Influence maximization in complex networks through optimal percolation
Flaviano Morone,Hernán A. Makse +1 more
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TLDR
This work maps the problem onto optimal percolation in random networks to identify the minimal set of influencers, which arises by minimizing the energy of a many-body system, where the form of the interactions is fixed by the non-backtracking matrix of the network.Abstract:
The whole frame of interconnections in complex networks hinges on a specific set of structural nodes, much smaller than the total size, which, if activated, would cause the spread of information to the whole network, or, if immunized, would prevent the diffusion of a large scale epidemic. Localizing this optimal, that is, minimal, set of structural nodes, called influencers, is one of the most important problems in network science. Despite the vast use of heuristic strategies to identify influential spreaders, the problem remains unsolved. Here we map the problem onto optimal percolation in random networks to identify the minimal set of influencers, which arises by minimizing the energy of a many-body system, where the form of the interactions is fixed by the non-backtracking matrix of the network. Big data analyses reveal that the set of optimal influencers is much smaller than the one predicted by previous heuristic centralities. Remarkably, a large number of previously neglected weakly connected nodes emerges among the optimal influencers. These are topologically tagged as low-degree nodes surrounded by hierarchical coronas of hubs, and are uncovered only through the optimal collective interplay of all the influencers in the network. The present theoretical framework may hold a larger degree of universality, being applicable to other hard optimization problems exhibiting a continuous transition from a known phase.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.
Journal ArticleDOI
Influence of fake news in Twitter during the 2016 US presidential election
Alexandre Bovet,Hernán A. Makse +1 more
TL;DR: In this article, the authors use a dataset of 171 million tweets in the five months preceding the election day to identify 30 million tweets, from 2.2 million users, which contain a link to news outlets.
Journal ArticleDOI
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.
Journal ArticleDOI
The H-index of a network node and its relation to degree and coreness
TL;DR: A family of H-indices are obtained that can be used to measure a node's importance and it is proved that the convergence to coreness can be guaranteed even under an asynchronous updating process, allowing a decentralized local method of calculating a nodes' coreness in large-scale evolving networks.
Journal ArticleDOI
Random walks and diffusion on networks
TL;DR: The theory and applications of random walks on networks are surveyed, restricting ourselves to simple cases of single and non-adaptive random walkers, and three main types are distinguished: discrete-time random walks, node-centric continuous-timerandom walks, and edge-centric Continuous-Time random walks.
References
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Sergey Brin,Lawrence Page +1 more
TL;DR: This paper provides an in-depth description of Google, a prototype of a large-scale search engine which makes heavy use of the structure present in hypertext and looks at the problem of how to effectively deal with uncontrolled hypertext collections where anyone can publish anything they want.
Journal Article
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Sergey Brin,Lawrence Page +1 more
TL;DR: Google as discussed by the authors is a prototype of a large-scale search engine which makes heavy use of the structure present in hypertext and is designed to crawl and index the Web efficiently and produce much more satisfying search results than existing systems.
Book
Networks: An Introduction
TL;DR: This book brings together for the first time the most important breakthroughs in each of these fields and presents them in a coherent fashion, highlighting the strong interconnections between work in different areas.