Literatura académica sobre el tema "Random walks on network"
Crea una cita precisa en los estilos APA, MLA, Chicago, Harvard y otros
Consulte las listas temáticas de artículos, libros, tesis, actas de conferencias y otras fuentes académicas sobre el tema "Random walks on network".
Junto a cada fuente en la lista de referencias hay un botón "Agregar a la bibliografía". Pulsa este botón, y generaremos automáticamente la referencia bibliográfica para la obra elegida en el estilo de cita que necesites: APA, MLA, Harvard, Vancouver, Chicago, etc.
También puede descargar el texto completo de la publicación académica en formato pdf y leer en línea su resumen siempre que esté disponible en los metadatos.
Artículos de revistas sobre el tema "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, n.º 04 (junio de 2012): 779–802. http://dx.doi.org/10.1142/s0129054112400369.
Texto completoMa, Qi, Anders Johansson, Atsushi Tero, Toshiyuki Nakagaki y David J. T. Sumpter. "Current-reinforced random walks for constructing transport networks". Journal of The Royal Society Interface 10, n.º 80 (6 de marzo de 2013): 20120864. http://dx.doi.org/10.1098/rsif.2012.0864.
Texto completoWang, Yan, Ding Juan Wu, Fang Lv y Meng Long Su. "Exploring activity-driven network with biased walks". International Journal of Modern Physics C 28, n.º 09 (septiembre de 2017): 1750111. http://dx.doi.org/10.1142/s012918311750111x.
Texto completoKalikova, A. "Statistical analysis of random walks on network". Scientific Journal of Astana IT University, n.º 5 (27 de julio de 2021): 77–83. http://dx.doi.org/10.37943/aitu.2021.99.34.007.
Texto completoGannon, M., E. Pechersky, Y. Suhov y A. Yambartsev. "Random walks in a queueing network environment". Journal of Applied Probability 53, n.º 2 (junio de 2016): 448–62. http://dx.doi.org/10.1017/jpr.2016.12.
Texto completoZheng, Zhongtuan, Hanxing Wang, Shengguo Gao y 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.
Texto completoAsztalos, A. y Z. Toroczkai. "Network discovery by generalized random walks". EPL (Europhysics Letters) 92, n.º 5 (1 de diciembre de 2010): 50008. http://dx.doi.org/10.1209/0295-5075/92/50008.
Texto completoToth, Christian, Denis Helic y Bernhard C. Geiger. "Synwalk: community detection via random walk modelling". Data Mining and Knowledge Discovery 36, n.º 2 (10 de enero de 2022): 739–80. http://dx.doi.org/10.1007/s10618-021-00809-w.
Texto completoXING, CHANGMING, LIN YANG y LEI GUO. "RANDOM WALKS WITH A TRAP IN SCALE-FREE FRACTAL HIERARCHICAL LATTICES". Fractals 25, n.º 06 (21 de noviembre de 2017): 1750058. http://dx.doi.org/10.1142/s0218348x1750058x.
Texto completoIkeda, N. "Network formed by traces of random walks". Physica A: Statistical Mechanics and its Applications 379, n.º 2 (junio de 2007): 701–13. http://dx.doi.org/10.1016/j.physa.2007.01.006.
Texto completoTesis sobre el tema "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.
Texto completoIn 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
Maddalena, Daniela. "Stationary states in random walks on networks". Master's thesis, Alma Mater Studiorum - Università di Bologna, 2016. http://amslaurea.unibo.it/10170/.
Texto completoZimmermann, Jochen [Verfasser] y 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.
Texto completoKolgushev, 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/.
Texto completoLinn, 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.
Texto completoSlumpvandringar 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.
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.
Texto completoMalmros, 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.
Texto completoAt 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.
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/.
Texto completoXu, 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.
Texto completoThesis: 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
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/.
Texto completoLibros sobre el tema "Random walks on network"
Transfiniteness for graphs, electrical networks, and random walks. Boston: Birkhäuser, 1996.
Buscar texto completoPál, Révész, Tóth Bálint, Paul Erdős Summer Research Center of Mathematics. y International Workshop on Random Walks (1998 : Budapest, Hungary), eds. Random walks. Budapest, Hungary: János Bolyai Mathematical Society, 1999.
