Random walk ≈ Random walk
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Representation Learning on Graphs with Jumping Knowledge Networks Open
Recent deep learning approaches for representation learning on graphs follow a neighborhood aggregation procedure. We analyze some important properties of these models, and propose a strategy to overcome those. In particular, the range of …
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<i>R</i> Package <b>gdistance</b>: Distances and Routes on Geographical Grids Open
The R package gdistance provides classes and functions to calculate various distance measures and routes in heterogeneous geographic spaces represented as grids. Least-cost distances as well as more complex distances based on (constrained)…
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Drug repositioning based on comprehensive similarity measures and Bi-Random walk algorithm Open
Motivation: Drug repositioning, which aims to identify new indications for existing drugs, offers a promising alternative to reduce the total time and cost of traditional drug development. Many computational strategies for drug repositioni…
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Strongly correlated quantum walks in optical lattices Open
Quantum walkers under a microscope Generations of physics students have been taught to think of one-dimensional random walks in terms of a drunken sailor taking random steps to the right or to the left. But that doesn't compare with the co…
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Go for a Walk and Arrive at the Answer: Reasoning Over Paths in Knowledge Bases using Reinforcement Learning Open
Knowledge bases (KB), both automatically and manually constructed, are often incomplete --- many valid facts can be inferred from the KB by synthesizing existing information. A popular approach to KB completion is to infer new relations by…
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Random Walks: A Review of Algorithms and Applications Open
A random walk is known as a random process which describes a path including a\nsuccession of random steps in the mathematical space. It has increasingly been\npopular in various disciplines such as mathematics and computer science.\nFurthe…
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IRWRLDA: improved random walk with restart for lncRNA-disease association prediction Open
In recent years, accumulating evidences have shown that the dysregulations of lncRNAs are associated with a wide range of human diseases. It is necessary and feasible to analyze known lncRNA-disease associations, predict potential lncRNA-d…
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Random walks on hypergraphs Open
In the past 20 years network science has proven its strength in modeling many real-world interacting systems as generic agents, the nodes, connected by pairwise edges. Nevertheless, in many relevant cases, interactions are not pairwise but…
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Stochastic resetting in backtrack recovery by RNA polymerases Open
Transcription is a key process in gene expression, in which RNA polymerases produce a complementary RNA copy from a DNA template. RNA polymerization is frequently interrupted by backtracking, a process in which polymerases perform a random…
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Robust single-cell Hi-C clustering by convolution- and random-walk–based imputation Open
Three-dimensional genome structure plays a pivotal role in gene regulation and cellular function. Single-cell analysis of genome architecture has been achieved using imaging and chromatin conformation capture methods such as Hi-C. To study…
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Random walks on semantic networks can resemble optimal foraging. Open
When people are asked to retrieve members of a category from memory, clusters of semantically related items tend to be retrieved together. A recent article by Hills, Jones, and Todd (2012) argued that this pattern reflects a process simila…
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Nuclear Reaction Optimization: A Novel and Powerful Physics-Based Algorithm for Global Optimization Open
Meta-heuristic algorithms have gained substantial popularity in recent decades and have focused on applications in a wide spectrum of fields. In this paper, a new and powerful physics-based algorithm named nuclear reaction optimization (NR…
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Optimal first-arrival times in Lévy flights with resetting Open
We consider the diffusive motion of a particle performing a random walk with Lévy distributed jump lengths and subject to a resetting mechanism, bringing the walker to an initial position at uniformly distributed times. In the limit of an …
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Ant groups optimally amplify the effect of transiently informed individuals Open
To cooperatively transport a large load, it is important that carriers conform in their efforts and align their forces. A downside of behavioural conformism is that it may decrease the group’s responsiveness to external information. Combin…
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Deep Graph Infomax. Open
We present Deep Graph Infomax (DGI), a general approach for learning node representations within graph-structured data in an unsupervised manner. DGI relies on maximizing mutual information between patch representations and corresponding h…
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Random walks in cones Open
