Academic literature on the topic 'Random walks on network'
Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles
Consult the lists of relevant articles, books, theses, conference reports, and other scholarly sources on the topic 'Random walks on network.'
Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.
You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.
Journal articles on the topic "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.
Full textMa, 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.
Full textWang, 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.
Full textKalikova, 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.
Full textGannon, 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.
Full textZheng, 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.
Full textAsztalos, 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.
Full textToth, 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.
Full textXING, 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.
Full textIkeda, 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.
Full textDissertations / Theses on the topic "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.
Full textIn 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/.
Full textZimmermann, 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.
Full textKolgushev, 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/.
Full textLinn, 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.
Full textSlumpvandringar 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.
Full textMalmros, 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.
Full textAt 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/.
Full textXu, 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.
Full textThesis: 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/.
Full textBooks on the topic "Random walks on network"
Transfiniteness for graphs, electrical networks, and random walks. Boston: Birkhäuser, 1996.
Find full textPál, 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.
Find full textGut, Allan. Stopped Random Walks. New York, NY: Springer New York, 1988. http://dx.doi.org/10.1007/978-1-4757-1992-5.
Full textGut, Allan. Stopped Random Walks. New York, NY: Springer New York, 2009. http://dx.doi.org/10.1007/978-0-387-87835-5.
Full textShi, Zhan. Branching Random Walks. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-25372-5.
Full textRandom walk in random and non-random environments. Hackensack, New Jersey: World Scientific, 2013.
Find full textRandom walk in random and non-random environments. 2nd ed. New Jersey: World Scientific, 2005.
Find full textBook chapters on the topic "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.
Full textAiyer, 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.
Full textRasteiro, 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.
Full textZemanian, 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.
Full textNachmias, 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.
Full textLawler, 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.
Full textJorgensen, 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.
Full textHou, 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.
Full textSarkar, 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.
Full textHoffmann, 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.
Full textConference papers on the topic "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.
Full textQian, 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.
Full textLu, 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.
Full textCooper, 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.
Full textLiew, 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.
Full textLima, 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.
Full textShao-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.
Full textBoghrati, 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.
Full textTomassini, 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.
Full textWu, 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.
Full textReports on the topic "Random walks on network"
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.
Full textBaggerly, 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.
Full textMetcalf, 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.
Full textBrooks, 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.
Full textLo, 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.
Full textCherupally, Sai Kiran. Hierarchical Random Boolean Network Reservoirs. Portland State University Library, January 2000. http://dx.doi.org/10.15760/etd.6238.
Full textCarley, 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.
Full textGoldsmith, 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.
Full textShi, 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.
Full textJain, 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.
Full text