is a challenging task. In view of the current situation, the work proposes a fast and accurate pathological lung segmentation method. The following contributions have been made: First, the edge weights introduce spatial information and clustering information, so that walkers can use more image information during walking. Second, a Gaussian Distribution of seed point set is established to further expand the possibility of selection between fake seed points and real seed points. Finally, the pre-parameter is calculated using original seed points, and the final results are fitted with new seed points. MethodsThis study proposes a segmentation method based on an improved random walker algorithm. The proposed method consists of the following steps: First, a gray value is used as the sample distribution. Gaussian mixture model is used to obtain the clustering probability of an image. Thus, the spatial distance and clustering result are added as new weights, and the new edge weights are used to construct a random walker map. Second, a large number of marked points are automatically selected, and the intermediate results are obtained from the newly constructed map and retained only as pre-parameters. When new seed points are introduced, the probability value of the walker is quickly calculated from the new parameters and pre-parameters, and the final segmentation result can be obtained. ResultsThe proposed method was tested on 65 sets of CT cases. Quantitative evaluation with different methods confirms the high accuracy on our dataset (98.55%) and LOLA11 dataset (97.41%). Similarly, the average segmentation time (10.5s) is faster than random walker (1,332.5s). ConclusionsThe comparison of the experimental results show that the proposed method can accurately and quickly obtain pathological lung processing results. Therefore, it has potential clinical applications.

Who is the boss? Identifying key roles in telecom fraud network via centrality-guided deep random walk

PurposeTelecommunication (telecom) fraud is one of the most common crimes and causes the greatest financial losses. To effectively eradicate fraud groups, the key fraudsters must be identified and captured. One strategy is to analyze the fraud interaction network using social network analysis. However, the underlying structures of fraud networks are different from those of common social networks, which makes traditional indicators such as centrality not directly applicable. Recently, a new line of research called deep random walk has emerged. These methods utilize random walks to explore local information and then apply deep learning algorithms to learn the representative feature vectors. Although effective for many types of networks, random walk is used for discovering local structural equivalence and does not consider the global properties of nodes.Design/methodology/approachThe authors proposed a new method to combine the merits of deep random walk and social network analysis, which is called centrality-guided deep random walk. By using the centrality of nodes as edge weights, the authors’ biased random walks implicitly consider the global importance of nodes and can thus find key fraudster roles more accurately. To evaluate the authors’ algorithm, a real telecom fraud data set with around 562 fraudsters was built, which is the largest telecom fraud network to date.FindingsThe authors’ proposed method achieved better results than traditional centrality indices and various deep random walk algorithms and successfully identified key roles in a fraud network.Research limitations/implicationsThe study used co-offending and flight record to construct a criminal network, more interpersonal relationships of fraudsters, such as friendships and relatives, can be included in the future.Originality/valueThis paper proposed a novel algorithm, centrality-guided deep random walk, and applied it to a new telecom fraud data set. Experimental results show that the authors’ method can successfully identify the key roles in a fraud group and outperform other baseline methods. To the best of the authors’ knowledge, it is the largest analysis of telecom fraud network to date.

A hierarchical estimation scheme of tire-force based on random-walk SCKF for vehicle dynamics control

The estimation of vehicle dynamics states is crucial for vehicle stability control and autonomous driving, especially tire-force which governs vehicular motion. However, measuring tire-force directly needs expensive measurement instruments, moreover, vehicle non-linear characteristics, vehicle parameter uncertainties, unknown key variables, and sensor noise could cause great challenges in its observation. Therefore, this paper aims to develop an accurate, affordable tire-force estimator to tackle the above-mentioned issues. A novel hierarchical estimation scheme based on random-walk square-root cubature Kalman filter (SCKF) is proposed and it works without a complex tire model and considers vehicle parameter uncertainties. The estimator scheme contains three blocks. The first block estimates related key variables of tire force observation. The second block estimates both vertical tire-force and longitudinal tire-force. The longitudinal tire-force is observed based on effective tire radius identification, and a proportional integral differential (PID) method is derived based on the Lyapunov principle. Then, a random-walk SCKF algorithm is presented to estimates lateral tire-force. To validate the effectiveness of the proposed estimation scheme, CarSim & Matlab/Simulink joint simulation and real car tests are carried out. Results show that the proposed estimation scheme's accuracy performance and its potential as an affordable solution for the tire-force observation.

