Influential spreaders for recurrent epidemics on networks

Poux-Médard, Gaël; Pastor-Satorras, Romualdo; Castellano, Claudio

doi:10.1103/physrevresearch.2.023332

Cited by 17 publications

(7 citation statements)

References 27 publications

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“…The essential question of ranking the node spreading influence is how to estimate the outbreak size of the initial spreader [53][54][55][56]. To answer this question, one needs to fix the nonlinear coupling issue during the spreading process.…”

Section: Discussionmentioning

confidence: 99%

Rank the spreading influence of nodes using dynamic Markov process

et al. 2023

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Ranking the spreading influence of nodes is of great importance in practice and research. The key to ranking a node’s spreading ability is to evaluate the fraction of susceptible nodes being infected by the target node during the outbreak, i.e. the outbreak size. In this paper, we present a dynamic Markov process (DMP) method by integrating the Markov chain and the spreading process to evaluate the outbreak size of the initial spreader. Following the idea of the Markov process, this method solves the problem of nonlinear coupling by adjusting the state transition matrix and evaluating the probability of the susceptible node being infected by its infected neighbors. We have employed the susceptible-infected-recovered and susceptible-infected-susceptible models to test this method on real-world static and temporal networks. Our results indicate that the DMP method could evaluate the nodes’ outbreak sizes more accurately than previous methods for both single and multi-spreaders. Besides, it can also be employed to rank the influence of nodes accurately during the spreading process.

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Section: Discussionmentioning

confidence: 99%

Rank the spreading influence of nodes using dynamic Markov process

et al. 2023

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“…There is a large body of literature on influence maximization in complex networks, where various models have been used: linear threshold models [63][64][65], independent cascade [63], and simple contagion models (SI,SIS,SIR) [66][67][68][69], to name a few. Recently, these ideas have been also exported to higher-order networks [70,71].…”

Section: Influence Maximizationmentioning

confidence: 99%

Influential groups for seeding and sustaining nonlinear contagion in heterogeneous hypergraphs

St‐Onge¹,

Iacopini²,

Latora³

et al. 2021

Preprint

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Several biological and social contagion phenomena, such as superspreading events or social reinforcement, are the results of multi-body interactions, for which hypergraphs offer a natural mathematical description. In this paper, we develop a novel mathematical framework based on approximate master equations to study contagions on random hypergraphs with a heterogeneous structure, both in terms of group size (hyperedge cardinality) and of membership of nodes to groups (hyperdegree). The characterization of the inner dynamics of groups provides an accurate description of the contagion process, without losing the analytical tractability. Using a contagion model where multi-body interactions are mapped onto a nonlinear infection rate, our two main results show how large groups are influential, in the sense that they drive both the early spread of a contagion and its endemic state (i.e., its stationary state). First, we provide a detailed characterization of the phase transition, which can be continuous or discontinuous with a bistable regime, and derive analytical expressions for the critical and tricritical points. We find that large values of the third moment of the membership distribution suppress the emergence of a discontinuous phase transition. Furthermore, the combination of heterogeneous group sizes and nonlinear contagion facilitates the onset of a mesoscopic localization phase, where contagion is sustained only by the largest groups, thereby inhibiting bistability as well. Second, we formulate a simple problem of optimal seeding for hypergraph contagions to compare two strategies: tuning the allocation of seeds according to either node individual properties or according to group properties. We find that, when the contagion is sufficiently nonlinear, groups are more effective seeds of contagion than individual nodes.

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“…Several works study the interaction of information with users' attention [11], closely linked to information overload concepts [18], but not the interaction between the pieces of information themselves. On the other hand, whereas most of the modeling of spreading processes are based on either no competition [19,15] or perfect competition [17] assumption, it has been shown that relaxing this hypothesis leads to a better description of competitive spread [2] -with the example of Firefox and Chrome web browsers, whose respective popularities are correlated. According to this finding, a significant effort has been done in elaborating complex processes to simulate interaction [22,17] on real-world networks.…”

Section: Related Workmentioning

confidence: 99%

Information Interaction Profile of Choice Adoption

Poux-Médard,

Velcin,

Loudcher

2021

Preprint

Self Cite

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Interactions between pieces of information (entities) play a substantial role in the way an individual acts on them: adoption of a product, the spread of news, strategy choice, etc. However, the underlying interaction mechanisms are often unknown and have been little explored in the literature. We introduce an efficient method to infer both the entities interaction network and its evolution according to the temporal distance separating interacting entities; together, they form the interaction profile. The interaction profile allows characterizing the mechanisms of the interaction processes. We approach this problem via a convex model based on recent advances in multi-kernel inference. We consider an ordered sequence of exposures to entities (URL, ads, situations) and the actions the user exerts on them (share, click, decision). We study how users exhibit different behaviors according to combinations of exposures they have been exposed to. We show that the effect of a combination of exposures on a user is more than the sum of each exposure's independent effect-there is an interaction. We reduce this modeling to a non-parametric convex optimization problem that can be solved in parallel. Our method recovers state-of-the-art results on interaction processes on three real-world datasets and outperforms baselines in the inference of the underlying data generation mechanisms. Finally, we show that interaction profiles can be visualized intuitively, easing the interpretation of the model.

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Influential spreaders for recurrent epidemics on networks

Cited by 17 publications

References 27 publications

Rank the spreading influence of nodes using dynamic Markov process

Rank the spreading influence of nodes using dynamic Markov process

Influential groups for seeding and sustaining nonlinear contagion in heterogeneous hypergraphs

Information Interaction Profile of Choice Adoption

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