Jove
Visualize
お問い合わせ
JoVE
x logofacebook logolinkedin logoyoutube logo
JoVEについて
概要リーダーシップブログJoVEヘルプセンター
著者向け
出版プロセス編集委員会範囲と方針査読よくある質問投稿
図書館員向け
推薦の声購読アクセスリソース図書館諮問委員会よくある質問
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experimentsアーカイブ
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教員リソースセンター教員サイト
利用規約
プライバシーポリシー
ポリシー

関連する概念動画

Network Covalent Solids02:18

Network Covalent Solids

Network covalent solids contain a three-dimensional network of covalently bonded atoms as found in the crystal structures of nonmetals like diamond, graphite, silicon, and some covalent compounds, such as silicon dioxide (sand) and silicon carbide (carborundum, the abrasive on sandpaper). Many minerals have networks of covalent bonds.
To break or to melt a covalent network solid, covalent bonds must be broken. Because covalent bonds are relatively strong, covalent network solids are typically...
Protein Networks02:26

Protein Networks

An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
Protein Networks02:26

Protein Networks

An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
Assembly of Signaling Complexes01:30

Assembly of Signaling Complexes

Multiprotein signaling complexes are formed in a dynamic process involving protein-protein interactions at the cytoplasmic domain of transmembrane receptors or enzymatic and non-enzymatic proteins associated with the receptor. These complexes ensure the activation and propagation of intracellular signals that regulate cell functions.
Interaction domains in cell signaling
Interaction domains recognize exposed features of their binding partners containing post-translationally modified sequences,...
Network Function of a Circuit01:25

Network Function of a Circuit

Frequency response analysis in electrical circuits provides vital insights into a circuit's behavior as the frequency of the input signal changes. The transfer function, a mathematical tool, is instrumental in understanding this behavior. It defines the relationship between phasor output and input and comes in four types: voltage gain, current gain, transfer impedance, and transfer admittance. The critical components of the transfer function are the poles and zeros.
Investigation of Disease Outbreaks01:23

Investigation of Disease Outbreaks

Multistate foodborne outbreaks pose significant public health risks and require meticulous investigation to identify sources and implement control measures. The Centers for Disease Control and Prevention (CDC) utilizes a dynamic seven-step process for these investigations, integrating data from laboratories, interviews, and environmental assessments to protect public health.Outbreak Detection: The detection of multistate outbreaks typically begins with PulseNet, the CDC's national laboratory...

こちらも読む

関連記事

共著者、ジャーナル、引用グラフによってこの研究に関連する記事。

並び替え
Same author

Addressing the minimum fleet problem in on-demand urban mobility.

Nature·2018
Same author

Scaling Law of Urban Ride Sharing.

Scientific reports·2017
Same author

The dynamics of correlated novelties.

Scientific reports·2014
Same author

Exact results for the Kuramoto model with a bimodal frequency distribution.

Physical review. E, Statistical, nonlinear, and soft matter physics·2009
Same author

Random graph models of social networks.

Proceedings of the National Academy of Sciences of the United States of America·2002
Same author

Are randomly grown graphs really random?

Physical review. E, Statistical, nonlinear, and soft matter physics·2001
Same journal

Daily briefing: 'Cyborg' cockroaches breathe underwater with printed suit.

Nature·2026
Same journal

China boosts prestigious grants for young scientists - will it ease competition?

Nature·2026
Same journal

Incoming US science academy chief vows to 'double down' on research.

Nature·2026
Same journal

Author Correction: Synthesis of enantioenriched atropisomers by biocatalytic deracemization.

Nature·2026
Same journal

Electrodeposited self-assembled molecules for perovskite photovoltaics.

Nature·2026
Same journal

Neutrino's nursery found: the 'Shadow Blaster'.

Nature·2026
関連記事をすべて見る

関連する実験動画

Updated: Jun 30, 2026

Mass Spectrometry-Guided Genome Mining as a Tool to Uncover Novel Natural Products
11:13

Mass Spectrometry-Guided Genome Mining as a Tool to Uncover Novel Natural Products

Published on: March 12, 2020

複雑なネットワークの探索

S H Strogatz1

  • 1Department of Theoretical and Applied Mechanics and Center for Applied Mathematics, Cornell University, Ithaca, New York 14853-1503, USA. strogatz@cornell.edu

Nature
|March 22, 2001
PubMed
まとめ
この要約は機械生成です。

この研究は,さまざまな科学分野における複雑なネットワークの構造と動態を調査します. ネットワークトポロジーと集団行動を理解することは,神経生物学や統計物理学のような分野にとって極めて重要です.

さらに関連する動画

Integration of 5G Experimentation Infrastructures into a Multi-Site NFV Ecosystem
10:15

Integration of 5G Experimentation Infrastructures into a Multi-Site NFV Ecosystem

Published on: February 3, 2021

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
05:30

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

関連する実験動画

Last Updated: Jun 30, 2026

Mass Spectrometry-Guided Genome Mining as a Tool to Uncover Novel Natural Products
11:13

Mass Spectrometry-Guided Genome Mining as a Tool to Uncover Novel Natural Products

Published on: March 12, 2020

Integration of 5G Experimentation Infrastructures into a Multi-Site NFV Ecosystem
10:15

Integration of 5G Experimentation Infrastructures into a Multi-Site NFV Ecosystem

Published on: February 3, 2021

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
05:30

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

科学分野:

  • ネットワークは,神経生物学,統計物理学,システム生物学を含む多様な科学分野にとって根本的なものです.
  • 複雑なネットワークを調査するには,その構造的性質と集団的動態を分析する必要があります.

背景:

  • 食物網,インターネット,代謝ネットワークなどのシステムの配線図の特徴づけは,重要な課題です.
  • ネットワークトポロジーの基礎となる統一原則を特定することは,現在進行中の研究分野です.

研究 の 目的:

  • 複雑なネットワークの構造的特徴を理解する.
  • ネットワーク内の相互作用するダイナミック・システムの集団的行動を調査する.
  • 個々のダイナミクス,コップリングアーキテクチャ,および全体的なネットワーク機能の間の関係を探求する.

主な方法:

  • ネットワークトポロジーと構造特性の分析.
  • システム相互作用を理解するために,非線形ダイナミクス原理の適用.
  • 様々な複雑なシステムにおけるカップリングアーキテクチャの検討.

主要な成果:

  • 研究者は,複雑なネットワークの複雑な構造を解明し始めています.
  • ネットワーク内のダイナミック・システムの集団的行動に関する洞察が生まれつつある.
  • この研究は,現代の研究におけるネットワーク科学の基本的役割を強調している.

結論:

  • ネットワークの構造とダイナミクスを理解することは,科学的知識の進歩に不可欠です.
  • 複雑なネットワークは,活発に調査されている基本的な原則を示しています.
  • 相互接続されたシステムの振る舞いを完全に理解するためには,さらなる研究が必要である.