Hybrid method combining superpixel, supervised learning, and random walk for glioma segmentation

AbstractCurrently, the analysis of magnetic resonance imaging (MRI) brain images of pathological patients is performed manually, both for the recognition of brain structures or lesions and for their characterization. Physicians sometimes encounter difficulties in interpreting these images for a reliable diagnosis of the patient's condition. This is due to the difficulty of detecting the nature of the lesions, particularly glioma. Glioma is one of the most common tumors, and one of the most difficult to detect because of its shape, irregularities, and ambiguous limits. The segmentation of these tumors is one of the most crucial steps for their classification and surgical planning. This article presents a new, accurate, and automatic approach for the precise segmentation of early gliomas (benign tumors), combining the random walk (RW) algorithm and the simple linear iterative clustering algorithm. The study was carried out in four steps. The first step consisted of decomposing the image into superpixels to obtain an initial outline of the tumor. The superpixels were generated using the SLIC algorithm. In the second step, for each superpixel, a set of statistical and multifractal characteristics were calculated (gray‐level co‐occurrence matrix, multifractal detrending moving average). In the third step, the superpixels were classified using a supervised random forest (RF) type classier into healthy or tumorous brain tissue. In the final step, the contour of the detected tumor was enhanced using the customized RW algorithm. The proposed method was evaluated using the Brain Tumor Image Segmentation Challenge 2013 database. The results obtained are competitive compared to other existing methods.

Robust segmentation of lung in chest x-ray: applications in analysis of acute respiratory distress syndrome

BackgroundThis study outlines an image processing algorithm for accurate and consistent lung segmentation in chest radiographs of critically ill adults and children typically obscured by medical equipment. In particular, this work focuses on applications in analysis of acute respiratory distress syndrome – a critical illness with a mortality rate of 40% that affects 200,000 patients in the United States and 3 million globally each year.MethodsChest radiographs were obtained from critically ill adults (n = 100), adults diagnosed with acute respiratory distress syndrome (ARDS) (n = 25), and children (n = 100) hospitalized at Michigan Medicine. Physicians annotated the lung field of each radiograph to establish the ground truth. A Total Variation-based Active Contour (TVAC) lung segmentation algorithm was developed and compared to multiple state-of-the-art methods including a deep learning model (U-Net), a random walker algorithm, and an active spline model, using the Sørensen–Dice coefficient to measure segmentation accuracy.ResultsThe TVAC algorithm accurately segmented lung fields in all patients in the study. For the adult cohort, an averaged Dice coefficient of 0.86 ±0.04 (min: 0.76) was reported for TVAC, 0.89 ±0.12 (min: 0.01) for U-Net, 0.74 ±0.19 (min: 0.15) for the random walker algorithm, and 0.64 ±0.17 (min: 0.20) for the active spline model. For the pediatric cohort, a Dice coefficient of 0.85 ±0.04 (min: 0.75) was reported for TVAC, 0.87 ±0.09 (min: 0.56) for U-Net, 0.67 ±0.18 (min: 0.18) for the random walker algorithm, and 0.61 ±0.18 (min: 0.18) for the active spline model.ConclusionThe proposed algorithm demonstrates the most consistent performance of all segmentation methods tested. These results suggest that TVAC can accurately identify lung fields in chest radiographs in critically ill adults and children.

A spam worker detection approach based on heterogeneous network embedding in crowdsourcing platforms

Due to the popularity of crowdsourcing, more crowds are participating in crowdsourcing tasks. However, the proportion of spam workers is continuously increasing due to the openness of crowdsourcing platforms and their incentive mechanisms. To defend against threats from spam workers, researchers have proposed reputation-based and verification-based detection methods, but they either cannot address various collusion patterns or are costly when facing a large number of spam workers with "good" reputations due to collusion. Therefore, we propose a spam worker detection approach based on heterogeneous network embedding. We first model three collusion patterns and analyze the characteristics of spam workers to provide a theoretical basis for detecting spam workers. We then transform the problem of spam worker detection into a node classification problem in a crowdsourcing heterogeneous network in which the vectors of worker nodes are learned using network embedding. To improve the efficiency of network embedding, we propose an improved variable-length random walk algorithm based on node centrality. Finally, based on the obtained vectors of worker nodes, a one-class SVM is used to detect spam workers. The experiments demonstrate that our proposed approach can effectively detect spam workers in different collusion patterns and that the proposed random walk algorithm can reduce the time spent on model training while improving the efficiency of network embedding.

