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Добірка наукової літератури з теми "Random walks on network"

Автор: Grafiati

Опубліковано: 11 березня 2023

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Статті в журналах з теми "Random walks on network"

LI, KEQIN. "PERFORMANCE ANALYSIS AND EVALUATION OF RANDOM WALK ALGORITHMS ON WIRELESS NETWORKS." International Journal of Foundations of Computer Science 23, no. 04 (June 2012): 779–802. http://dx.doi.org/10.1142/s0129054112400369.

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We propose a model of dynamically evolving random networks and give an analytical result of the cover time of the simple random walk algorithm on a dynamic random symmetric planar point graph. Our dynamic network model considers random node distribution and random node mobility. We analyze the cover time of the parallel random walk algorithm on a complete network and show by numerical data that k parallel random walks reduce the cover time by almost a factor of k. We present simulation results for four random walk algorithms on random asymmetric planar point graphs. These algorithms include the simple random walk algorithm, the intelligent random walk algorithm, the parallel random walk algorithm, and the parallel intelligent random walk algorithm. Our random network model considers random node distribution and random battery transmission power. Performance measures include normalized cover time, probability distribution of the length of random walks, and load distribution.

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Ma, Qi, Anders Johansson, Atsushi Tero, Toshiyuki Nakagaki, and David J. T. Sumpter. "Current-reinforced random walks for constructing transport networks." Journal of The Royal Society Interface 10, no. 80 (March 6, 2013): 20120864. http://dx.doi.org/10.1098/rsif.2012.0864.

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Biological systems that build transport networks, such as trail-laying ants and the slime mould Physarum , can be described in terms of reinforced random walks. In a reinforced random walk, the route taken by ‘walking’ particles depends on the previous routes of other particles. Here, we present a novel form of random walk in which the flow of particles provides this reinforcement. Starting from an analogy between electrical networks and random walks, we show how to include current reinforcement. We demonstrate that current-reinforcement results in particles converging on the optimal solution of shortest path transport problems, and avoids the self-reinforcing loops seen in standard density-based reinforcement models. We further develop a variant of the model that is biologically realistic, in the sense that the particles can be identified as ants and their measured density corresponds to those observed in maze-solving experiments on Argentine ants. For network formation, we identify the importance of nonlinear current reinforcement in producing networks that optimize both network maintenance and travel times. Other than ant trail formation, these random walks are also closely related to other biological systems, such as blood vessels and neuronal networks, which involve the transport of materials or information. We argue that current reinforcement is likely to be a common mechanism in a range of systems where network construction is observed.

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Wang, Yan, Ding Juan Wu, Fang Lv, and Meng Long Su. "Exploring activity-driven network with biased walks." International Journal of Modern Physics C 28, no. 09 (September 2017): 1750111. http://dx.doi.org/10.1142/s012918311750111x.

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We investigate the concurrent dynamics of biased random walks and the activity-driven network, where the preferential transition probability is in terms of the edge-weighting parameter. We also obtain the analytical expressions for stationary distribution and the coverage function in directed and undirected networks, all of which depend on the weight parameter. Appropriately adjusting this parameter, more effective search strategy can be obtained when compared with the unbiased random walk, whether in directed or undirected networks. Since network weights play a significant role in the diffusion process.

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Kalikova, A. "Statistical analysis of random walks on network." Scientific Journal of Astana IT University, no. 5 (July 27, 2021): 77–83. http://dx.doi.org/10.37943/aitu.2021.99.34.007.

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This paper describes an investigation of analytical formulas for parameters in random walks. Random walks are used to model situations in which an object moves in a sequence of steps in randomly chosen directions. Given a graph and a starting point, we select a neighbor of it at random, and move to this neighbor; then we select a neighbor of this point at random, and move to it etc. It is a fundamental dynamic process that arise in many models in mathematics, physics, informatics and can be used to model random processes inherent to many important applications. Different aspects of the theory of random walks on graphs are surveyed. In particular, estimates on the important parameters of hitting time, commute time, cover time are discussed in various works. In some papers, authors have derived an analytical expression for the distribution of the cover time for a random walk over an arbitrary graph that was tested for small values of n. However, this work will show the simplified analytical expressions for distribution of hitting time, commute time, cover time for bigger values of n. Moreover, this work will present the probability mass function and the cumulative distribution function for hitting time, commute time.

