Random Walk Method Research Articles

TNE: A general time-aware network representation learning framework for temporal applications

Temporal dynamics such as short term and long term effects, recency effects, periodic and seasonal temporal factors in information networks are of great importance for many real-world applications. However, existing network embedding learning approaches mainly focus on semantic information or temporal phenomenon such as recency or dynamic process. They failed to have the capability of incorporating multiple temporal factors/phenomenon in information networks. To bridge the gap, this paper proposes a general time-aware network representation learning framework TNE for temporal applications. TNE contains a temporally annotated network TAN, a temporally annotated meta-path based random walk method, and a self-supervised embedding learning approach. We introduce temporal nodes and relations to existing information networks to construct TAN that can incorporate multiple temporal factors. We propose a temporally annotated meta-path based random walk approach to form a time-aware hybrid neighbourhood context that considers both semantic and temporal factors. Based on the time-aware context, self-supervised representation learning approaches are used to simultaneously preserve both semantic and temporal factors in embeddings. Extensive experiments of two large scale real-life datasets show that the proposed framework is effective in various temporal applications such as temporal similarity search and temporal recommendations.

TraceRank: Abnormal service localization with dis‐aggregated end‐to‐end tracing data in cloud native systems

AbstractModern cloud native applications are generally built with a microservice architecture. To tackle various performance problems among a large number of services and machines, an end‐to‐end tracing tool is always equipped in these systems to track the execution path of every single request. However, it is nontrivial to conduct root cause analysis of anomalies with such a large volume of tracing data. This paper proposes a novel system named TraceRank to identify and locate abnormal services causing performance problems with dis‐aggregated end‐to‐end traces. TraceRank mainly includes an anomaly detection module and a root cause analysis module. The root cause analysis procedure is triggered when an anomaly is detected. To fully leverage the information provided by the tracing data, both the spectrum analysis and the PageRank‐based random walk methods are introduced to pinpoint abnormal services. The experiments in TrainTicket and Bookinfo microservice benchmarks and a real‐world system show that TraceRank can locate root causes with 90% in Precision and 86% in Recall. TraceRank has up to 10% improvement compared with several state‐of‐the‐art approaches in both Precision and Recall. Finally, TraceRank has good scalability and a low overhead to adapt to large‐scale microservice systems.

Continuum to rarefied diffusive tortuosity factors in porous media from X-ray microtomography

The diffusive tortuosity factor of a porous media quantifies the material’s resistance to diffusion, an important component of modeling flows in porous structures at the macroscale. Advances in X-ray micro-computed tomography (μ-CT) imaging provide the geometry of the material at the microscale (microstructure) thus enabling direct numerical simulation (DNS) of transport at the microscale. The data from these DNS are then used to close material’s macroscale transport models, which rely on effective material properties. In this work, we present numerical methods suitable for large scale simulations of diffusive transport through complex microstructures for the full range of Knudsen regimes. These numerical methods include a finite-volume method for continuum conditions, a random walk method for all regimes from continuum to rarefied, and the direct simulation Monte Carlo method. We show that for particle methods, the surface representation significantly affects the accuracy of the simulation for high Knudsen numbers, but not for continuum conditions. We discuss the upscaling of pore-resolved simulations to single species and multi-species volume-averaged models. Finally, diffusive tortuosities of a fibrous material are computed by applying the discussed numerical methods to 3D images of the actual microstructure obtained from X-ray computed micro-tomography.

Estimating Permeability of Partially Frozen Soil Using Floating Random Walks

AbstractFlow through partially frozen pores in granular media containing ice or gas hydrate plays an essential role in diverse phenomena including methane migration and frost heave. As freezing progresses, the frozen phase grows in the pore space and constricts flow paths so that the permeability decreases. Previous works have measured the relationship between permeability and volumetric fraction of the frozen phase, and various correlations have been proposed to predict permeability change in hydrology and the oil industry. However, predictions from different formulae can differ by orders of magnitude, causing great uncertainty in modeling results. We present a floating random walk method to approximate the porous flow field and estimate the effective permeability in isotropic granular media with specified particle size distributions, without solving for the entire flow field in the pore space. In packed spherical particles, the method compares favorably with the Kozeny‐Carman formula. We further extend this method with a probabilistic interpretation of the volumetric fraction of the frozen phase, simulate the effect of freezing in irregular pores, and predict the evolution of permeability. Employing no adjustable parameters, our results can provide insight into the coupling between phase transitions and permeability change, which plays important roles in hydrate formation and dissociation, as well as in the thawing and freezing of permafrost and ice‐bed coupling beneath glaciers.

