Effects of Network Structure, Competition and Memory Time on Social Spreading Phenomena

Gleeson, James P.; O’Sullivan, Kevin P.; Baños, Raúl; Moreno, Yamir

doi:10.1103/physrevx.6.021019

Cited by 94 publications

(112 citation statements)

References 77 publications

(118 reference statements)

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“…Reference 15 provides a generative model for online sharing behaviour and distinguishes two distinct factors affecting meme popularity: the memory time of users (the visible time period of messages retweeted by the user) and the connectivity structure of the social network. On the other hand, inspired by epidemic spreading, the Susceptible-Infected-Recovered (SIR) model 16 is frequently used to represent the spread of information 17,18 .…”

Section: Introductionmentioning

confidence: 99%

Mechanistic modelling of viral spreading on empirical social network and popularity prediction

Feng

Lai

2018

Sci Rep

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Online social networks are becoming major platforms for people to exchange opinions and information. While spreading models have been used to study the dynamics of spreading on social networks, the actual spreading mechanism on social networks may be different from these previous models due to users’ limited attention and heterogeneous interests. The tractability of the spreading process in social networks allows us to develop a detailed and realistic model accounting for these factors. In addition, the empirical social networks have higher order correlations among node degrees, especially for directed networks like Twitter, that could affect the dynamics of spreading. Based on the analysis of the retweet process in the empirical Twitter network, we find both non-trivial correlations in network structures and non-standard spreading mechanisms for viral tweets. In particular, there is a strong evidence of information overload for retweeting behaviors that is not in line with the standard spreading model like the SIR (Susceptible, Infectious and Recovered) model, and can be described by a sublinear function. From these empirical findings, we introduce an intrinsic variable “attractiveness” to the message, describing the overall propensity for any node to retweet the message, and present the analytical equations to solve such an empirical process, validated through numerical simulations. The results from our model is consistent with findings from the empirical Twitter data. Our analysis also indicates a close relationship between the spreading sub-network structure and the final popularity of the information, leading to a method to predict the popularity of tweets more accurately than existing models.

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Section: Introductionmentioning

confidence: 99%

Mechanistic modelling of viral spreading on empirical social network and popularity prediction

Feng

Lai

2018

Sci Rep

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“…Therefore, theoretically, successful analysis methods and simulation algorithms developed in the Markovian framework can apply to non-Markovian problems [59]. In addition, it is worth pointing out that our formalism is not restricted to biochemical networks considered here and can be easily extended to any non-Markovian networks with a finite/infinite set of discrete states, such as epidemic spreading on complex networks [60][61][62][63].…”

Section: Discussionmentioning

confidence: 99%

Computation of stationary distributions in stochastic models of cellular processes with molecular memory

Zhang

2019

Preprint

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Modeling stochastic dynamics of intracellular processes has long rested on Markovian (i.e., memoryless) hypothesis. However, many of these processes are non-Markovian (i.e., memorial) due to, e.g., small reaction steps involved in synthesis or degradation of a macroscopic molecule.When interrogating aspects of a cellular network by experimental measurements (e.g., by singlemolecule and single-cell measurement technologies) of network components, a key need is to develop efficient approaches to simulate and compute joint distributions of these components. To cope with this computational challenge, we develop two efficient algorithms: stationary generalized Gillespie algorithm and stationary generalized finite state projection, both being established based on a stationary generalized chemical master equation. We show how these algorithms can be combined in a streamlined procedure for evaluation of non-Markovian effects in a general cellular network. Stationary distributions are evaluated in two models of constitutive and bursty gene expressions as well as a model of genetic toggle switch, each considering molecular memory. Our approach significantly expands the capability of stochastic simulation to investigate gene regulatory network dynamics, which has the potential to advance both understanding of molecular systems biology and design of synthetic circuits. Author summaryCellular systems are driven by interactions between subsystems via time-stamped discrete events, involving numerous components and reaction steps and spanning several time scales. Such biochemical reactions are subject to inherent noise due to the small numbers of molecules. Also, they could involve several small steps, creating a memory between individual events. Delineating 2 these molecular stochasticity and memory of biomolecular networks are continuing challenges for molecular systems biology. We present a novel approach to compute the probability distribution in stochastic models of cellular processes with molecular memory based on stationary generalized chemical master equation. We map a stochastic system with memory onto a Markovian model with effective reaction propensity functions. This formulation enables us to efficiently develop algorithms under the Markovian framework, and thus systematically analyze how molecular memories regulate stochastic behaviors of biomolecular networks. Here we propose two representative algorithms: stationary generalized Gillespie algorithm and stationary generalized finite state projection algorithm. The former generate realizations with Monte Carlo simulation, but the later compute approximations of the probability distribution by solving a truncated version of stochastic process. Our approach is demonstrated by applying it to three different examples from systems biology: generalized birth-death process, a stochastic toggle switch model, and a 3-stage gene expression model.  1 is a row vector with the same dimension as that of J A , J P and J  P are the stationary probability distributions correspondin...

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“…Ideas are part of an ecosystem, and they interact with each other through their scientific "hosts", akin to the dynamics of co-infection and super-infection studied in evolutionary epidemiology [1]. Competition for limited human time and attention is the most studied interaction [15,16,44]. However, there are also synergistic effects between ideas.…”

Section: Rp Mann and O Woolley-meza / Maintaining Intellectual DIVmentioning

confidence: 99%

Maintaining intellectual diversity in data science

Mann

Woolley-Meza

2017

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Editor: Tobias Kuhn (https://orcid.org/0000-0002-1267-0234) Solicited reviews: Jodi Schneider (https://orcid.org/0000-0002-5098-5667); Melissa Haendel (https://orcid.org/0000-0001-9114-8737); Bjarke Mønsted (https://orcid.org/0000-0003-3683-312X); Victor de Boer (https://orcid.org/0000-0001-9079-039X) Received 5 March 2017 Accepted 28 March 2017Abstract. Data science is a young and rapidly expanding field, but one which has already experienced several waves of temporarily-ubiquitous methodological fashions. In this paper we argue that a diversity of ideas and methodologies is crucial for the long term success of the data science community. Towards the goal of a healthy, diverse ecosystem of different statistical models and approaches, we review how ideas spread in the scientific community and the role of incentives in influencing which research ideas scientists pursue. We conclude with suggestions for how universities, research funders and other actors in the data science community can help to maintain a rich, eclectic statistical environment.

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Effects of Network Structure, Competition and Memory Time on Social Spreading Phenomena

Cited by 94 publications

References 77 publications

Mechanistic modelling of viral spreading on empirical social network and popularity prediction

Mechanistic modelling of viral spreading on empirical social network and popularity prediction

Computation of stationary distributions in stochastic models of cellular processes with molecular memory

Maintaining intellectual diversity in data science

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