Buscar texto completoHughes, B. D. Random walks and random environments. Oxford: Clarendon Press, 1995.
Buscar texto completoGut, Allan. Stopped Random Walks. New York, NY: Springer New York, 1988. http://dx.doi.org/10.1007/978-1-4757-1992-5.
Texto completoGut, Allan. Stopped Random Walks. New York, NY: Springer New York, 2009. http://dx.doi.org/10.1007/978-0-387-87835-5.
Texto completoShi, Zhan. Branching Random Walks. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-25372-5.
Texto completoRandom walk in random and non-random environments. Hackensack, New Jersey: World Scientific, 2013.
Buscar texto completoRandom walk in random and non-random environments. Singapore: Teaneck, N.J., 1990.
Buscar texto completoRandom walk in random and non-random environments. 2a ed. New Jersey: World Scientific, 2005.
Buscar texto completoIntersections of random walks. Boston: Birkhäuser, 1991.
Buscar texto completoCapítulos de libros sobre el tema "Random walks on network"
Zemanian, Armen H. "Transfinite Random Walks". En 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.
Texto completoAiyer, Anand, Xiao Liang, Nilu Nalini y Omkant Pandey. "Random Walks and Concurrent Zero-Knowledge". En Applied Cryptography and Network Security, 24–44. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-57808-4_2.
Texto completoRasteiro, D. M. L. D. "Random Walks in Electric Networks". En 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.
Texto completoZemanian, A. H. "Random Walks on ω-Networks". En 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.
Texto completoNachmias, Asaf. "Random Walks and Electric Networks". En Lecture Notes in Mathematics, 11–31. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-27968-4_2.
Texto completoLawler, Gregory y Lester Coyle. "Random walks and electrical networks". En The Student Mathematical Library, 53–62. Providence, Rhode Island: American Mathematical Society, 1999. http://dx.doi.org/10.1090/stml/002/09.
Texto completoJorgensen, Palle E. T. y Erin P. J. Pearse. "Resistance Boundaries of Infinite Networks". En Random Walks, Boundaries and Spectra, 111–42. Basel: Springer Basel, 2011. http://dx.doi.org/10.1007/978-3-0346-0244-0_7.
Texto completoHou, Lei, Kecheng Liu y Jianguo Liu. "Navigated Random Walks on Amazon Book Recommendation Network". En Studies in Computational Intelligence, 935–45. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-72150-7_75.
Texto completoSarkar, Purnamrita y Andrew W. Moore. "Random Walks in Social Networks and their Applications: A Survey". En Social Network Data Analytics, 43–77. Boston, MA: Springer US, 2011. http://dx.doi.org/10.1007/978-1-4419-8462-3_3.
Texto completoHoffmann, Till, Mason A. Porter y Renaud Lambiotte. "Random Walks on Stochastic Temporal Networks". En Understanding Complex Systems, 295–313. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-36461-7_15.
Texto completoActas de conferencias sobre el tema "Random walks on network"
Nguyen, Giang H., John Boaz Lee, Ryan A. Rossi, Nesreen K. Ahmed, Eunyee Koh y Sungchul Kim. "Dynamic Network Embeddings: From Random Walks to Temporal Random Walks". En 2018 IEEE International Conference on Big Data (Big Data). IEEE, 2018. http://dx.doi.org/10.1109/bigdata.2018.8622109.
Texto completoQian, Haifeng, Sani R. Nassif y Sachin S. Sapatnekar. "Random walks in a supply network". En the 40th conference. New York, New York, USA: ACM Press, 2003. http://dx.doi.org/10.1145/775832.775860.
Texto completoLu, Shan, Jieqi Kang, Weibo Gong y Don Towsley. "Complex network comparison using random walks". En the 23rd International Conference. New York, New York, USA: ACM Press, 2014. http://dx.doi.org/10.1145/2567948.2579363.
Texto completoCooper, Colin, Tomasz Radzik y Yiannis Siantos. "Estimating network parameters using random walks". En 2012 Fourth International Conference on Computational Aspects of Social Networks (CASoN). IEEE, 2012. http://dx.doi.org/10.1109/cason.2012.6412374.
