Dynamics Of Social Contagions Research Articles

Enhancing predictive accuracy in social contagion dynamics via directed hypergraph structures

Evidence from both theoretical and empirical studies suggests that higher-order networks have emerged as powerful tools for modeling social contagions, such as opinion formation. In this article, we develop a model of social contagion on directed hypergraphs by considering the heterogeneity of individuals and environments in terms of reinforcing contagion effects. By distinguishing the directedness between nodes and hyperedges, we find that the bistable interval of the discontinuous phase transition decreases as the directedness strength decreases. Additionally, directed hypergraphs tend to generate bistable intervals when nodes with a large hyperdegree are more likely to adopt a specific opinion, as evidenced by simulations of directionality assignments for three sets of real networks. These findings provide two approaches to enhance the accuracy of predicting social contagion dynamics: one is to increase the stubbornness of all individuals, and the other is to prioritize increasing the stubbornness of highly influential individuals.

Insights from exact social contagion dynamics on networks with higher-order structures

Abstract Recently, there has been an increasing interest in studying dynamical processes on networks exhibiting higher-order structures, such as simplicial complexes, where the dynamics acts above and beyond dyadic interactions. Using simulations or heuristically derived epidemic spreading models, it was shown that new phenomena can emerge, such as bi-stability/multistability. Here, we show that such new emerging phenomena do not require complex contact patterns, such as community structures, but naturally result from the higher-order contagion mechanisms. We show this by deriving an exact higher-order Susceptible-Infected-Susceptible model and its limiting mean-field equivalent for fully connected simplicial complexes. Going beyond previous results, we also give the global bifurcation picture for networks with 3- and 4-body interactions, with the latter allowing for two non-trivial stable endemic steady states. Differently from previous approaches, we are able to study systems featuring interactions of arbitrary order. In addition, we characterize the contributions from higher-order infections to the endemic equilibrium as perturbations of the pairwise baseline, finding that these diminish as the pairwise rate of infection increases. Our approach represents a first step towards a principled understanding of higher-order contagion processes beyond triads and opens up further directions for analytical investigations.

Reference to Global State and Social Contagion Dynamics

The network-based model of social contagion has revolved around information on local interactions; its central focus has been on network topological properties shaping the local interactions and, ultimately, social contagion outcomes. We extend this approach by introducing information on the global state, or global information, into the network-based model and analyzing how it alters social contagion dynamics in six different classes of networks: a two-dimensional square lattice, small-world networks, Erdős-Rényi networks, regular random networks, Holme-Kim networks, and Barabási-Albert networks. We find that there is an optimal amount of global information that minimizes the time to reach global cascades in highly clustered networks. We also find that global information prolongs the time to hit the tipping point but substantially compresses the time to reach global cascades after then, so that the overall time to reach global cascades can even be shortened under certain conditions. Finally, we show that random links substitute for global information in regulating the social contagion dynamics.

Quantum Contagion: A Quantum-Like Approach for the Analysis of Social Contagion Dynamics with Heterogeneous Adoption Thresholds.

Modeling the information of social contagion processes has recently attracted a substantial amount of interest from researchers due to its wide applicability in network science, multi-agent-systems, information science, and marketing. Unlike in biological spreading, the existence of a reinforcement effect in social contagion necessitates considering the complexity of individuals in the systems. Although many studies acknowledged the heterogeneity of the individuals in their adoption of information, there are no studies that take into account the individuals’ uncertainty during their adoption decision-making. This resulted in less than optimal modeling of social contagion dynamics in the existence of phase transition in the final adoption size versus transmission probability. We employed the Inverse Born Problem (IBP) to represent probabilistic entities as complex probability amplitudes in edge-based compartmental theory, and demonstrated that our novel approach performs better in the prediction of social contagion dynamics through extensive simulations on random regular networks.

The growing amplification of social media: measuring temporal and social contagion dynamics for over 150 languages on Twitter for 2009\u20132020

Working from a dataset of 118 billion messages running from the start of 2009 to the end of 2019, we identify and explore the relative daily use of over 150 languages on Twitter. We find that eight languages comprise 80% of all tweets, with English, Japanese, Spanish, Arabic, and Portuguese being the most dominant. To quantify social spreading in each language over time, we compute the ‘contagion ratio’: The balance of retweets to organic messages. We find that for the most common languages on Twitter there is a growing tendency, though not universal, to retweet rather than share new content. By the end of 2019, the contagion ratios for half of the top 30 languages, including English and Spanish, had reached above 1—the naive contagion threshold. In 2019, the top 5 languages with the highest average daily ratios were, in order, Thai (7.3), Hindi, Tamil, Urdu, and Catalan, while the bottom 5 were Russian, Swedish, Esperanto, Cebuano, and Finnish (0.26). Further, we show that over time, the contagion ratios for most common languages are growing more strongly than those of rare languages.