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Gannon, M., E. Pechersky, Y. Suhov, and A. Yambartsev. "Random walks in a queueing network environment." Journal of Applied Probability 53, no. 2 (June 2016): 448–62. http://dx.doi.org/10.1017/jpr.2016.12.

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Abstract We propose a class of models of random walks in a random environment where an exact solution can be given for a stationary distribution. The environment is cast in terms of a Jackson/Gordon–Newell network although alternative interpretations are possible. The main tool is the detailed balance equations. The difference compared to earlier works is that the position of the random walk influences the transition intensities of the network environment and vice versa, creating strong correlations. The form of the stationary distribution is closely related to the well-known product formula.

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Zheng, Zhongtuan, Hanxing Wang, Shengguo Gao, and Guoqiang Wang. "Comparison of Multiple Random Walks Strategies for Searching Networks." Mathematical Problems in Engineering 2013 (2013): 1–12. http://dx.doi.org/10.1155/2013/734630.

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We investigate diverse random-walk strategies for searching networks, especially multiple random walks (MRW). We use random walks on weighted networks to establish various models of single random walks and take the order statistics approach to study corresponding MRW, which can be a general framework for understanding random walks on networks. Multiple preferential random walks (MPRW) and multiple simple random walks (MSRW) are two special types of MRW. As search strategies, MPRW prefers high-degree nodes while MSRW searches for low-degree nodes more efficiently. We analyze the first passage time (FPT) of wandering walkers of MRW and give the corresponding formulas of probability distributions and moments, and the mean first passage time (MFPT) is included. We show the convergence of the MFPT of the first arriving walker and find the MFPT of the last arriving walker closely related with the mean cover time. Simulations confirm analytical predictions and deepen discussions. We use a small random network to test the FPT properties from different aspects. We also explore some practical search-related issues by MRW, such as detecting unknown shortest paths and avoiding poor routings on networks. Our results are of practical significance for realizing optimal routing and performing efficient search on complex networks.

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Asztalos, A., and Z. Toroczkai. "Network discovery by generalized random walks." EPL (Europhysics Letters) 92, no. 5 (December 1, 2010): 50008. http://dx.doi.org/10.1209/0295-5075/92/50008.

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Toth, Christian, Denis Helic, and Bernhard C. Geiger. "Synwalk: community detection via random walk modelling." Data Mining and Knowledge Discovery 36, no. 2 (January 10, 2022): 739–80. http://dx.doi.org/10.1007/s10618-021-00809-w.

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AbstractComplex systems, abstractly represented as networks, are ubiquitous in everyday life. Analyzing and understanding these systems requires, among others, tools for community detection. As no single best community detection algorithm can exist, robustness across a wide variety of problem settings is desirable. In this work, we present Synwalk, a random walk-based community detection method. Synwalk builds upon a solid theoretical basis and detects communities by synthesizing the random walk induced by the given network from a class of candidate random walks. We thoroughly validate the effectiveness of our approach on synthetic and empirical networks, respectively, and compare Synwalk’s performance with the performance of Infomap and Walktrap (also random walk-based), Louvain (based on modularity maximization) and stochastic block model inference. Our results indicate that Synwalk performs robustly on networks with varying mixing parameters and degree distributions. We outperform Infomap on networks with high mixing parameter, and Infomap and Walktrap on networks with many small communities and low average degree. Our work has a potential to inspire further development of community detection via synthesis of random walks and we provide concrete ideas for future research.

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XING, CHANGMING, LIN YANG, and LEI GUO. "RANDOM WALKS WITH A TRAP IN SCALE-FREE FRACTAL HIERARCHICAL LATTICES." Fractals 25, no. 06 (November 21, 2017): 1750058. http://dx.doi.org/10.1142/s0218348x1750058x.

