Difference between revisions of "Network Pattern"
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* [[Analytics]] ... [[Visualization]] ... [[Graphical Tools for Modeling AI Components|Graphical Tools]] ... [[Diagrams for Business Analysis|Diagrams]] & [[Generative AI for Business Analysis|Business Analysis]] ... [[Requirements Management|Requirements]] ... [[Loop]] ... [[Bayes]] ... [[Network Pattern]] | * [[Analytics]] ... [[Visualization]] ... [[Graphical Tools for Modeling AI Components|Graphical Tools]] ... [[Diagrams for Business Analysis|Diagrams]] & [[Generative AI for Business Analysis|Business Analysis]] ... [[Requirements Management|Requirements]] ... [[Loop]] ... [[Bayes]] ... [[Network Pattern]] | ||
* [http://networksciencebook.com/ Network Science |] [[Creatives#Albert-László Barabási|Albert-László Barabási]]... free online book to get started | * [http://networksciencebook.com/ Network Science |] [[Creatives#Albert-László Barabási|Albert-László Barabási]]... free online book to get started | ||
| − | * [[Excel]] ... [[LangChain#Documents|Documents]] ... [[Database]] ... [[Graph]] ... [[LlamaIndex]] | + | * [[Life~Meaning]] ... [[Consciousness]] ... [[Loop#Feedback Loop - Creating Consciousness|Creating Consciousness]] ... [[Quantum#Quantum Biology|Quantum Biology]] ... [[Orch-OR]] ... [[TAME]] ... [[Protein Folding & Discovery|Proteins]] |
| + | * [[Excel]] ... [[LangChain#Documents|Documents]] ... [[Database|Database; Vector & Relational]] ... [[Graph]] ... [[LlamaIndex]] | ||
* [[Computer Networks]] | * [[Computer Networks]] | ||
* [[Python#NetworkX|NetworkX]] a Python library for studying graphs and networks. | * [[Python#NetworkX|NetworkX]] a Python library for studying graphs and networks. | ||
| − | * | + | * [[Artificial General Intelligence (AGI) to Singularity]] ... [[Inside Out - Curious Optimistic Reasoning| Curious Reasoning]] ... [[Emergence]] ... [[Moonshots]] ... [[Explainable / Interpretable AI|Explainable AI]] ... [[Algorithm Administration#Automated Learning|Automated Learning]] |
* [[Math for Intelligence]] ... [[Finding Paul Revere]] ... [[Social Network Analysis (SNA)]] ... [[Dot Product]] ... [[Kernel Trick]] | * [[Math for Intelligence]] ... [[Finding Paul Revere]] ... [[Social Network Analysis (SNA)]] ... [[Dot Product]] ... [[Kernel Trick]] | ||
* [[COVID-19]] | * [[COVID-19]] | ||
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* [http://oracleofbacon.org/ The Oracle of Bacon] computes the Bacon number of any actor or actress from Wikipedia data. A previous implementation used IMDB data. ...[http://en.wikipedia.org/wiki/Six_Degrees_of_Kevin_Bacon Six Degrees of Kevin Bacon | Wikipedia] | * [http://oracleofbacon.org/ The Oracle of Bacon] computes the Bacon number of any actor or actress from Wikipedia data. A previous implementation used IMDB data. ...[http://en.wikipedia.org/wiki/Six_Degrees_of_Kevin_Bacon Six Degrees of Kevin Bacon | Wikipedia] | ||
| − | Network science is an academic field which studies complex networks such as telecommunication networks, computer networks, biological networks, cognitive and semantic networks, and social networks, considering distinct elements or actors represented by nodes (or vertices) and the connections between the elements or actors as links (or edges). The field draws on theories and methods including graph theory from mathematics, statistical mechanics from physics, data mining and information visualization from computer science, inferential modeling from statistics, and social structure from sociology. The United States National Research Council defines network science as "the study of network representations of physical, biological, and social phenomena leading to predictive models of these phenomena." [http://en.wikipedia.org/wiki/Network_science#:~:text=Network%20science%20is%20an%20academic,connections%20between%20the%20elements%20or Network science | Wikipedia] | + | A network pattern is a recurring structural arrangement or established topology of connections between nodes that dictates how they interact, communicate, or distribute resources within a system. This architecture bridges the gap between silicon and biology, echoing the principles of [[Creatives#Michael Levin|Michael Levin's]] bioelectric networks, where cells utilize voltage gradients not just for localized reactions, but to store pattern memories and coordinate large-scale decision-making. In this light, a well-architected network pattern serves as the digital nervous system of your enterprise—not merely a static set of cables, but a self-organizing ecosystem. Just as biological systems rely on intricate feedback loops to sustain life, your digital infrastructure uses topologies like mesh, hub-and-spoke, or decentralized peer-to-peer structures to define the rules of engagement. Here, nodes—whether they are distinct devices, data centers, or software microservices—act like individual cells, communicating continuously to optimize data flow and distribute resources. By mimicking the resilience found in nature, this approach allows the system to adapt to stress, heal around bottlenecks, and evolve with changing demands. Implementing the correct network pattern is therefore critical for scalability; it ensures that communication remains low-latency and secure, minimizing bottlenecks while maximizing availability across the entire digital ecosystem. |
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| + | "A key discovery of network science is that the architecture and the evolution of networks emerging in various domains of science, nature, and technology are rather similar to each other, allowing us to use the same mathematical tools to explore the organizing principles of all these systems." | ||
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| + | — Albert-László Barabási, Network Science | ||