RWR-algorithm-based dissection of microRNA-506-3p and microRNA-140-5p as radiosensitive biomarkers in colorectal cancer.

Radiotherapy resistance is one of the main causes for treatment failure in colorectal cancer (CRC), and it is associated with the deregulation of certain microRNAs. In this study, we constructed the microRNA-mRNA network consisting of 2275 microRNAs and 7045 target genes, collected the known microRNAs related to CRC-radiosensitivity (CRCR) (n=18) as the seed nodes, and applied the algorithm of random walk with restart (RWR) to the network to identify novel CRCR-related microRNAs (n=263). In functional analysis, 263 novel microRNAs shared a high proportion of the same biological processes and pathways with the known microRNAs. In topological analysis of the sub-network of the 263 microRNAs and their targets, hsa-mir-506-3p and hsa-mir-140-5p were identified as network hub nodes. In plasma, radiosensitive patients had a higher expression level of hsa-mir-506-3p and hsa-mir-140-5p than radioresistant patients. In experimental validation, both hsa-mir-506-3p and hsa-mir-140-5p over-expression could obviously decrease the cell proliferation, survival rate and colonality in CRC cells after radiation. In conclusion, this study combined the novel network-based method with experimental validation, and identified two novel radiosensitive biomarkers of hsa-mir-506-3p and hsa-mir-140-5p in CRC.

Goal‐oriented design of optimal degree distribution for LT codes

Goal-oriented Luby transform (LT) codes are required in different scenarios. Based on the random walk model, analysis of ripple size evolution provides an effective method to design LT codes. However, the ripple addition in each step depends on both the number of symbols released in the current step and the ripple size in the last step, leading to distortion. Therefore, the authors propose a random walk algorithm with barrier (RWA-B) to model the ripple addition directly. To evaluate the performance of ripple size evolution, they utilise generalised degree distribution algorithm to generate corresponding degree distribution. Through analysing the relationship between the performance of generated ripple size evolution and the parameters of RWA-B, they design an iterator greedy algorithm to search the optimal ripple size evolution for desired goals. As a result, they compare three goal-oriented degree distributions with others through numerical simulations. In binary erasure channel, both the memory usage oriented and block error rate (BER) oriented degree distributions outperform others with lowest average overhead, and the overhead oriented degree distribution outperforms others in terms of average overhead and average degree. In additive white Gaussian noise channel, the BER-oriented degree distribution outperforms others in terms of BER and average overhead.

A Method of Directional Signs Location Selection and Content Generation in Scenic Areas

The Tourist Oriented Directional Signs (TODS) system is an essential and important project in constructing and planning scenic areas. At present, the placement of directional signs generally depends on the personal experience of the tour manager to identify important positions and display the name of critical scenic spots on a signboard. Few studies have focused on how to generate the location and display the content of directional signs automatically. This article proposes a method for directional sign location selection and automatic generation of content in a scenic area based on the tourist spatial behavior theory and network analysis algorithm. Junction nodes of the road in a scenic area are used as the candidate locations of the directional signs to be placed. The main steps of the method in this paper include tourist route simulation, betweenness centrality calculation, location selection, and content generation. The Ming Tomb in Nanjing, China, is selected as the experimental area. The evaluation indexes of the traveled distance and the number of errors were adopted. The random walk algorithm is applied to compare the generated scheme with the existing scheme in the experimental scenic area. The generated scheme is also verified through questionnaires and interviews. The results show that the method proposed in this paper can select relevant and appropriate junction nodes where to deploy directional signs and automatically generate displayed content more prominently. The comparison shows that the generated scheme in this method is significantly better than the actual placement scheme. It can optimize the actual placement scheme in the experimental area, and it also can reduce the traveled distance and number of errors.