A Multiplex Social Contagion Dynamics Model to Shape and Discriminate D2D Content Dissemination

5G network technology is growing fast, thus the number of devices and the traffic are likely to pose impressive challenges. A new paradigm called “Internet-of-People” (IoP) represents a valid approach to include the social aspect. Following an IoP perspective, we believe that the knowledge of social multiplex interactions and dynamics could drive more sustainable growth. By merging this with the Device-to-Device communication (D2D), we originate a new paradigm presented in this work. We propose a novel bio-inspired approach for quantifying the impact of the social multiplex structure on D2D contents' dissemination. Through rigorous mathematical modelling, we have shaped the D2D data dissemination process as a social contagion dynamics of two co-evolving spreading processes. We weigh the dynamic interactions by including the concepts of homophily and awareness. We have measured the effect of homophily, awareness and network heterogeneity on information diffusion. The bio-inspired mechanism is evaluated through a rigorous mathematical and algorithm analysis, and a meaningful simulation. We show that this mechanism is effective in tuning network awareness and alertness, breaking the “echo chambers” effect. Through our model, we have defined and proposed the guidelines to discriminate the nature of the contents based on contents' dissemination.

Impact of Immunization Strategies on the Dynamics of Social Contagions

Immunization strategies on complex networks are effective methods to control the spreading dynamics on complex networks, which change the topology and connectivity of the underlying network, thereby affecting the dynamics process of propagation. Here, we use a non-Markovian threshold model to study the impact of immunization strategies on social contagions, in which the immune index greater than (or equal to) 0 corresponds to targeted (random) immunization, and when the immune index is less than 0, the probability of an individual being immunized is inversely related to the degree of the individual. A generalized edge-based compartmental theory is developed to analyze the dynamics of social contagions under immunization, and theoretical predictions are very consistent with simulation results. We find that increasing the immune index or increasing the immune ratio will reduce the final adoption size and increase the outbreak threshold, in other words, make the residual network after immunization not conducive to social contagions. Interestingly, enhancing the network heterogeneity is proved to help improve the immune efficiency of targeted immunization. Besides, the dependence of the outbreak threshold on the network heterogeneity is correlated with the immune ratio and immune index.

Social contagion models on hypergraphs

Our understanding of the dynamics of complex networked systems has increased significantly in the last two decades. However, most of our knowledge is built upon assuming pairwise relations among the system's components. This is often an oversimplification, for instance, in social interactions that occur frequently within groups. To overcome this limitation, here we study the dynamics of social contagion on hypergraphs. We develop an analytical framework and provide numerical results for arbitrary hypergraphs, which we also support with Monte Carlo simulations. Our analyses show that the model has a vast parameter space, with first- and second-order transitions, bistability, and hysteresis. Phenomenologically, we also extend the concept of latent heat to social contexts, which might help understanding oscillatory social behaviors. Our work unfolds the research line of higher-order models and the analytical treatment of hypergraphs, posing new questions and paving the way for modeling dynamical processes on higher-order structures.

Benchmarking seeding strategies for spreading processes in social networks: an interplay between influencers, topologies and sizes

The explosion of network science has permitted an understanding of how the structure of social networks affects the dynamics of social contagion. In community-based interventions with spill-over effects, identifying influential spreaders may be harnessed to increase the spreading efficiency of social contagion, in terms of time needed to spread all the largest connected component of the network. Several strategies have been proved to be efficient using only data and simulation-based models in specific network topologies without a consensus of an overall result. Hence, the purpose of this paper is to benchmark the spreading efficiency of seeding strategies related to network structural properties and sizes. We simulate spreading processes on empirical and simulated social networks within a wide range of densities, clustering coefficients, and sizes. We also propose three new decentralized seeding strategies that are structurally different from well-known strategies: community hubs, ambassadors, and random hubs. We observe that the efficiency ranking of strategies varies with the network structure. In general, for sparse networks with community structure, decentralized influencers are suitable for increasing the spreading efficiency. By contrast, when the networks are denser, centralized influencers outperform. These results provide a framework for selecting efficient strategies according to different contexts in which social networks emerge.

Social Networks and Health: New Developments in Diffusion, Online and Offline

The relationship between social networks and health encompasses everything from the flow of pathogens and information to the diffusion of beliefs and behaviors. This review addresses the vast and multidisciplinary literature that studies social networks as a structural determinant of health. In particular, we report on the current state of knowledge on how social contagion dynamics influence individual and collective health outcomes. We pay specific attention to research that leverages large-scale online data and social network experiments to empirically identify three broad classes of contagion processes: pathogenic diffusion, informational and belief diffusion, and behavioral diffusion. We conclude by identifying the need for more research on ( a) how multiple contagions interact within the same social network, ( b) how online social networks impact offline health, and ( c) the effectiveness of social network interventions for improving population health.