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| + | Network science is an academic field which studies complex networks such as telecommunication networks, computer networks, biological networks, cognitive and semantic networks, and social networks, considering distinct elements or actors represented by nodes (or vertices) and the connections between the elements or actors as links (or edges). The field draws on theories and methods including graph theory from mathematics, statistical mechanics from physics, data mining and information visualization from computer science, inferential modeling from statistics, and social structure from sociology. The United States National Research Council defines network science as "the study of network representations of physical, biological, and social phenomena leading to [[Predictive Analytics|predictive models]] of these phenomena." [http://en.wikipedia.org/wiki/Network_science#:~:text=Network%20science%20is%20an%20academic,connections%20between%20the%20elements%20or Network science | Wikipedia] | ||
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Latest revision as of 08:24, 7 January 2026
Youtube search... ...Google search
- Analytics ... Visualization ... Graphical Tools ... Diagrams & Business Analysis ... Requirements ... Loop ... Bayes ... Network Pattern
- Network Science | Albert-László Barabási... free online book to get started
- Life~Meaning ... Consciousness ... Creating Consciousness ... Quantum Biology ... Orch-OR ... TAME ... Proteins
- Excel ... Documents ... Database; Vector & Relational ... Graph ... LlamaIndex
- Computer Networks
- NetworkX a Python library for studying graphs and networks.
- Artificial General Intelligence (AGI) to Singularity ... Curious Reasoning ... Emergence ... Moonshots ... Explainable AI ... Automated Learning
- Math for Intelligence ... Finding Paul Revere ... Social Network Analysis (SNA) ... Dot Product ... Kernel Trick
- COVID-19
- Network Science.org ...power-law (scale-free) node-degree distributions are a property of only sparsely connected networks. More densely connected networks show an increasing divergence from power-law
- An Experimental Study of the Small World Problem | Jeffrey Travers and Stanley Milgram
- The Square and the Tower | Niall Ferguson
- The Oracle of Bacon computes the Bacon number of any actor or actress from Wikipedia data. A previous implementation used IMDB data. ...Six Degrees of Kevin Bacon | Wikipedia
A network pattern is a recurring structural arrangement or established topology of connections between nodes that dictates how they interact, communicate, or distribute resources within a system. This architecture bridges the gap between silicon and biology, echoing the principles of Michael Levin's bioelectric networks, where cells utilize voltage gradients not just for localized reactions, but to store pattern memories and coordinate large-scale decision-making. In this light, a well-architected network pattern serves as the digital nervous system of your enterprise—not merely a static set of cables, but a self-organizing ecosystem. Just as biological systems rely on intricate feedback loops to sustain life, your digital infrastructure uses topologies like mesh, hub-and-spoke, or decentralized peer-to-peer structures to define the rules of engagement. Here, nodes—whether they are distinct devices, data centers, or software microservices—act like individual cells, communicating continuously to optimize data flow and distribute resources. By mimicking the resilience found in nature, this approach allows the system to adapt to stress, heal around bottlenecks, and evolve with changing demands. Implementing the correct network pattern is therefore critical for scalability; it ensures that communication remains low-latency and secure, minimizing bottlenecks while maximizing availability across the entire digital ecosystem.
"A key discovery of network science is that the architecture and the evolution of networks emerging in various domains of science, nature, and technology are rather similar to each other, allowing us to use the same mathematical tools to explore the organizing principles of all these systems."
— Albert-László Barabási, Network Science
Network science is an academic field which studies complex networks such as telecommunication networks, computer networks, biological networks, cognitive and semantic networks, and social networks, considering distinct elements or actors represented by nodes (or vertices) and the connections between the elements or actors as links (or edges). The field draws on theories and methods including graph theory from mathematics, statistical mechanics from physics, data mining and information visualization from computer science, inferential modeling from statistics, and social structure from sociology. The United States National Research Council defines network science as "the study of network representations of physical, biological, and social phenomena leading to predictive models of these phenomena." Network science | Wikipedia
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Albert-László Barabási
- Wikipedia
- Albert-László Barabási Website
- Linked: How Everything Is Connected to Everything Else and What It Means for Business, Science, and Everyday Life | Albert-László Barabási ... Amazon
- Network Science | Albert-László Barabási ...Amazon
- Bursts: The Hidden Patterns Behind Everything We Do, from Your E-mail to Bloody Crusades | Albert-László Barabási ...Amazon
- The Formula: The Universal Laws of Success | Albert-László Barabási ... Amazon
Albert-László Barabási is a Hungarian/Romanian network scientist and author. He is also the Robert Gray Dodge Professor of Network Science and a Distinguished University Professor at Northeastern University, where he directs the Center for Complex Network Research, and holds appointments in the Departments of Physics and College of Computer and Information Science, as well as in the Department of Medicine at Harvard Medical School and Brigham and Women Hospital in the Channing Division of Network Science, and is a member of the Center for Cancer Systems Biology at Dana Farber Cancer Institute.