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In this paper, we study two kinds of random walks with a trap in a class of scale-free fractal hierarchical lattices. One is standard random walks belonging to unbiased random walks, while the other one named mixed random walks is biased. The structural properties of these hierarchical lattices are controlled by a parameter [Formula: see text]. We derive exact solutions of the average trapping time (ATT) for the two trapping issue, respectively. The results show that in large networks, both of the ATT grow asymptotically as a power-law function of network size with the exponent related to the parameter [Formula: see text]. It indicates that network structure has a substantial effect on the efficiency of trapping processes performed in scale-free networks. Comparing the results obtained for the two different random walks, we find that changes of the walking rule have no effect on the leading exponent of the ATT, but could modify the coefficient of the formula for the ATT. The findings are helpful for better understanding the influence factor of random walks in complex systems.

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Ikeda, N. "Network formed by traces of random walks." Physica A: Statistical Mechanics and its Applications 379, no. 2 (June 2007): 701–13. http://dx.doi.org/10.1016/j.physa.2007.01.006.

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Більше джерел

Дисертації з теми "Random walks on network"

De, Bacco Caterina. "Decentralized network control, optimization and random walks on networks." Thesis, Paris 11, 2015. http://www.theses.fr/2015PA112164/document.

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Dans les dernières années, plusieurs problèmes ont été étudiés à l'interface entre la physique statistique et l'informatique. La raison étant que, souvent, ces problèmes peuvent être réinterprétés dans le langage de la physique des systèmes désordonnés, où un grand nombre de variables interagit à travers champs locales qui dépendent de l'état du quartier environnant. Parmi les nombreuses applications de l'optimisation combinatoire le routage optimal sur les réseaux de communication est l'objet de la première partie de la thèse. Nous allons exploiter la méthode de la cavité pour formuler des algorithmes efficaces de type ‘’message-passing’’ et donc résoudre plusieurs variantes du problème grâce à sa mise en œuvre numérique. Dans un deuxième temps, nous allons décrire un modèle pour approcher la version dynamique de la méthode de la cavité, ce qui permet de diminuer la complexité du problème de l'exponentielle de polynôme dans le temps. Ceci sera obtenu en utilisant le formalisme de ‘’Matrix Product State’’ de la mécanique quantique.Un autre sujet qui a suscité beaucoup d'intérêt en physique statistique de processus dynamiques est la marche aléatoire sur les réseaux. La théorie a été développée depuis de nombreuses années dans le cas que la topologie dessous est un réseau de dimension d. Au contraire le cas des réseaux aléatoires a été abordé que dans la dernière décennie, laissant de nombreuses questions encore ouvertes pour obtenir des réponses. Démêler plusieurs aspects de ce thème fera l'objet de la deuxième partie de la thèse. En particulier, nous allons étudier le nombre moyen de sites distincts visités au cours d'une marche aléatoire et caractériser son comportement en fonction de la topologie du graphe. Enfin, nous allons aborder les événements rares statistiques associées aux marches aléatoires sur les réseaux en utilisant le ‘’Large deviations formalism’’. Deux types de transitions de phase dynamiques vont se poser à partir de simulations numériques. Nous allons conclure décrivant les principaux résultats d'une œuvre indépendante développée dans le cadre de la physique hors de l'équilibre. Un système résoluble en deux particules browniens entouré par un bain thermique sera étudiée fournissant des détails sur une interaction à médiation par du bain résultant de la présence du bain
In the last years several problems been studied at the interface between statistical physics and computer science. The reason being that often these problems can be reinterpreted in the language of physics of disordered systems, where a big number of variables interacts through local fields dependent on the state of the surrounding neighborhood. Among the numerous applications of combinatorial optimisation the optimal routing on communication networks is the subject of the first part of the thesis. We will exploit the cavity method to formulate efficient algorithms of type message-passing and thus solve several variants of the problem through its numerical implementation. At a second stage, we will describe a model to approximate the dynamic version of the cavity method, which allows to decrease the complexity of the problem from exponential to polynomial in time. This will be obtained by using the Matrix Product State formalism of quantum mechanics. Another topic that has attracted much interest in statistical physics of dynamic processes is the random walk on networks. The theory has been developed since many years in the case the underneath topology is a d-dimensional lattice. On the contrary the case of random networks has been tackled only in the past decade, leaving many questions still open for answers. Unravelling several aspects of this topic will be the subject of the second part of the thesis. In particular we will study the average number of distinct sites visited during a random walk and characterize its behaviour as a function of the graph topology. Finally, we will address the rare events statistics associated to random walks on networks by using the large-deviations formalism. Two types of dynamic phase transitions will arise from numerical simulations, unveiling important aspects of these problems. We will conclude outlining the main results of an independent work developed in the context of out-of-equilibrium physics. A solvable system made of two Brownian particles surrounded by a thermal bath will be studied providing details about a bath-mediated interaction arising for the presence of the bath

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Maddalena, Daniela. "Stationary states in random walks on networks." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2016. http://amslaurea.unibo.it/10170/.