Collective action and social contagion: Community gardens as a case study

AbstractInstitutions for collective action (ICAs), comprising individuals that informally organize to manage collective resources, have gained recognition as a significant means of informal governance of common resources alongside the more formal schemes of privatization and top‐down regulation. Using the case study of community gardens, this article locates ICAs within the broader phenomenon of self‐organization in complex systems, and inquires whether ICAs exhibit dynamics of social contagion and diffuse in accordance with patterns that prevail in self‐organized complex systems. Applying quantitative methods derived from the field of complexity, we measure the temporal, spatial, and spatiotemporal diffusion of community gardens in the city of Jerusalem. The results suggest that the spread of community gardens in the urban space displays patterns of self‐organization and social contagion. More generally, these findings suggest that ICAs may scale from the micro to the macro level in a bottom‐up, self‐expanding manner, while maintaining the advantages of local, commons‐based arrangements. This perspective carries significant policy implications, and highlights the potential use of ICAs as a means for the governance of public resources, not only on a local, micro scale but also on a more global scale.

Social contagions with communication channel alternation on multiplex networks

Internet communication channels, e.g., Facebook, Twitter, and email, are multiplex networks that facilitate interaction and information-sharing among individuals. During brief time periods users often use a single communication channel, but then communication channel alteration (CCA) occurs. This means that we must refine our understanding of the dynamics of social contagions. We propose a non-Markovian behavior spreading model in multiplex networks that takes into account the CCA mechanism, and we develop a generalized edge-based compartmental method to describe the spreading dynamics. Through extensive numerical simulations and theoretical analyses we find that the time delays induced by CCA slow the behavior spreading but do not affect the final adoption size. We also find that the CCA suppresses behavior spreading. On two coupled random regular networks, the adoption size exhibits hybrid growth, i.e., it grows first continuously and then discontinuously with the information transmission probability. CCA in ER-SF multiplex networks in which two subnetworks are Erd\H{o}s-R\'{e}nyi (ER) and scale-free (SF) introduces a crossover from continuous to hybrid growth in adoption size versus information transmission probability. Our results extend our understanding of the role of CCA in spreading dynamics, and may elicit further research.

Heterogeneous behavioral adoption in multiplex networks

Heterogeneity is found widely in populations, e.g. different individuals have diverse personalities and a different willingness to accept novel ideas or behaviors. Whereas population heterogeneity is rarely considered in studying the social contagions on complex networks, especially on multiplex networks. To explore the effect of population heterogeneity on the dynamics of social contagions, a novel model based on double-layer multiplex networks is proposed, in which information diffuses synchronously on the two layers, and each layer is assigned with different adoption thresholds. Meanwhile, populations are classified into the activists and conservatives according to their willingness to adopt new behaviors. To qualitatively understand the effect of population heterogeneity on social contagions, a generalized edge-based compartmental theory is proposed. Through rigorous theoretical analysis and extensive simulations, we find the activists in the two layers promote the adoption of behavior. More interestingly, the crossover phenomena in phase transition are found in the growth of the final adopted size when increasing the information transmission rate. When the proportion of activists is relatively large, the phase transition exhibits continuous pattern. Reducing the proportion of activists induces a hybrid transition, in which the final adoption size first grows continuously at the first threshold, and then increases discontinuously at the second threshold. In addition, we find that the network heterogeneity will change the crossover phenomena in phase transition.

Social contagions on interconnected networks of heterogeneous populations.

Recently, the dynamics of social contagions ranging from the adoption of a new product to the diffusion of a rumor have attracted more and more attention from researchers. However, the combined effects of individual's heterogenous adoption behavior and the interconnected structure on the social contagions processes have yet to be understood deeply. In this paper, we study theoretically and numerically the social contagions with heterogeneous adoption threshold in interconnected networks. We first develop a generalized edge-based compartmental approach to predict the evolution of social contagion dynamics on interconnected networks. Both the theoretical predictions and numerical results show that the growth of the final recovered fraction with the intralayer propagation rate displays double transitions. When increasing the initial adopted proportion or the adopted threshold, the first transition remains continuous within different dynamic parameters, but the second transition gradually vanishes. When decreasing the interlayer propagation rate, the change in the double transitions mentioned above is also observed. The heterogeneity of degree distribution does not affect the type of first transition, but increasing the heterogeneity of degree distribution results in the type change of the second transition from discontinuous to continuous. The consistency between the theoretical predictions and numerical results confirms the validity of our proposed analytical approach.

