Searching for superspreaders of information in real-world social media

Pei, Sen; Muchnik, Lev; Andrade, José S.; Zheng, Zhiming; Makse, Hernán A.

doi:10.1038/srep05547

Cited by 354 publications

(316 citation statements)

References 55 publications

Supporting

Mentioning

289

Contrasting

Unclassified

Order By: Relevance

“…analyses of social contagion for online social networks, such as Twitter and Facebook (45,46), individuals' proximity to the core of the network is not predictive of social influence (generalized linear model fit with weighted k-core: likelihood ratio test, P = 0:18; SI Appendix, Table S2). In addition, individuals with a high betweenness centrality are less likely to be influential (SI Appendix).…”

Section: Predicting Behavioral Cascades and The Relationship Between mentioning

confidence: 99%

Revealing the hidden networks of interaction in mobile animal groups allows prediction of complex behavioral contagion

Rosenthal¹,

Twomey

Hartnett³

et al. 2015

Proc. Natl. Acad. Sci. U.S.A.

419

453

View full text Add to dashboard Cite

Coordination among social animals requires rapid and efficient transfer of information among individuals, which may depend crucially on the underlying structure of the communication network. Establishing the decision-making circuits and networks that give rise to individual behavior has been a central goal of neuroscience. However, the analogous problem of determining the structure of the communication network among organisms that gives rise to coordinated collective behavior, such as is exhibited by schooling fish and flocking birds, has remained almost entirely neglected. Here, we study collective evasion maneuvers, manifested through rapid waves, or cascades, of behavioral change (a ubiquitous behavior among taxa) in schooling fish (Notemigonus crysoleucas). We automatically track the positions and body postures, calculate visual fields of all individuals in schools of ∼150 fish, and determine the functional mapping between socially generated sensory input and motor response during collective evasion. We find that individuals use simple, robust measures to assess behavioral changes in neighbors, and that the resulting networks by which behavior propagates throughout groups are complex, being weighted, directed, and heterogeneous. By studying these interaction networks, we reveal the (complex, fractional) nature of social contagion and establish that individuals with relatively few, but strongly connected, neighbors are both most socially influential and most susceptible to social influence. Furthermore, we demonstrate that we can predict complex cascades of behavioral change at their moment of initiation, before they actually occur. Consequently, despite the intrinsic stochasticity of individual behavior, establishing the hidden communication networks in large self-organized groups facilitates a quantitative understanding of behavioral contagion.T he social transmission of behavioral change is central to collective animal behavior. For many mobile groups, such as schooling fish and flocking birds, social contagion can be fast, resulting in dramatic waves of response (1-6). Such waves are evident in particular when individuals are under threat of attack from predators (1). Despite the ubiquity and importance of behavioral contagion, and the fact that survival depends on how individual interactions scale to collective properties (2), we still know very little about the sensory basis and mechanism of such coordinated collective response.In the early 20th century, Edmund Selous proposed that rapid waves of turning in large flocks of birds resulted from a direct transference of thoughts among animals: "They must think collectively, all at the same time. . . a flash out of so many brains" (3). By the mid-1950s, however, attention had turned from telepathy to synchrony arising from the rapid transmission of local behavioral response to neighbors, with some of the first experimental studies of cascading behavioral change undertaken by Dimitrii Radakov (4). Radakov (4) hand-traced the paths of each fish, frame-by-fram...

show abstract

Section: Predicting Behavioral Cascades and The Relationship Between mentioning

confidence: 99%

Revealing the hidden networks of interaction in mobile animal groups allows prediction of complex behavioral contagion

Rosenthal¹,

Twomey

Hartnett³

et al. 2015

Proc. Natl. Acad. Sci. U.S.A.