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In this thesis we dealt with the problem of describing a transportation network in which the objects in movement were subject to both finite transportation capacity and finite accomodation capacity. The movements across such a system are realistically of a simultaneous nature which poses some challenges when formulating a mathematical description. We tried to derive such a general modellization from one posed on a simplified problem based on asyncronicity in particle transitions. We did so considering one-step processes based on the assumption that the system could be describable through discrete time Markov processes with finite state space. After describing the pre-established dynamics in terms of master equations we determined stationary states for the considered processes. Numerical simulations then led to the conclusion that a general system naturally evolves toward a congestion state when its particle transition simultaneously and we consider one single constraint in the form of network node capacity. Moreover the congested nodes of a system tend to be located in adjacent spots in the network, thus forming local clusters of congested nodes.

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Zimmermann, Jochen [Verfasser], and Andreas [Akademischer Betreuer] Buchleitner. "Random walks with nonlinear interactions on heterogeneous networks = Random Walk mit nichtlinearen Wechselwirkungen auf heterogenen Netzwerken." Freiburg : Universität, 2015. http://d-nb.info/1123482381/34.

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Kolgushev, Oleg. "Influence of Underlying Random Walk Types in Population Models on Resulting Social Network Types and Epidemiological Dynamics." Thesis, University of North Texas, 2016. https://digital.library.unt.edu/ark:/67531/metadc955128/.

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Epidemiologists rely on human interaction networks for determining states and dynamics of disease propagations in populations. However, such networks are empirical snapshots of the past. It will greatly benefit if human interaction networks are statistically predicted and dynamically created while an epidemic is in progress. We develop an application framework for the generation of human interaction networks and running epidemiological processes utilizing research on human mobility patterns and agent-based modeling. The interaction networks are dynamically constructed by incorporating different types of Random Walks and human rules of engagements. We explore the characteristics of the created network and compare them with the known theoretical and empirical graphs. The dependencies of epidemic dynamics and their outcomes on patterns and parameters of human motion and motives are encountered and presented through this research. This work specifically describes how the types and parameters of random walks define properties of generated graphs. We show that some configurations of the system of agents in random walk can produce network topologies with properties similar to small-world networks. Our goal is to find sets of mobility patterns that lead to empirical-like networks. The possibility of phase transitions in the graphs due to changes in the parameterization of agent walks is the focus of this research as this knowledge can lead to the possibility of disruptions to disease diffusions in populations. This research shall facilitate work of public health researchers to predict the magnitude of an epidemic and estimate resources required for mitigation.

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Linn, Hanna. "Detecting quantum speedup for random walks with artificial neural networks." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-289347.