Comparative analysis of aneurysm subtypes associated genes based on protein\u2013protein interaction network

The arterial aneurysm refers to localized dilation of blood vessel wall and is common in general population. The majority of aneurysm cases remains asymptomatic until a sudden rupture which is usually fatal and of extremely high mortality (~ 50–60%). Therefore, early diagnosis, prevention and management of aneurysm are in urgent need. Unfortunately, current understanding of disease driver genes of various aneurysm subtypes is still limited, and without appropriate biomarkers and drug targets no specialized drug has been developed for aneurysm treatment. In this research, aneurysm subtypes were analyzed based on protein–protein interaction network to better understand aneurysm pathogenesis. By measuring network-based proximity of aneurysm subtypes, we identified a relevant closest relationship between aortic aneurysm and aortic dissection. An improved random walk method was performed to prioritize candidate driver genes of each aneurysm subtype. Thereafter, transcriptomes of 6 human aneurysm subtypes were collected and differential expression genes were identified to further filter potential driver genes. Functional enrichment of above driver genes indicated a general role of ubiquitination and programmed cell death in aneurysm pathogenesis. Especially, we further observed participation of BCL-2-mediated apoptosis pathway and caspase-1 related pyroptosis in the development of cerebral aneurysm and aneurysmal subarachnoid hemorrhage in corresponding transcriptomes.

Epidemiological study of the previousness of the symptoms of gastroesophageal refl ux disease in residents of the baykal territory with account of ethnic peculiarities of the population

Aim. Study of the prevalence of gastroesophageal refl ux disease among residents of the Trans- Baikal Territory, taking into account its ethnic composition.Materials and methods. By the method of random household walk, the study included persons over 18 years of age. The GerdQ questionnaire was applied. Additionally, passport data, information on smoking, alcohol use, coff ee, anthropometric data, social status, membership in a particular ethnic group (Buryats, Europoids) are collected. Four groups were analyzed: the 1st Caucasians, who scored 8 or more points on the GerdQ questionnaire and the 2nd — Caucasians, who scored less than 8 points, the 3rd group—the Buryats, who scored 8 or more points, and the 4th—Buryats, not scored 8 points. For statistical processing used the program Statistica 10.0, the diff erences were considered signifi cant when p< 0.05.Results. 371 profi les were selected for analysis. 8 or more points on the GerdQ questionnaire scored 48 people (12.9%). 236 people—Caucasians (63.6%) and 135 Buryat people (36.4%), while the latter had more rare manifestations of GERD (38 Caucasians (16.1%) and 10 Buryats (7.4%) p = 0.009). The average age of persons of the 1st group was 53.4 ± 17.47 years and exceeded that of the 2nd group (46.2 ± 19.2 years), p = 0.035. Manifestations of GERD in Buryats were observed at a younger age and did not diff er between the 3rd and 4th groups. There was no correlation between GERD symptoms and bad habits (smoking, alcohol). The dependence of GERD symptoms with coff ee consumption and obesity has not been established.Conclusion. The prevalence of symptoms of gastroesophageal reflux disease in the territory of the Trans- Baikal Territory is below the national indicators, which is probably related to the ethnic composition of the population. Age-related features of the identification of symptoms of GERD are characteristic only for Caucasians.

A global random walk on grid algorithm for second order elliptic equations

Abstract In this paper we develop stochastic simulation methods for solving large systems of linear equations, and focus on two issues: (1) construction of global random walk algorithms (GRW), in particular, for solving systems of elliptic equations on a grid, and (2) development of local stochastic algorithms based on transforms to balanced transition matrix. The GRW method calculates the solution in any desired family of prescribed points of the gird in contrast to the classical stochastic differential equation based Feynman–Kac formula. The use in local random walk methods of balanced transition matrices considerably decreases the variance of the random estimators and hence decreases the computational cost in comparison with the conventional random walk on grids algorithms.

Pore-Scale Prediction of the oxygen effective diffusivity in porous battery electrodes using the random walk theory

This study developed a predictive model to calculate the effective diffusivity of oxygen in porous electrodes. The model integrates the random walk method and the tomographic technique. We reconstructed pore-scale structures of battery electrodes, and quantitatively studied the effective diffusivity of oxygen in non-wetted and wetted digital geometry. Both the reconstructed geometry and the random walk model were well validated. This model can simulate effective diffusivity with reasonable accuracy and low computational time. Results show that the effective diffusivity of oxygen is very sensitive to the porosity and the electrolyte saturation. A higher porosity improves the oxygen diffusion in a porous medium. On the contrary, the oxygen diffusivity drops significantly when the porous medium is wetted by the electrolyte. At higher saturation (> 0.79), the effective diffusivity is similar to the diffusivity in the liquid. The findings from this study have a strong potential to enhance oxygen mass transfer in porous electrodes and will accelerate the deployment of metal-air batteries. In addition, the advantages of low computational cost, real-time capture of material structures, and wide applicability make this model a powerful and convenient tool to predict transport properties of porous media in a wide range of science and engineering fields.

