His primary areas of investigation include Complex network, Topology, Statistical physics, Microeconomics and Scale-free network. Naoki Masuda combines subjects such as Synaptic plasticity, Spike-timing-dependent plasticity, Content-addressable memory and Neuron with his study of Complex network. His Topology research integrates issues from Evolutionary dynamics, Entire population, Fixation and Artificial intelligence.
His Evolutionary dynamics study incorporates themes from Welfare economics and Social dilemma. His Statistical physics research is multidisciplinary, relying on both Phase transition, Network model and Computer simulation. His work in the fields of Microeconomics, such as Game theory, intersects with other areas such as Political Elections.
His scientific interests lie mostly in Statistical physics, Artificial intelligence, Complex network, Social dilemma and Node. His work deals with themes such as Event and Nonlinear system, which intersect with Statistical physics. His research on Artificial intelligence focuses in particular on Artificial neural network.
His Complex network study combines topics from a wide range of disciplines, such as Degree and Random graph. The concepts of his Social dilemma study are interwoven with issues in Reciprocity, Game theory and Reinforcement learning. His Node research is multidisciplinary, incorporating elements of Theoretical computer science and Community structure.
Naoki Masuda focuses on Complex network, Node, Random graph, Statistical physics and Degree. Naoki Masuda performs multidisciplinary study on Complex network and Liner shipping in his works. His studies deal with areas such as Language model, Embedding and Algorithm as well as Node.
His studies examine the connections between Algorithm and genetics, as well as such issues in Markov chain, with regards to Random walk. His research in Statistical physics tackles topics such as Event which are related to areas like Node, Markov process, Enhanced Data Rates for GSM Evolution and Benchmark. In his work, Theoretical computer science is strongly intertwined with Core, which is a subfield of Degree.
Naoki Masuda spends much of his time researching Complex network, Network structure, Mathematical economics, Degree and Decomposition. His Complex network study frequently links to related topics such as Discrete time and continuous time. Naoki Masuda integrates several fields in his works, including Network structure, Autocorrelation, Competition, Air transport, Natural disaster and Economic geography.
His study in the field of Evolutionary game theory also crosses realms of Critical mass, Fraction and Tipping point. His Degree study combines topics in areas such as Theoretical computer science, Core, Random graph, Node and Community structure. He conducted interdisciplinary study in his works that combined Decomposition and Structure.
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Random walks and diffusion on networks
Naoki Masuda;Mason A. Porter;Mason A. Porter;Renaud Lambiotte.
Physics Reports (2017)
Cryptosystems with discretized chaotic maps
N. Masuda;K. Aihara.
IEEE Transactions on Circuits and Systems I-regular Papers (2002)
Spatial prisoner's dilemma optimally played in small-world networks
Naoki Masuda;Kazuyuki Aihara.
Physics Letters A (2003)
A Guide to Temporal Networks
Naoki Masuda;Renaud Lambiotte.
Participation costs dismiss the advantage of heterogeneous networks in evolution of cooperation.
Proceedings of The Royal Society B: Biological Sciences (2007)
Predicting and controlling infectious disease epidemics using temporal networks
Naoki Masuda;Petter Holme;Petter Holme;Petter Holme.
F1000 Medicine Reports (2013)
Global and local synchrony of coupled neurons in small-world networks
Naoki Masuda;Kazuyuki Aihara.
Biological Cybernetics (2004)
Chaotic block ciphers: from theory to practical algorithms
N. Masuda;G. Jakimoski;K. Aihara;L. Kocarev.
IEEE Transactions on Circuits and Systems I-regular Papers (2006)
Systematic analysis of neural projections reveals clonal composition of the Drosophila brain.
Masayoshi Ito;Naoki Masuda;Kazunori Shinomiya;Keita Endo.
Current Biology (2013)
A pairwise maximum entropy model accurately describes resting-state human brain networks
Takamitsu Watanabe;Satoshi Hirose;Hiroyuki Wada;Yoshio Imai.
Nature Communications (2013)
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