Quantifying the Role of Homophily in Human Cooperation Using Multiplex Evolutionary Game Theory

Stefano, Alessandro Di; Scatá, Marialisa; Corte, Aurelio La; Lió, Píetro; Catania, Emanuele; Guardo, Ermanno; Pagano, Salvatore

doi:10.1371/journal.pone.0140646

Cited by 35 publications

(74 citation statements)

References 70 publications

(73 reference statements)

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“…Among them are practical models suitable to describe non-uniform transmission and disease interactions [32, 33]. Recent models further consider new features like awareness, homophily, and diverse activity patterns with multiplexes to explain cooperative actions [34–36]. In the same manner, we identified major temporal features of each rumor (such as the natural propagation cycle) based on the Periodic External Shock (PES) epidemic model proposed in earlier work [37].…”

Section: Introductionmentioning

confidence: 99%

Rumor Detection over Varying Time Windows

Kwon¹,

Cha²,

Jung³

2017

PLoS ONE

292

163

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This study determines the major difference between rumors and non-rumors and explores rumor classification performance levels over varying time windows—from the first three days to nearly two months. A comprehensive set of user, structural, linguistic, and temporal features was examined and their relative strength was compared from near-complete date of Twitter. Our contribution is at providing deep insight into the cumulative spreading patterns of rumors over time as well as at tracking the precise changes in predictive powers across rumor features. Statistical analysis finds that structural and temporal features distinguish rumors from non-rumors over a long-term window, yet they are not available during the initial propagation phase. In contrast, user and linguistic features are readily available and act as a good indicator during the initial propagation phase. Based on these findings, we suggest a new rumor classification algorithm that achieves competitive accuracy over both short and long time windows. These findings provide new insights for explaining rumor mechanism theories and for identifying features of early rumor detection.

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Section: Introductionmentioning

confidence: 99%

Rumor Detection over Varying Time Windows

Kwon¹,

Cha²,

Jung³

2017

PLoS ONE

292

163

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“…This condition represents a link between the two dynamics of the two spreading processes co-evolving in the multiplex network. To define and identify the set of nodes to be preventively isolated, we introduce a social network approach, considering a scale-free network for each layer of the multiplex network57, and taking into account centrality and awareness measures in a multiplex structure37. In our model, centrality is calculated using the eigenvector-like centrality measure, which allows to include the concept of influence in our analysis.…”

Section: Resultsmentioning

confidence: 99%

“…We consider the situation where the influence among layers is heterogeneous. Given a multiplex network and an influence matrix , we define the global heterogeneous eigenvector-like centrality of 3437. To identify the set of nodes to whom applying the preventive isolation strategy, we need also an awareness measure in the multiplex structure.…”

Section: Resultsmentioning

confidence: 99%

“…where Z ⊗ is the Khatri–Rao product of the influence matrix W and Z T 37. For each node, we define an overall measure of its centrality and awareness, denoted by θ i , in the multiplex network .…”

Section: Resultsmentioning

confidence: 99%

“…To the best of our knowledge, for the first time we face the even more intriguing and novel issue which consists of studying the spreading of the two processes without separating and constraining each of the spreading processes to only one of the layers. In other words, we decide not to disjoint the two processes in single layers, but rather to explore the complex dynamics of their coevolution in order to define a scenario as realistic as possible and more coherent with the real nature of multiplex networks333738. The study of the two dynamics on a multiplex network allows us highlighting the psychological dynamics of awareness, which is marked by cognitive limitation3940 and then a re-ranking of news based on the emerging matters.…”

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confidence: 99%

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The Impact of Heterogeneity and Awareness in Modeling Epidemic Spreading on Multiplex Networks

Scatá

Stefano

Lió

et al. 2016

Sci Rep

Self Cite

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In the real world, dynamic processes involving human beings are not disjoint. To capture the real complexity of such dynamics, we propose a novel model of the coevolution of epidemic and awareness spreading processes on a multiplex network, also introducing a preventive isolation strategy. Our aim is to evaluate and quantify the joint impact of heterogeneity and awareness, under different socioeconomic conditions. Considering, as case study, an emerging public health threat, Zika virus, we introduce a data-driven analysis by exploiting multiple sources and different types of data, ranging from Big Five personality traits to Google Trends, related to different world countries where there is an ongoing epidemic outbreak. Our findings demonstrate how the proposed model allows delaying the epidemic outbreak and increasing the resilience of nodes, especially under critical economic conditions. Simulation results, using data-driven approach on Zika virus, which has a growing scientific research interest, are coherent with the proposed analytic model.

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