The small world yields the most effective information spreading
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In this article, the authors proposed a model to emphasize the essential difference between information spreading and epidemic spreading, where the memory effects, the social reinforcement and the non-redundancy of contacts are taken into account.Abstract:
The spreading dynamics of information and diseases are usually analyzed by using a unified framework and analogous models. In this paper, we propose a model to emphasize the essential difference between information spreading and epidemic spreading, where the memory effects, the social reinforcement and the non-redundancy of contacts are taken into account. Under certain conditions, the information spreads faster and broader in regular networks than in random networks, which to some extent supports the recent experimental observation of spreading in online society (Centola D 2010 Science 329 1194). At the same time, the simulation result indicates that the random networks tend to be favorable for effective spreading when the network size increases. This challenges the validity of the above-mentioned experiment for large-scale systems. More importantly, we show that the spreading effectiveness can be sharply enhanced by introducing a little randomness into the regular structure, namely the small-world networks yield the most effective information spreading. This work provides insights into the role of local clustering in information spreading.read more
Citations
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Vital nodes identification in complex networks
Linyuan Lü,Linyuan Lü,Duanbing Chen,Xiao-Long Ren,Qian-Ming Zhang,Yi-Cheng Zhang,Yi-Cheng Zhang,Tao Zhou +7 more
TL;DR: In this paper, the state-of-the-art algorithms for vital node identification in real networks are reviewed and compared, and extensive empirical analyses are provided to compare well-known methods on disparate real networks.
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Vital nodes identification in complex networks
Linyuan Lü,Linyuan Lü,Duanbing Chen,Xiao-Long Ren,Qian-Ming Zhang,Yi-Cheng Zhang,Yi-Cheng Zhang,Tao Zhou +7 more
TL;DR: This review clarifies the concepts and metrics, classify the problems and methods, as well as review the important progresses and describe the state of the art, and provides extensive empirical analyses to compare well-known methods on disparate real networks and highlight the future directions.
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Dynamics of information diffusion and its applications on complex networks
Zi-Ke Zhang,Zi-Ke Zhang,Chuang Liu,Xiu-Xiu Zhan,Xiu-Xiu Zhan,Xin Lu,Xin Lu,Chuxu Zhang,Yi-Cheng Zhang,Yi-Cheng Zhang +9 more
TL;DR: It is emphasized that information diffusion has great scientific depth and combines diverse research fields which makes it interesting for physicists as well as interdisciplinary researchers.
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Identifying influential nodes in large-scale directed networks: the role of clustering
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Identifying influential spreaders by weighted LeaderRank
TL;DR: According to the simulations on the standard SIR model, the weighted LeaderRank performs better than LeaderRank in three aspects: the ability to find out more influential spreaders; the higher tolerance to noisy data; and the higher robustness to intentional attacks.
References
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Collective dynamics of small-world networks
TL;DR: Simple models of networks that can be tuned through this middle ground: regular networks ‘rewired’ to introduce increasing amounts of disorder are explored, finding that these systems can be highly clustered, like regular lattices, yet have small characteristic path lengths, like random graphs.
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TL;DR: This book discusses the biology of host-microparasite associations, dynamics of acquired immunity heterogeneity within the human community indirectly transmitted helminths, and the ecology and genetics of hosts and parasites.
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Epidemic Spreading in Scale-Free Networks
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TL;DR: In this article, a wide list of topics ranging from opinion and cultural and language dynamics to crowd behavior, hierarchy formation, human dynamics, and social spreading are reviewed and connections between these problems and other, more traditional, topics of statistical physics are highlighted.
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Specificity and Stability in Topology of Protein Networks
Sergei Maslov,Kim Sneppen +1 more
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