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Random walks on graphs are an essential base for crucial algorithms for solving problems, like the boolean satisfiability problem. A speedup of random walks could improve these algorithms. The quantum version of the random walk, quantum walk, is faster than random walks in specific cases, e.g., on some linear graphs. An analysis of when the quantum walk is faster than the random walk can be accomplished analytically or by simulating both the walks on the graph. The problem arises when the graphs grow in size and connectivity. There are no known general rules for what an arbitrary graph not having explicit symmetries should exhibit to promote the quantum walk. Simulations will only answer the question for one single case, and will not provide any general rules for properties the graph should have. Using artificial neural networks (ANNs) as an aid for detecting when the quantum walk is faster on average than random walk on graphs, going from an initial node to a target node, has been done before. The quantum speedup may not be more than polynomial if the initial state of the quantum walk is purely in the initial node of the graph. We investigate starting the quantum walk in various superposition states, with an additional auxiliary node, to maybe achieve a larger quantum speedup. We suggest different ways to add the auxiliary node and select one of these schemes for use in this thesis. The superposition states examined are two stabiliser states and two magic states, inspired by the Gottesman-Knill theorem. According to this theorem, starting a quantum algorithm in a magic state may give an exponential speedup, but starting in a stabilizer state cannot give an exponential speedup, given that only gates from the Clifford group are used in the algorithm, as well as measurements are performed in the Pauli basis. We show that it is possible to train an ANN to classify graphs into what quantum walk was the fastest for various initial states of the quantum walk. The ANN classifies linear graphs and random graphs better than a random guess. We also show that a convolutional neural network (CNN) with a deeper architecture than earlier proposed for the task, is better at classifying the graphs than before. Our findings pave the way for automated research in novel quantum walk-based algorithms.
Slumpvandringar på grafer är essensiella i viktiga algoritmer för att lösa olika problem, till exempel SAT, booleska uppfyllningsproblem (the satisfiability problem). Genom att göra slumpvandringar snabbare går det att förbättra dessa algoritmer. Kvantversionen av slumpvandringar, kvantvandringar, har visats vara snabbare än klassiska slumpvandringar i specifika fall, till exempel på vissa linjära grafer. Det går att analysera, analytiskt eller genom att simulera vandringarna på grafer, när kvantvandringen är snabbare än slumpvandingen. Problem uppstår dock när graferna blir större, har fler noder samt fler kanter. Det finns inga kända generella regler för vad en godtycklig graf, som inte har några explicita symmetrier, borde uppfylla för att främja kvantvandringen. Simuleringar kommer bara besvara frågan för ett enda fall. De kommer inte att ge några generella regler för vilka egenskaper grafer borde ha. Artificiella neuronnät (ANN) har tidigare används som hjälpmedel för att upptäcka när kvantvandringen är snabbare än slumpvandingen på grafer. Då jämförs tiden det tar i genomsnitt att ta sig från startnoden till slutnoden. Dock är det inte säkert att få kvantacceleration för vandringen om initialtillståndet för kvantvandringen är helt i startnoden. I det här projektet undersöker vi om det går att få en större kvantacceleration hos kvantvandringen genom att starta den i superposition med en extra nod. Vi föreslår olika sätt att lägga till den extra noden till grafen och sen väljer vi en för att använda i resen av projektet. De superpositionstillstånd som undersöks är två av stabilisatortillstånden och två magiska tillstång. Valen av dessa tillstånd är inspirerat av Gottesmann- Knill satsen. Enligt satsen så kan en algoritm som startar i ett magiskt tillstånd ha en exponetiell uppsnabbning, men att starta i någon stabilisatortillstånden inte kan ha det. Detta givet att grindarna som används i algoritmen är från Cliffordgruppen samt att alla mätningar är i Paulibasen. I projektet visar vi att det är möjligt att träna en ANN så att den kan klassificera grafer utifrån vilken kvantvandring, med olika initialtillstånd, som var snabbast. Artificiella neuronnätet kan klassificera linjära grafer och slumpmässiga grafer bättre än slumpen. Vi visar också att faltningsnätverk med en djupare arkitektur än tidigare föreslaget för uppgiften är bättre på att klassificera grafer än innan. Våra resultat banar vägen för en automatiserad forskning i nya kvantvandringsbaserade algoritmer.

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Lau, Hon Wai. "Random walk in networks : first passage time and speed analysis /." View abstract or full-text, 2009. http://library.ust.hk/cgi/db/thesis.pl?PHYS%202009%20LAU.

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Malmros, Jens. "Studies in respondent-driven sampling : Directed networks, epidemics, and random walks." Doctoral thesis, Stockholms universitet, Matematiska institutionen, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-129287.

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Respondent-driven sampling (RDS) is a link-tracing sampling methodology especially suitable for sampling hidden populations. A clever sampling mechanism and inferential procedures that facilitate asymptotically unbiased population estimates has contributed to the rising popularity of the method. The papers in this thesis extend RDS estimation theory to some population structures to which the classical RDS estimation framework is not applicable and analyse the behaviour of the RDS recruitment process.

At the time of the doctoral defense, the following papers were unpublished and had a status as follows: Paper 2: In press. Paper 3: Accepted. Paper 4: Manuscript.

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Russo, Elena Tea. "Fluctuation properties in random walks on networks and simple integrate and fire models." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2015. http://amslaurea.unibo.it/9565/.