We study the asymptotic behavior of a multidimensional random walk in a general cone. We find the tail asymptotics for the exit time and prove integral and local limit theorems for a random walk conditioned to stay in a cone. The main step…
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NetGAN: Generating Graphs via Random Walks Open
We propose NetGAN - the first implicit generative model for graphs able to mimic real-world networks. We pose the problem of graph generation as learning the distribution of biased random walks over the input graph. The proposed model is b…
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Discriminative Deep Random Walk for Network Classification Open
Deep Random Walk (DeepWalk) can learn a latent space representation for describing the topological structure of a network.However, for relational network classification, DeepWalk can be suboptimal as it lacks a mechanism to optimize the ob…
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Hamiltonian Monte Carlo methods for Subset Simulation in reliability analysis Open
This paper studies a non-random-walk Markov Chain Monte Carlo method, namely the Hamiltonian Monte Carlo (HMC) method in the context of Subset Simulation used for reliability analysis. The HMC method relies on a deterministic mechanism ins…
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Watch Your Step: Learning Node Embeddings via Graph Attention Open
Graph embedding methods represent nodes in a continuous vector space, preserving information from the graph (e.g. by sampling random walks). There are many hyper-parameters to these methods (such as random walk length) which have to be man…
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Network Dynamics of Innovation Processes Open
We introduce a model for the emergence of innovations, in which cognitive processes are described as random walks on the network of links among ideas or concepts, and an innovation corresponds to the first visit of a node. The transition m…
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Bedload transport: a walk between randomness and determinism. Part 1. The state of the art Open
This paper outlines the various approaches used to calculate bedload transport. As bedload transport exhibits considerable spatial and temporalvariations, computing the bedload transport rates and morphological changes experienced by strea…
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REVISITING STOCHASTIC VARIABILITY OF AGNs WITH STRUCTURE FUNCTIONS Open
Discrepancies between reported structure function (SF) slopes and their overall flatness as compared to the expectations from the damped random walk (DRW) model, which generally well describes the variability of active galactic nuclei (AGN…
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Large deviations for Markov processes with resetting Open
Markov processes restarted or reset at random times to a fixed state or region in space have been actively studied recently in connection with random searches, foraging, and population dynamics. Here we study the large deviations of time-a…
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An Attentional Recurrent Neural Network for Personalized Next Location Recommendation Open
Most existing studies on next location recommendation propose to model the sequential regularity of check-in sequences, but suffer from the severe data sparsity issue where most locations have fewer than five following locations. To this e…
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The IntCal20 Approach to Radiocarbon Calibration Curve Construction: A New Methodology Using Bayesian Splines and Errors-in-Variables Open
To create a reliable radiocarbon calibration curve, one needs not only high-quality data but also a robust statistical methodology. The unique aspects of much of the calibration data provide considerable modeling challenges and require a m…
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Space-Time Correspondence as a Contrastive Random Walk Open
This paper proposes a simple self-supervised approach for learning a representation for visual correspondence from raw video. We cast correspondence as prediction of links in a space-time graph constructed from video. In this graph, the no…
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Well-posedness and numerical algorithm for the tempered fractional differential equations Open
Trapped dynamics widely appears in nature, e.g., the motion of particles in viscous cytoplasm. The famous continuous time random walk (CTRW) model with power law waiting time distribution (having diverging first moment) describes this phen…
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Equivalence between Step Selection Functions and Biased Correlated Random Walks for Statistical Inference on Animal Movement Open
Animal movement has a fundamental impact on population and community structure and dynamics. Biased correlated random walks (BCRW) and step selection functions (SSF) are commonly used to study movements. Because no studies have contrasted …
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Random walks and market efficiency tests: evidence on US, Chinese and European capital markets within the context of the global Covid-19 pandemic Open
Research background: Covid-19 has affected the global economy and has had an inevitable impact on capital markets. In the week of February 24?28, 2020, stock markets crashed. The index FTSE 100 decreased 13%, while the indices DJIA and S&P…