Information evolution in complex networks
文献类型：期刊论文
| 作者 | Tian, Yang3,4; Li, Guoqi1,2 ; Sun, Pei4
|
| 刊名 | CHAOS
 |
| 出版日期 | 2022-07-01 |
| 卷号 | 32期号:7页码:21 |
| ISSN号 | 1054-1500 |
| DOI | 10.1063/5.0096009 |
| 通讯作者 | Li, Guoqi(guoqi.li@ia.ac.cn) ; Sun, Pei(peisun@tsinghua.edu.cn) |
| 英文摘要 | Many biological phenomena or social events critically depend on how information evolves in complex networks. However, a general theory to characterize information evolution is yet absent. Consequently, numerous unknowns remain about the mechanisms underlying information evolution. Among these unknowns, a fundamental problem, being a seeming paradox, lies in the coexistence of local randomness, manifested as the stochastic distortion of information content during individual-individual diffusion, and global regularity, illustrated by specific non-random patterns of information content on the network scale. Here, we attempt to formalize information evolution and explain the coexistence of randomness and regularity in complex networks. Applying network dynamics and information theory, we discover that a certain amount of information, determined by the selectivity of networks to the input information, frequently survives from random distortion. Other information will inevitably experience distortion or dissipation, whose speeds are shaped by the diversity of information selectivity in networks. The discovered laws exist irrespective of noise, but noise accounts for disturbing them. We further demonstrate the ubiquity of our discovered laws by analyzing the emergence of neural tuning properties in the primary visual and medial temporal cortices of animal brains and the emergence of extreme opinions in social networks. (c) 2022 Author(s). All article content, except where otherwise noted, is licensed under a Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
| WOS关键词 | BIASED ASSIMILATION ; CAPACITY ; TRANSMISSION ; DYNAMICS ; MODEL ; MT ; PREDICTABILITY ; REPRESENTATION ; SELECTIVITY ; PREDICTION |
| 资助项目 | Artificial and General Intelligence Research Program of Guo Qiang Research Institute at Tsinghua University[2020GQG1017] ; Tsinghua University Initiative Scientific Research Program |
| WOS研究方向 | Mathematics ; Physics |
| 语种 | 英语 |
| WOS记录号 | WOS:000886356600006 |
| 出版者 | AIP Publishing |
| 资助机构 | Artificial and General Intelligence Research Program of Guo Qiang Research Institute at Tsinghua University ; Tsinghua University Initiative Scientific Research Program |
| 源URL | [http://ir.ia.ac.cn/handle/173211/51257]  |
| 专题 | 数字内容技术与服务研究中心_听觉模型与认知计算 |
| 通讯作者 | Li, Guoqi; Sun, Pei |
| 作者单位 | 1.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 2.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China 3.Huawei Technol Co Ltd, Cent Res Inst, Lab Adv Comp & Storage, Labs 2012, Beijing 100084, Peoples R China 4.Tsinghua Univ, Dept Psychol, Tsinghua Lab Brain & Intelligence, Beijing 100084, Peoples R China |
| 推荐引用方式 GB/T 7714 | Tian, Yang,Li, Guoqi,Sun, Pei. Information evolution in complex networks[J]. CHAOS,2022,32(7):21. |
| APA | Tian, Yang,Li, Guoqi,&Sun, Pei.(2022).Information evolution in complex networks.CHAOS,32(7),21. |
| MLA | Tian, Yang,et al."Information evolution in complex networks".CHAOS 32.7(2022):21. |
入库方式： OAI收割
来源：自动化研究所
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