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In questa tesi si è studiato l’insorgere di eventi critici in un semplice modello neurale del tipo Integrate and Fire, basato su processi dinamici stocastici markoviani definiti su una rete. Il segnale neurale elettrico è stato modellato da un flusso di particelle. Si è concentrata l’attenzione sulla fase transiente del sistema, cercando di identificare fenomeni simili alla sincronizzazione neurale, la quale può essere considerata un evento critico. Sono state studiate reti particolarmente semplici, trovando che il modello proposto ha la capacità di produrre effetti "a cascata" nell’attività neurale, dovuti a Self Organized Criticality (auto organizzazione del sistema in stati instabili); questi effetti non vengono invece osservati in Random Walks sulle stesse reti. Si è visto che un piccolo stimolo random è capace di generare nell’attività della rete delle fluttuazioni notevoli, in particolar modo se il sistema si trova in una fase al limite dell’equilibrio. I picchi di attività così rilevati sono stati interpretati come valanghe di segnale neurale, fenomeno riconducibile alla sincronizzazione.

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Xu, Keyulu. "Graph structures, random walks, and all that : learning graphs with jumping knowledge networks." Thesis, Massachusetts Institute of Technology, 2019. https://hdl.handle.net/1721.1/121660.

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This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Thesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2019
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 51-54).
Graph representation learning aims to extract high-level features from the graph structures and node features, in order to make predictions about the nodes and the graphs. Applications include predicting chemical properties of drugs, community detection in social networks, and modeling interactions in physical systems. Recent deep learning approaches for graph representation learning, namely Graph Neural Networks (GNNs), follow a neighborhood aggregation procedure, where the representation vector of a node is computed by recursively aggregating and transforming feature vectors of its neighboring nodes. We analyze some important properties of these models, and propose a strategy to overcome the limitations. In particular, the range of neighboring nodes that a node's representation draws from strongly depends on the graph structure, analogous to the spread of a random walk. To adapt to local neighborhood properties and tasks, we explore an architecture - jumping knowledge (JK) networks that flexibly leverages, for each node, different neighborhood ranges to enable better structure-aware representation. In a number of experiments on social, bioinformatics and citation networks, we demonstrate that our model achieves state-of-the-art performance. Furthermore, combining the JK framework with models like Graph Convolutional Networks, GraphSAGE and Graph Attention Networks consistently improves those models' performance.
by Keyulu Xu.
S.M.
S.M. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science

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Uguccioni, Marco. "Introduzione alla meccanica statistica dei random walk su network." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2020. http://amslaurea.unibo.it/21027/.

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Lo scopo della tesi è quello di studiare la dinamica stocastica di particelle che realizzano un random walk su network, focalizzandosi in particolare sulle proprietà dello stato stazionario del processo. Dopo aver introdotto i concetti fondamentali di network e random walk per singola particella, si generalizza la trattazione a N particelle non interagenti su network a capacità di trasporto e storage infinita (ISTC) e si ricava il relativo stato stazionario con una trattazione meccano-statistica, applicando il principio di massima entropia. Successivamente, per avvicinarsi a modelli di trasporto reali, si introducono nel sistema dei vincoli sulla capacità di trasporto e di storage (1-FTC+q-FSC) e si derivano le relazioni reciproche di Onsager, mostrando ancora una volta di poter trattare il sistema in termini termodinamici. Durante la trattazione si realizzano numerose simulazioni numeriche utilizzando codici sviluppati in C++ e ROOT, volte a confermare l'andamento analitico atteso dei sistemi studiati.

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Книги з теми "Random walks on network"

Transfiniteness for graphs, electrical networks, and random walks. Boston: Birkhäuser, 1996.

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Pál, Révész, Tóth Bálint, Paul Erdős Summer Research Center of Mathematics., and International Workshop on Random Walks (1998 : Budapest, Hungary), eds. Random walks. Budapest, Hungary: János Bolyai Mathematical Society, 1999.

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Hughes, B. D. Random walks and random environments. Oxford: Clarendon Press, 1995.

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Gut, Allan. Stopped Random Walks. New York, NY: Springer New York, 1988. http://dx.doi.org/10.1007/978-1-4757-1992-5.