Abstract Concept Instantiation with Context Relevance Measurement

In different contexts, one abstract concept (e.g., fruit) may be mapped into different concrete instance sets, which is called abstract concept instantiation. It has been widely applied in many applications, such as web search, intelligent recommendation, etc. However, in most abstract concept instantiation models have the following problems: (1) the neglect of incorrect label and label incompleteness in the category structure on which instance selection relies; (2) the subjective design of instance profile for calculating the relevance between instance and contextual constraint. The above problems lead to false prediction in terms of abstract concept instantiation. To tackle these problems, we proposed a novel model to instantiate the abstract concept. Firstly, to alleviate the incorrect label and remedy label incompleteness in the category structure, an improved random-walk algorithm is proposed, called InstanceRank, which not only utilize the category information, but it also exploits the association information to infer the right instances of an abstract concept. Secondly, for better measuring the relevance between instances and contextual constraint, we learn the proper instance profile from different granularity ones. They are designed based on the surrounding text of the instance. Finally, noise reduction and instance filtering are introduced to further enhance the model performance. Experiments on Chinese food abstract concept set show that the proposed model can effectively reduce false positive and false negative of instantiation results.

Segmentation of the placenta and its vascular tree in Doppler ultrasound for fetal surgery planning.

Twin-to-twin transfusion syndrome (TTTS) is a serious condition that occurs in about 10-15% of monochorionic twin pregnancies. In most instances, the blood flow is unevenly distributed throughout the placenta anastomoses leading to the death of both fetuses if no surgical procedure is performed. Fetoscopic laser coagulation is the optimal therapy to considerably improve co-twin prognosis by clogging the abnormal anastomoses. Notwithstanding progress in recent years, TTTS surgery is highly risky. Computer-assisted planning of the intervention can thus improve the outcome. In this work, we implement a GPU-accelerated random walker (RW) algorithm to detect the placenta, both umbilical cord insertions and the placental vasculature from Doppler ultrasound (US). Placenta and background seeds are manually initialized in 10-20 slices (out of 245). Vessels are automatically initialized in the same slices by means of Otsu thresholding. The RW finds the boundaries of the placenta and reconstructs the vasculature. We evaluate our semiautomatic method in 5 monochorionic and 24 singleton pregnancies. Although satisfactory performance is achieved on placenta segmentation (Dice ≥ 84.0%), some vascular connections are still neglected due to the presence of US reverberation artifacts (Dice ≥ 56.9%). We also compared inter-user variability and obtained Dice coefficients of ≥ 76.8% and ≥ 97.42% for placenta and vasculature, respectively. After a 3-min manual initialization, our GPU approach speeds the computation 10.6 times compared to the CPU. Our semiautomatic method provides a near real-time user experience and requires short training without compromising the segmentation accuracy. A powerful approach is thus presented to rapidly plan the fetoscope insertion point ahead of TTTS surgery.

Organ Contouring for Lung Cancer Patients with a Seed Generation Scheme and Random Walks.

In this study, we proposed a semi-automated and interactive scheme for organ contouring in radiotherapy planning for patients with non-small cell lung cancers. Several organs were contoured, including the lungs, airway, heart, spinal cord, body, and gross tumor volume (GTV). We proposed some schemes to automatically generate and vanish the seeds of the random walks (RW) algorithm. We considered 25 lung cancer patients, whose computed tomography (CT) images were obtained from the China Medical University Hospital (CMUH) in Taichung, Taiwan. The manual contours made by clinical oncologists were taken as the gold standard for comparison to evaluate the performance of our proposed method. The Dice coefficient between two contours of the same organ was computed to evaluate the similarity. The average Dice coefficients for the lungs, airway, heart, spinal cord, and body and GTV segmentation were 0.92, 0.84, 0.83, 0.73, 0.85 and 0.66, respectively. The computation time was between 2 to 4 min for a whole CT sequence segmentation. The results showed that our method has the potential to assist oncologists in the process of radiotherapy treatment in the CMUH, and hopefully in other hospitals as well, by saving a tremendous amount of time in contouring.