The Multi-Advective Water Mixing Approach for Transport through Heterogeneous Media

Finding a numerical method to model solute transport in porous media with high heterogeneity is crucial, especially when chemical reactions are involved. The phase space formulation termed the multi-advective water mixing approach (MAWMA) was proposed to address this issue. The water parcel method (WP) may be obtained by discretizing MAWMA in space, time, and velocity. WP needs two transition matrices of velocity to reproduce advection (Markovian in space) and mixing (Markovian in time), separately. The matrices express the transition probability of water instead of individual solute concentration. This entails a change in concept, since the entire transport phenomenon is defined by the water phase. Concentration is reduced to a chemical attribute. The water transition matrix is obtained and is demonstrated to be constant in time. Moreover, the WP method is compared with the classic random walk method (RW) in a high heterogeneous domain. Results show that the WP adequately reproduces advection and dispersion, but overestimates mixing because mixing is a sub-velocity phase process. The WP method must, therefore, be extended to take into account incomplete mixing within velocity classes.

Effects of flow regime and conjugate heat transfer on particle deposition in heat exchange duct

Deposition of micron particles in heat exchange duct is commonly encountered in many thermal and HVAC engineering applications. Previous studies on this important issue always considered turbulent air flow regime without conjugate heat transfer (CHT). However, the flow regimes and the CHT may have significant influences on particle deposition behaviors. Therefore, this paper aims to study particle deposition characteristics in laminar flow (LF), developing turbulent flow (DTF) and fully developed turbulent flow (FDTF) regimes with and without CHT. The v′2‾−fturbulence model and discrete particle model (DPM) were used to predict gas phase and solid phase, respectively. Moreover, a modified discrete random walk method was adopted to model turbulent particle dispersion. The results showed that particle deposition velocities greatly increase for small particles (dp<20μm) but keep invariable for large particles (dp>20μm) due to thermophoretic deposition. WhenΔT=100Kanddp=1μm, the peak deposition velocity enhancement ratio reaches 3.8, 9.7 and 7.5 for LF, DTF and FDTF regimes respectively. Different flow regimes have obvious effects on the deposition characteristics. Particle deposition velocity for DTF regime is the highest, followed by FDTF case and LF case. Thermophoresis effect is reduced by the CHT, as the temperature difference decreases due to thermal conduction in solid domain. The CHT in DTF or FDTF regime has obviously larger effect on deposition characteristics, compared with the LF regime.

A Comparison of Geosampling and Random Walk Methods for Household Sample Selection in Uttar Pradesh, India

Abstract Our research evaluates an innovative sampling technique for household surveys called “geosampling” which leverages recent advances in geographic information systems, computer vision algorithms, and satellite imagery. We compare geosampling to the random walk method. We conducted two surveys in Uttar Pradesh, India: one using geosampling (1,026 completes) and another using random walk (939 completes). We compare the two sampling techniques along three dimensions: (a) performance indicators—response rates and contact attempts; (b) sample composition; and (c) components of variance. We help researchers understand the survey contexts for which geosampling and random walk are best suited.

Integrating Protein-Protein Interaction Networks and Somatic Mutation Data to Detect Driver Modules in Pan-Cancer.

With the constant update of large-scale sequencing data and the continuous improvement of cancer genomics data, such as International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA), it gains increasing importance to detect the functional high-frequency mutation gene set in cells that causes cancer in the field of medicine. In this study, we propose a new recognition method of driver modules, named ECSWalk to solve the issue of mutated gene heterogeneity and improve the accuracy of driver modules detection, based on human protein-protein interaction networks and pan-cancer somatic mutation data. This study first utilizes high mutual exclusivity and high coverage between mutation genes and topological structure similarity of the nodes in complex networks to calculate interaction weights between genes. Second, the method of random walk with restart is utilized to construct a weighted directed network, and the strong connectivity principle of the directed graph is utilized to create the initial candidate modules with a certain number of genes. Finally, the large modules in the candidate modules are split using induced subgraph method, and the small modules are expanded using a greedy strategy to obtain the optimal driver modules. This method is applied to TCGA pan-cancer data and the experimental results show that ECSWalk can detect driver modules more effectively and accurately, and can identify new candidate gene sets with higher biological relevance and statistical significance than MEXCOWalk and HotNet2. Thus, ECSWalk is of theoretical implication and practical value for cancer diagnosis, treatment and drug targets.

