Coevolution spreading in complex networks (1901.02125v3)

Abstract: The propagations of diseases, behaviors and information in real systems are rarely independent of each other, but they are coevolving with strong interactions. To uncover the dynamical mechanisms, the evolving spatiotemporal patterns and critical phenomena of networked coevolution spreading are extremely important, which provide theoretical foundations for us to control epidemic spreading, predict collective behaviors in social systems, and so on. The coevolution spreading dynamics in complex networks has thus attracted much attention in many disciplines. In this review, we introduce recent progress in the study of coevolution spreading dynamics, emphasizing the contributions from the perspectives of statistical mechanics and network science. The theoretical methods, critical phenomena, phase transitions, interacting mechanisms, and effects of network topology for four representative types of coevolution spreading mechanisms, including the coevolution of biological contagions, social contagions, epidemic-awareness, and epidemic-resources, are presented in detail, and the challenges in this field as well as open issues for future studies are also discussed.

Overview of Coevolution Spreading in Complex Networks

The paper "Coevolution Spreading in Complex Networks" provides a comprehensive review of coevolution spreading dynamics, with a particular emphasis on the interactions between different types of spreading processes observed in complex networks. The paper is crucial as it lays down the theoretical framework necessary to comprehend and predict the behaviors of diseases, behaviors, and information as they coevolve within interconnected systems. By incorporating both statistical mechanics and network science perspectives, the authors aim to unravel the intricacies of coevolution dynamics through four primary coevolution types: biological contagions, social contagions, epidemic-awareness dynamics, and epidemic-resource interactions.

Key Scientific Contributions

1. Biological and Social Coevolution: The paper explores the coevolution of both biological contagions (e.g., simultaneous spread of multiple diseases) and social contagions (e.g., spread of behaviors or innovations). The authors emphasize the necessity for multiple interactions in social contagions, distinguishing them from biological spreads which often require just a single contact.
2. Epidemic-Awareness Coevolution: An interesting aspect of the paper is the role of awareness diffusion in controlling disease outbreaks. By integrating the diffusion of awareness (through social or mass media) alongside epidemic spreading, the authors inspect how informed individuals adopt protective measures, thus impacting the epidemic's trajectory.
3. Epidemic-Resource Coevolution: The resource allocation, particularly in the context of medical supplies or vaccines during epidemics, is analyzed to highlight how limited resources affect epidemic dynamics. The paper addresses the optimization of resource distribution to minimize epidemic impact and the critical phenomena emerging from such interactions.
4. Network Topology Effects: The effects of network topology on coevolution spreading dynamics are explored in depth. The authors underline how attributes such as degree distributions, clustering, temporal dynamics, and multilayer structures can either facilitate or impede spreading processes.
5. Interacting Mechanisms and Phase Transitions: By examining diverse interacting mechanisms, the paper uncovers the rich phase transition phenomena associated with coevolution spreading. These include coexistence thresholds, discontinuous phase transitions due to synergy, and competitive interactions leading to unique epidemic thresholds.

Implications and Future Directions

This review not only elucidates the complexity of analyzing dynamic processes but also imparts vital implications for future research and practical applications:

• Epidemiological Modelling: Understanding coevolution dynamics aids in refining epidemiological models that consider multiple interacting diseases simultaneously.
• Social and Strategic Interventions: By deciphering the coevolution dynamics between awareness campaigns and infection spread, policymakers can design more effective public health strategies and interventions.
• Optimization in Networked Systems: The paper provides insights into optimizing network structures and resource allocation mechanisms to mitigate epidemic risks promptly.

Future developments could focus on large-scale empirical validations of theoretical models, exploring additional coevolution types such as economic or technological networks, and refining theoretical approaches to better comprehend the nuanced correlations present in real data.

By integrating interdisciplinary research perspectives, the paper "Coevolution Spreading in Complex Networks" outlines a systematic framework to advance our understanding in predicting and controlling complex coevolutionary processes across diverse domains.