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Gut, Allan. Stopped Random Walks. New York, NY: Springer New York, 2009. http://dx.doi.org/10.1007/978-0-387-87835-5.

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Shi, Zhan. Branching Random Walks. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-25372-5.

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Random walk in random and non-random environments. Hackensack, New Jersey: World Scientific, 2013.

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Random walk in random and non-random environments. Singapore: Teaneck, N.J., 1990.

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Random walk in random and non-random environments. 2nd ed. New Jersey: World Scientific, 2005.

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Intersections of random walks. Boston: Birkhäuser, 1991.

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Частини книг з теми "Random walks on network"

Zemanian, Armen H. "Transfinite Random Walks." In Pristine Transfinite Graphs and Permissive Electrical Networks, 149–71. Boston, MA: Birkhäuser Boston, 2001. http://dx.doi.org/10.1007/978-1-4612-0163-2_8.

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Aiyer, Anand, Xiao Liang, Nilu Nalini, and Omkant Pandey. "Random Walks and Concurrent Zero-Knowledge." In Applied Cryptography and Network Security, 24–44. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-57808-4_2.

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Rasteiro, D. M. L. D. "Random Walks in Electric Networks." In Intelligent Systems, Control and Automation: Science and Engineering, 259–65. Dordrecht: Springer Netherlands, 2012. http://dx.doi.org/10.1007/978-94-007-4722-7_24.

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Zemanian, A. H. "Random Walks on ω-Networks." In Harmonic Analysis and Discrete Potential Theory, 249–57. Boston, MA: Springer US, 1992. http://dx.doi.org/10.1007/978-1-4899-2323-3_20.

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Nachmias, Asaf. "Random Walks and Electric Networks." In Lecture Notes in Mathematics, 11–31. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-27968-4_2.

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Lawler, Gregory, and Lester Coyle. "Random walks and electrical networks." In The Student Mathematical Library, 53–62. Providence, Rhode Island: American Mathematical Society, 1999. http://dx.doi.org/10.1090/stml/002/09.

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Jorgensen, Palle E. T., and Erin P. J. Pearse. "Resistance Boundaries of Infinite Networks." In Random Walks, Boundaries and Spectra, 111–42. Basel: Springer Basel, 2011. http://dx.doi.org/10.1007/978-3-0346-0244-0_7.

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Hou, Lei, Kecheng Liu, and Jianguo Liu. "Navigated Random Walks on Amazon Book Recommendation Network." In Studies in Computational Intelligence, 935–45. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-72150-7_75.

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Sarkar, Purnamrita, and Andrew W. Moore. "Random Walks in Social Networks and their Applications: A Survey." In Social Network Data Analytics, 43–77. Boston, MA: Springer US, 2011. http://dx.doi.org/10.1007/978-1-4419-8462-3_3.

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Hoffmann, Till, Mason A. Porter, and Renaud Lambiotte. "Random Walks on Stochastic Temporal Networks." In Understanding Complex Systems, 295–313. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-36461-7_15.

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Тези доповідей конференцій з теми "Random walks on network"

Nguyen, Giang H., John Boaz Lee, Ryan A. Rossi, Nesreen K. Ahmed, Eunyee Koh, and Sungchul Kim. "Dynamic Network Embeddings: From Random Walks to Temporal Random Walks." In 2018 IEEE International Conference on Big Data (Big Data). IEEE, 2018. http://dx.doi.org/10.1109/bigdata.2018.8622109.

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Qian, Haifeng, Sani R. Nassif, and Sachin S. Sapatnekar. "Random walks in a supply network." In the 40th conference. New York, New York, USA: ACM Press, 2003. http://dx.doi.org/10.1145/775832.775860.

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Lu, Shan, Jieqi Kang, Weibo Gong, and Don Towsley. "Complex network comparison using random walks." In the 23rd International Conference. New York, New York, USA: ACM Press, 2014. http://dx.doi.org/10.1145/2567948.2579363.

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Cooper, Colin, Tomasz Radzik, and Yiannis Siantos. "Estimating network parameters using random walks." In 2012 Fourth International Conference on Computational Aspects of Social Networks (CASoN). IEEE, 2012. http://dx.doi.org/10.1109/cason.2012.6412374.