Bryophytes of Rio Turvo State Park (SP), Brazil: integrating floristics, geographical distribution, reproduction and ecological traits to support the conservation of an Atlantic Forest fragment

Rio Turvo State Park (RTSP) is a Conservation Unit located within the Atlantic Forest biome in the Vale do Ribeira region of the State of São Paulo, Brazil. Considering the lack of knowledge regarding its bryoflora, we performed a floristic inventory of RTSP (following the random-walk method), the results of which we report here including discussion of the geographical distribution (Brazil and worldwide), substrate colonization, life forms and reproduction of the species. We found a total of 414 species, which represents 30 % of all known bryophyte species in Brazil. Dioicous species were predominant over monoicous species, the most colonized substrate was tree trunk (40 % of all species) and the predominant life form was mat (47 %), indicating that the floristic composition is influenced by the environmental conditions of the area (high humidity, dense vegetation and shading). Although anthropogenic intervention is present in RTSP, it harbors huge biodiverse potential, since 22 % of the species are rare. Furthermore, 16 new occurrences were recorded for the State of São Paulo. The information provided by this study exalts the importance of this Conservation Unit in the preservation of bryophytes, as well as all the biodiversity of its included ecosystems.

Pore-scale Transport in Rocks of Different Complexity Modeled by Random Walk Methods

We investigate pore-scale transport of a passive solute in three types of reservoir rocks of distinctly different heterogeneity characteristics, using both random walk particle tracking (RWPT), a Lagrangian method free of numerical dispersion, and time domain random walk (TDRW), a regular-lattice-based approximation of the advection–diffusion equation. The transport behavior is probed in terms of solute breakthrough curves for a large range of values of the Péclet number, and for both flux-weighted and uniform injection conditions. We compare the two numerical modeling approaches and thereby highlight the impact of the distribution of pore-scale flow velocities on the large-scale transport behavior. We discuss the properties of two numerical approaches and analyze the influence of the numerical resolution of the velocity field on the simulated transport behaviors. A main difference consists in the presence of numerical dispersion in the TDRW method in contrast to the RWPT method. We find that this feature does not affect the simulated large-scale transport compared to the RWPT method, because numerical dispersion is negligible compared to the impact of the broad spectra of velocity fluctuations, which leads to heavy-tailed breakthrough curves and broad peak behaviors determined by hydrodynamic dispersion. The two random walk methods can be equivalently used to simulate hydrodynamic transport at pore scale. The direct simulations provide breakthrough curves that cover up to ten orders of magnitude in time. The tailing behavior is directly related to the distribution of pore-scale flow velocities using a continuous time random walk approach. The comparison of breakthrough curves and velocity distributions for three different rock types indicates that the solute breakthrough behavior can be used to infer the pore-scale velocity distribution and the medium structure.

A Theoretical Framework for Studying Random Walk Planning

Random walks are a relatively new component used in several state of the art satisficing planners. Empirical results have been mixed: while the approach clearly outperforms more systematic search methods such as weighted A* on many planning domains, it fails in many others. So far, the explanations for these empirical results have been somewhat ad hoc. This paper proposes a formal framework for comparing the performance of random walk and systematic search methods. Fair homogenous graphs are proposed as a graph class that represents characteristics of the state space of prototypical planning domains, and is simple enough to allow a theoretical analysis of the performance of both random walk and systematic search algorithms. This gives well-founded insights into the relative strength and weaknesses of these approaches. The close relation of the models to some well-known planning domains is shown through simplified but semi-realistic planning domains that fulfill the constraints of the models. One main result is that in contrast to systematic search methods, for which the branching factor plays a decisive role, the performance of random walk methods is determined to a large degree by the Regress Factor, the ratio between the probabilities of progressing towards and regressing away from a goal with an action. The performance of random walk and systematic search methods can be compared by considering both branching and regress factors of a state space.