419

453

View full text Add to dashboard Cite

show abstract

“…These influential spreaders identification algorithms are applied on different topological network representation. Then the effectiveness of different identification algorithms on different topological network representations are evaluated by comparing ranking list obtained generated by each identification algorithm with ranking list obtained by tracking diffusion links in real spreading dynamics of information [18]. The findings of this paper (presented in result section) are significant in understanding information spread with the real OSNs and on selecting the most efficient algorithms for identifying influential spreaders.…”

Section: Introductionmentioning

confidence: 95%

“…Classical centrality measures, such as degree, closeness centrality, betweenness centrality and eigenvector centrality, are direct methods for recognizing the influential spreaders. However, closeness centrality and betweenness centrality have very high computational complexity, hence, it is not suitable to be applied into very largescale OSNs [18,19]. This limitation has made impractical for large OSNs.…”

Section: Related Workmentioning

confidence: 99%

“…The research [28] showed that the most efficient spreaders are those located within the core of the network as identified by the k-shell decomposition analysis. Recently Pei et al have conducted a research [18] with large OSNs datasets and reported that the most influential spreaders are placed in the k-core. The k-core algorithm performs better than degree centrality and PageRank.…”

Section: Related Workmentioning

confidence: 99%

“…The k-core algorithm performs better than degree centrality and PageRank. The performance of aforementioned algorithms have been tested on single layer OSN such as followers network on Twitter [22,29] , retweet network [25,26] or mention network [18].…”

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

Identifying the influential spreaders in multilayer interactions of online social networks

Al-Garadi

Varathan

Ravana

et al. 2016

IFS

View full text Add to dashboard Cite

Abstract. Online social networks (OSNs) portray a multi-layer of interactions through which users become a friend, information is propagated, ideas are shared, and interaction is constructed within an OSN. Identifying the most influential spreaders in a network is a significant step towards improving the use of existing resources to speed up the spread of information for application such as viral marketing or hindering the spread of information for application like virus blocking and rumor restraint. Users communications facilitated by OSNs could confront the temporal and spatial limitations of traditional communications in an exceptional way, thereby presenting new layers of social interactions, which coincides and collaborates with current interaction layers to redefine the multiplex OSN. In this paper, the effects of different topological network structure on influential spreaders identification are investigated. The results analysis concluded that improving the accuracy of influential spreaders identification in OSNs is not only by improving identification algorithms but also by developing a network topology that represents the information diffusion well. Moreover, in this paper a topological representation for an OSN is proposed which takes into accounts both multilayers interactions as well as overlaying links as weight. The measurement results are found to be more reliable when the identification algorithms are applied to proposed topological representation compared when these algorithms are applied to single layer representations.

show abstract

Immunization strategy for epidemic spreading based on membership (m) over a multilayer network

et al. 2019

View full text Add to dashboard Cite

The purpose of this paper is to declare an immunization strategy for epidemic spreading based on membership (m) of a node among various communities over a multilayer network. The past few years have seen exponential growth in the understanding and research in modern complex networks field. The mapping of real‐world social interactions to complex networks has opened a new pathway for innovation and research. The real‐world interactions among people take place in various circumstances, and thus, a complex network imitates these interactions using various layers. The events and its information in a real world are incorporated in multiple layers in a network that otherwise are lost due to layer aggregation. The form of interaction captured in a multilayer network can have effects of spreading of a process such as an epidemic. The findings explain the highest membership nodes in one layer, immunizing it and observing the propagation of the epidemic through the layers based on a fraction of overlapped nodes in the network that transmit the disease to various layers. Further, this research paper tried to measure the efficiency of the immunization strategy for epidemic spreading based on membership metric when applied on a multilayer network and henceforth apply them to prevent spreading of the rumor/disease. In this manner, the strategy is more productive to secure the people of the entire system.

show abstract

Searching for superspreaders of information in real-world social media

Cited by 354 publications

References 55 publications

Revealing the hidden networks of interaction in mobile animal groups allows prediction of complex behavioral contagion

Revealing the hidden networks of interaction in mobile animal groups allows prediction of complex behavioral contagion

Identifying the influential spreaders in multilayer interactions of online social networks

Immunization strategy for epidemic spreading based on membership (m) over a multilayer network

Contact Info

Product

Resources

About