Social contagions on multiplex networks with heterogeneous population

In this paper, we study the effects of heterogeneous population on the dynamics of social contagions on multiplex networks. We assume a fraction of f nodes with a higher adoption threshold T>1, and the remaining fraction of 1−f nodes with adoption threshold 1. A social contagion model is proposed to describe the social contagions, in which a susceptible node adopting the contagion only when its received accumulated information is larger than the adoption threshold in either subnetwork. With an edge-based compartmental approach and extensive numerical simulations, we find that the system exhibits a continuous phase transition for small values of f, while shows a hybrid phase transition for relatively large values of f and T. For homogeneous multiplex networks the hybrid phase transition occurs, while there is only a continuous phase transition for heterogeneous multiplex networks. Our theoretical predictions agree well with numerical simulations.

A general social contagion dynamic in interconnected lattices

Research on dynamical processes in interconnected spatial networks has expanded in recent years, but there has been little focus on social contagions. Using a general social contagion model, we numerically study how an interconnected spatial system composed of two interconnected planar lattices influences social contagion dynamics. When information is transmitted and allows for a probability of behavior adoption, strongly interconnected lattices stimulate the contagion process and significantly increase the final density of adopted individuals. We perform a finite-size analysis and confirm that the dependency of prevalence on the transmission rate is continuous regardless of the adoption probability. The prevalence grows discontinuously with the adoption probability even when the transmission rate is low. Although a high transmission rate or a high adoption probability increases the final adopted density in weak interconnected lattices, the prevalence always grows continuously in these networks. These findings help us understand social contagion dynamics in interconnected lattices.

Optimal imitation capacity and crossover phenomenon in the dynamics of social contagions

In the threshold model of social contagions with non-redundant memory, researchers have overlooked the investigation on the limited imitation (LI) effect, which shows individual imitates the behavior adoption only for a certain range of ratio of his adopted informants. To understand such LI effect, we propose a social contagion model with a gate-like adoption probability consisting of the ‘on’ and ‘off’ thresholds. With extensive numerical simulations, we find that, given information transmission probability and a gate width (the ‘off’ threshold minus the ‘on’ threshold), there exists an optimal imitation capacity with the optimal ‘on’ threshold maximizing the final adoption size. And the large ‘off’ threshold serves for further enlarging the final adoption size at a given ‘on’ threshold. Besides, a cross phenomenon in phase transition is also uncovered: the increase of ‘on’ threshold causes the growth pattern of final behavior adoption size versus the information transmission probability change from the second-order to the first-order phase transition. We finally find that the above phenomena are qualitatively unaffected by the heterogeneity level of degree distribution. At last, an edge-based compartmental theory is conceived for theoretical analysis. And our suggested theory agrees well with the simulation results.

Dynamics of social contagions with local trend imitation

Research on social contagion dynamics has not yet included a theoretical analysis of the ubiquitous local trend imitation (LTI) characteristic. We propose a social contagion model with a tent-like adoption probability to investigate the effect of this LTI characteristic on behavior spreading. We also propose a generalized edge-based compartmental theory to describe the proposed model. Through extensive numerical simulations and theoretical analyses, we find a crossover in the phase transition: when the LTI capacity is strong, the growth of the final adoption size exhibits a second-order phase transition. When the LTI capacity is weak, we see a first-order phase transition. For a given behavioral information transmission probability, there is an optimal LTI capacity that maximizes the final adoption size. Finally we find that the above phenomena are not qualitatively affected by the heterogeneous degree distribution. Our suggested theoretical predictions agree with the simulation results.

COSINE: Community-Preserving Social Network Embedding From Information Diffusion Cascades

This paper studies the problem of social network embedding without relying on network structures that are usually not observed in many cases. We address that the information diffusion process across networks naturally reflects rich proximity relationships between users. Meanwhile, social networks contain multiple communities regularizing communication pathways for information propagation. Based on the above observations, we propose a probabilistic generative model, called COSINE, to learn community-preserving social network embeddings from the recurrent and time-stamped social contagion logs, namely information diffusion cascades. The learned embeddings therefore capture the high-order user proximities in social networks. Leveraging COSINE, we are able to discover underlying social communities and predict temporal dynamics of social contagion. Experimental results on both synthetic and real-world datasets show that our proposed model significantly outperforms the existing approaches.

Effects of time-delays in the dynamics of social contagions

Time-delays are pervasive in such real-world complex networks as social contagions and biological systems, and they radically alter the evolution of the dynamic processes in networks. We use a non-Markovian spreading threshold model to study the effects of time-delays on social contagions. Using extensive numerical simulations and theoretical analyses we find that relatively long time-delays induce a microtransition in the evolution of a fraction of recovered individuals, i.e., the fraction of recovered individuals versus time exhibits multiple phase transitions. The microtransition is sharper and more obvious when high-degree individuals have a higher probability of experiencing time-delays, and the microtransition is obscure when the time-delay distribution reaches heterogeneity. We use an edge-based compartmental theory to analyze our research and find that the theoretical results agree well with our numerical simulation results.