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・ Network First
・ Network flow
・ Network for a New Culture
・ Network for Astronomy School Education
・ Network for Capacity Development in Nutrition
・ Network for Earthquake Engineering Simulation
・ Network for Education and Academic Rights
・ Network for Electronic Transfers
・ Network for Good
・ Network for Integrity in Reconstruction
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・ Network for Pancreatic Organ Donors with Diabetes
・ Network For Teaching Entrepreneurship
・ Network for the Promotion of Asian Cinema
・ Network forensics
Network formation
・ Network Foundation Technologies
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・ Network in Canadian History and Environment
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Network formation : ウィキペディア英語版
Network formation
Network formation is an aspect of network science that seeks to model how a network evolves by identifying which factors affect its structure and how these mechanisms operate. Network formation hypotheses are tested by using either a dynamic model with an increasing network size or by making an agent-based model to determine which network structure is the equilibrium in a fixed-size network.
==Dynamic models==
A dynamic model, often used by physicists and biologists, begins as a small network or even a single node. The modeler then uses a (usually randomized) rule on how newly arrived nodes form links in order to increase the size of the network. The aim is to determine what the properties the network will be when it grows in size. In this way, researchers try to reproduce properties common in most real networks, such as the small world network property or the scale-free network property. These properties are common in almost every real network including the World Wide Web, the metabolic network or the network of international air routes.
The oldest model of this type is the Erdős-Rényi model, in which new nodes randomly choose other nodes to connect to. A second well-known model is the Watts and Strogatz model, which starts from a standard two-dimensional lattice and evolves by replacing links randomly. These models display some realistic network properties, but fail to account for others.
One of the most influential models of network formation is the Barabási-Albert model. Here, the network also starts from a small system, and incoming nodes choose their links randomly, but the randomization is not uniform. Instead, nodes which already possess a greater number of links will have a higher likelihood of becoming connected to incoming nodes. This mechanism is known as preferential attachment. In comparison to previous models, the Barabási-Albert model seems to more accurately reflect phenomena observed in real-world networks.

抄文引用元・出典: フリー百科事典『 ウィキペディア(Wikipedia)
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