Research on Block Chain Service Composition Strategy Based on Big Data

With the proposal of China’s new infrastructure strategy, big data, artificial intelligence and 5G networks are bound to get further rapid development, and the Internet of Things, as a key component, is also getting more and more extensive attention and in-depth research. Wireless sensor network is an important part of the development of the Internet of Things, which is widely used in military, transportation, industry and daily life. Therefore, the security of wireless sensor network is particularly important. Due to the limited resources such as energy, storage and space, existing heavyweight security authentication methods are not suitable for wireless sensor networks. Based on the block chain design idea, a lightweight wireless sensor network security authentication model is proposed to solve the data security problem of wireless sensor network terminal. The model divides the sensor network into two layers: global network and local network. The local network adopts asymmetric encryption for centralized security authentication. The global network uses directed acyclic graph (DAG) and Tangle technology to construct the account books, and uses the key nodes and random walk method to verify the consistency.

Priority analysis of port investment along the 21st-Century Maritime Silk Road region: the case of Southeast Asia

ABSTRACT Most of the Southeast Asian countries are developing economies that have large demand for maritime infrastructures. Some but not all the ports in this region could significantly benefit from and contribute to the Belt and Road Initiative (BRI) proposed by the Chinese government. This paper models the port investment priority in the Southeast Asian region, so that efficient and sustainable investments can be made under the BRI. Based on the link prediction theory, a random walk method is proposed to assess the priorities of port construction projects at different sites. The proposed method explicitly considers important economic and political factors, especially those linking Southeast Asian countries with China. Our model is calibrated and verified with numerical experiments, so that policy and managerial recommendations can be obtained for this region. Results consistent with industry reality also provide supports to the validity of the model. This study introduces a new dimension of investment planning for multiple ports taking into account the resultant impacts on shipping networks, and recommends selected port construction sites with good potential in Southeast Asia.

One-step random-walk process of nanoparticles in cement-based materials

Efficient modelling approaches capable of predicting the behavior and effects of nanoparticles in cement-based materials are required for conducting relevant experiments. From the microstructural characterization of a cement-nanoparticle system, this paper investigates the potential of cell-based weighted random-walk method to establish statistically significant relationships between chemical bonding and diffusion processes of nanoparticles within cement matrix. LaSr0.5C0.5O3 (LSCO) nanoparticles were employed to develop a discrete event system that accounts for the behavior of individual cells where nanoparticles and cement components were expected to interact. The stochastic model is based on annihilation (loss) and creation (gain) of a bond in the cell. The model considers both chemical reactions and transport mechanism of nanoparticles from cementitious cells, along with cement hydration process. This approach may be useful for simulating nanoparticle transport in complex 2D cement-based materials systems.

Understanding airspace in leaves: 3D anatomy and directional tortuosity.

The leaf intercellular airspace is a tortuous environment consisting of cells of different shapes, packing densities, and orientation, all of which have an effect on the travelling distance of molecules from the stomata to the mesophyll cell surfaces. Tortuosity, the increase in displacement over the actual distance between two points, is typically defined as encompassing the whole leaf airspace, but heterogeneity in pore dimensions and orientation between the spongy and palisade mesophyll likely result in heterogeneity in tortuosity along different axes and would predict longer traveling distance along the path of least tortuosity, such as vertically within the columnar cell matrix of the palisade layer. Here, we compare a previously established geometric method to a random walk approach, novel for this analysis in plant leaves, in four different Eucalyptus species. The random walk method allowed us to quantify directional tortuosity across the whole leaf profile, and separately for the spongy and palisade mesophyll. For all species tortuosity was higher in the palisade mesophyll than the spongy mesophyll and horizontal (parallel to the epidermis) tortuosity was consistently higher than vertical (from epidermis to epidermis) tortuosity. We demonstrate that a random walk approach improves on previous geometric approaches and is valuable for investigating CO2 and H2 O transport within leaves.

Embedding Heterogeneous Networks into Hyperbolic Space Without Meta-path

Networks found in the real-world are numerous and varied. A common type of network is the heterogeneous network, where the nodes (and edges) can be of different types. Accordingly, there have been efforts at learning representations of these heterogeneous networks in low-dimensional space. However, most of the existing heterogeneous network embedding suffers from the following two drawbacks: (1) The target space is usually Euclidean. Conversely, many recent works have shown that complex networks may have hyperbolic latent anatomy, which is non-Euclidean. (2) These methods usually rely on meta-paths, which requires domain-specific prior knowledge for meta-path selection. Additionally, different down-streaming tasks on the same network might require different meta-paths in order to generate task-specific embeddings. In this paper, we propose a novel self-guided random walk method that does not require meta-path for embedding heterogeneous networks into hyperbolic space. We conduct thorough experiments for the tasks of network reconstruction and link prediction on two public datasets, showing that our model outperforms a variety of well-known baselines across all tasks.