Texto completoLiew, Seng Pei, Tsubasa Takahashi, Shun Takagi, Fumiyuki Kato, Yang Cao y Masatoshi Yoshikawa. "Network Shuffling: Privacy Amplification via Random Walks". En SIGMOD/PODS '22: International Conference on Management of Data. New York, NY, USA: ACM, 2022. http://dx.doi.org/10.1145/3514221.3526162.
Texto completoLima, Luisa y Joao Barros. "Random Walks on Sensor Networks". En 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.
Texto completoShao-Ping Wang, Wen-Jiang Pei y Zhen-Ya He. "Random walks on the neural network of C.elegans". En 2008 International Conference on Neural Networks and Signal Processing (ICNNSP). IEEE, 2008. http://dx.doi.org/10.1109/icnnsp.2008.4590327.
Texto completoBoghrati, Baktash y Sachin S. Sapatnekar. "Incremental power network analysis using backward random walks". En 2012 17th Asia and South Pacific Design Automation Conference (ASP-DAC). IEEE, 2012. http://dx.doi.org/10.1109/aspdac.2012.6164983.
Texto completoTomassini, Marco. "Random Walks on Local Optima Networks". En 2020 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2020. http://dx.doi.org/10.1109/cec48606.2020.9185569.
Texto completoWu, Bin, Yijia Zhang y Yuxin Wang. "Hyperbolic Attributed Network Embedding with self-adaptive Random Walks". En 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.
Texto completoInformes sobre el tema "Random walks on network"
Reeder, Leah, Aaron Jamison Hill, James Bradley Aimone y William Mark Severa. Exploring Applications of Random Walks on Spiking Neural Algorithms. Office of Scientific and Technical Information (OSTI), septiembre de 2018. http://dx.doi.org/10.2172/1471656.
Texto completoBaggerly, K., D. Cox y R. Picard. Adaptive importance sampling of random walks on continuous state spaces. Office of Scientific and Technical Information (OSTI), noviembre de 1998. http://dx.doi.org/10.2172/677157.
Texto completoMetcalf, Gilbert y Kevin Hassett. Investment Under Alternative Return Assumptions: Comparing Random Walks and Mean Reversion. Cambridge, MA: National Bureau of Economic Research, marzo de 1995. http://dx.doi.org/10.3386/t0175.
Texto completoBrooks, Rodney A. A Robot that Walks; Emergent Behaviors from a Carefully Evolved Network. Fort Belvoir, VA: Defense Technical Information Center, febrero de 1989. http://dx.doi.org/10.21236/ada207958.
Texto completoLo, Andrew y A. Craig MacKinlay. Stock Market Prices Do Not Follow Random Walks: Evidence From a Simple Specification Test. Cambridge, MA: National Bureau of Economic Research, febrero de 1987. http://dx.doi.org/10.3386/w2168.
Texto completoCherupally, Sai Kiran. Hierarchical Random Boolean Network Reservoirs. Portland State University Library, enero de 2000. http://dx.doi.org/10.15760/etd.6238.
Texto completoCarley, Kathleen M. y Eunice J. Kim. Random Graph Standard Network Metrics Distributions in ORA. Fort Belvoir, VA: Defense Technical Information Center, marzo de 2008. http://dx.doi.org/10.21236/ada487516.
Texto completoGoldsmith, Andrea J., Stephen Boyd, H. V. Poor y Yonina Eldar. Complex Network Information Exchange in Random Wireless Environments. Fort Belvoir, VA: Defense Technical Information Center, junio de 2012. http://dx.doi.org/10.21236/ada576751.
Texto completoShi, Cindy. Development of Novel Random Network Theory-Based Approaches to Identify Network Interactions among Nitrifying Bacteria. Office of Scientific and Technical Information (OSTI), julio de 2015. http://dx.doi.org/10.2172/1194724.
Texto completoJain, Anjani y John W. Mamer. Approximations for the Random Minimal Spanning Tree with Application to Network Provisioning. Fort Belvoir, VA: Defense Technical Information Center, noviembre de 1986. http://dx.doi.org/10.21236/ada204656.
Texto completo