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Liew, Seng Pei, Tsubasa Takahashi, Shun Takagi, Fumiyuki Kato, Yang Cao, and Masatoshi Yoshikawa. "Network Shuffling: Privacy Amplification via Random Walks." In SIGMOD/PODS '22: International Conference on Management of Data. New York, NY, USA: ACM, 2022. http://dx.doi.org/10.1145/3514221.3526162.

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Lima, Luisa, and Joao Barros. "Random Walks on Sensor Networks." In 2007 5th International Symposium on Modeling and Optimization in Mobile, Ad Hoc and Wireless Networks (WiOpt). IEEE, 2007. http://dx.doi.org/10.1109/wiopt.2007.4480064.

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Shao-Ping Wang, Wen-Jiang Pei, and Zhen-Ya He. "Random walks on the neural network of C.elegans." In 2008 International Conference on Neural Networks and Signal Processing (ICNNSP). IEEE, 2008. http://dx.doi.org/10.1109/icnnsp.2008.4590327.

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Boghrati, Baktash, and Sachin S. Sapatnekar. "Incremental power network analysis using backward random walks." In 2012 17th Asia and South Pacific Design Automation Conference (ASP-DAC). IEEE, 2012. http://dx.doi.org/10.1109/aspdac.2012.6164983.

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Tomassini, Marco. "Random Walks on Local Optima Networks." In 2020 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2020. http://dx.doi.org/10.1109/cec48606.2020.9185569.

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Wu, Bin, Yijia Zhang, and Yuxin Wang. "Hyperbolic Attributed Network Embedding with self-adaptive Random Walks." In CIIS 2020: 2020 The 3rd International Conference on Computational Intelligence and Intelligent Systems. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3440840.3440859.

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Reeder, Leah, Aaron Jamison Hill, James Bradley Aimone, and William Mark Severa. Exploring Applications of Random Walks on Spiking Neural Algorithms. Office of Scientific and Technical Information (OSTI), September 2018. http://dx.doi.org/10.2172/1471656.

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Baggerly, K., D. Cox, and R. Picard. Adaptive importance sampling of random walks on continuous state spaces. Office of Scientific and Technical Information (OSTI), November 1998. http://dx.doi.org/10.2172/677157.

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Metcalf, Gilbert, and Kevin Hassett. Investment Under Alternative Return Assumptions: Comparing Random Walks and Mean Reversion. Cambridge, MA: National Bureau of Economic Research, March 1995. http://dx.doi.org/10.3386/t0175.

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Brooks, Rodney A. A Robot that Walks; Emergent Behaviors from a Carefully Evolved Network. Fort Belvoir, VA: Defense Technical Information Center, February 1989. http://dx.doi.org/10.21236/ada207958.

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Lo, Andrew, and A. Craig MacKinlay. Stock Market Prices Do Not Follow Random Walks: Evidence From a Simple Specification Test. Cambridge, MA: National Bureau of Economic Research, February 1987. http://dx.doi.org/10.3386/w2168.

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Cherupally, Sai Kiran. Hierarchical Random Boolean Network Reservoirs. Portland State University Library, January 2000. http://dx.doi.org/10.15760/etd.6238.

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Carley, Kathleen M., and Eunice J. Kim. Random Graph Standard Network Metrics Distributions in ORA. Fort Belvoir, VA: Defense Technical Information Center, March 2008. http://dx.doi.org/10.21236/ada487516.

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Goldsmith, Andrea J., Stephen Boyd, H. V. Poor, and Yonina Eldar. Complex Network Information Exchange in Random Wireless Environments. Fort Belvoir, VA: Defense Technical Information Center, June 2012. http://dx.doi.org/10.21236/ada576751.

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Shi, Cindy. Development of Novel Random Network Theory-Based Approaches to Identify Network Interactions among Nitrifying Bacteria. Office of Scientific and Technical Information (OSTI), July 2015. http://dx.doi.org/10.2172/1194724.

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Jain, Anjani, and John W. Mamer. Approximations for the Random Minimal Spanning Tree with Application to Network Provisioning. Fort Belvoir, VA: Defense Technical Information Center, November 1986. http://dx.doi.org/10.21236/ada204656.

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