Social–ecological networks (SENs) represent the complex relationships between ecological and social systems and are a useful tool for analyzing and managing ecosystem services. However, mainstreaming ...the application of SENs in ecosystem service research has been hindered by a lack of clarity about how to match research questions to ecosystem service conceptualizations in SEN (i.e., as nodes, links, attributes, or emergent properties). Building from different disciplines, we propose a typology to represent ecosystem service in SENs and identify opportunities and challenges of using SENs in ecosystem service research. Our typology provides guidance for this growing field to improve research design and increase the breadth of questions that can be addressed with SEN to understand human–nature interdependencies in a changing world.
Social–ecological networks (SENs) provide a promising approach to represent the complex ecological, social, and social–ecological relationships that influence ecosystems service supply.Ecosystem services can be represented in SENs as nodes, links, attributes, or as emergent properties of the network, each bringing distinct aspects of ecosystem services into focus to address different questions.Applications of SENs in ecosystem service research can foster: (i) understanding of the social and ecological drivers of ecosystem services; (ii) forecasting of the impacts of stressors; (iii) investigation of trade-offs between ecosystem services; and (iv) assessment of the effects of alternative management options.Ecosystem service research would benefit from a typology to conceptualize particular ecosystem services in SEN analyses and from greater clarity of when ecosystem service research can benefit from a SEN approach.
Selecting influential users in a network is essential to spread information quickly. Identifying influential users is very useful for viral marketing and brand communication. Influence maximization ...(IM) is selecting a few influential users in the network who can maximize the influence spread. Many existing algorithms address IM in single-layer networks. However, the study of IM in multi-layer networks is gaining importance after the advancement and rapid growth in the usage of online social networks. Studying IM in multi-layer networks in the context of viral marketing will be interesting. Motivated by this, this paper investigates the K＋＋ Shell decomposition algorithm to find the k set of influential nodes (seed nodes) in a multi-layer network. The proposed model prunes the nodes based on degree and assign reward points to their neighbors. We conducted a comparative study of various IM algorithms and reported the results. We observed that the K＋＋ Shell decomposition algorithm outperforms other algorithms on various real-time datasets under various settings and environments.
Network science is today established as a backbone for description of structure and function of various physical, chemical, biological, technological, and social systems. Here we review recent ...advances in the study of complex biological systems that were inspired and enabled by methods of network science. First, we present research highlights ranging from determination of the molecular interaction network within a cell to studies of architectural and functional properties of brain networks and biological transportation networks. Second, we focus on synergies between network science and data analysis, which enable us to determine functional connectivity patterns in multicellular systems. Until now, this intermediate scale of biological organization received the least attention from the network perspective. As an example, we review the methodology for the extraction of functional beta cell networks in pancreatic islets of Langerhans by means of advanced imaging techniques. Third, we concentrate on the emerging field of multilayer networks and review the first endeavors and novel perspectives offered by this framework in exploring biological complexity. We conclude by outlining challenges and directions for future research that encompass utilization of the multilayer network formalism in exploring intercellular communication patterns in tissues, and we advocate for network science being one of the key pillars for assessing physiological function of complex biological systems—from organelles to organs—in health and disease.
•Network science offers a comprehensive toolset for exploring biological systems.•Intercellular interaction patterns can be successfully studied by network approaches.•The emergent field of multilayer networks is promising for biological data analysis.
Chimera states in neuronal networks: A review Majhi, Soumen; Bera, Bidesh K.; Ghosh, Dibakar ...
Physics of life reviews,
March 2019, 2019-03-00, 20190301, Volume:
28
Journal Article
Peer reviewed
Neuronal networks, similar to many other complex systems, self-organize into fascinating emergent states that are not only visually compelling, but also vital for the proper functioning of the brain. ...Synchronous spatiotemporal patterns, for example, play an important role in neuronal communication and plasticity, and in various cognitive processes. Recent research has shown that the coexistence of coherent and incoherent states, known as chimera states or simply chimeras, is particularly important and characteristic for neuronal systems. Chimeras have also been linked to the Parkinson's disease, epileptic seizures, and even to schizophrenia. The emergence of this unique collective behavior is due to diverse factors that characterize neuronal dynamics and the functioning of the brain in general, including neural bumps and unihemispheric slow-wave sleep in some aquatic mammals. Since their discovery, chimera states have attracted ample attention of researchers that work at the interface of physics and life sciences. We here review contemporary research dedicated to chimeras in neuronal networks, focusing on the relevance of different synaptic connections, and on the effects of different network structures and coupling setups. We also cover the emergence of different types of chimera states, we highlight their relevance in other related physical and biological systems, and we outline promising research directions for the future, including possibilities for experimental verification.
•Chimera states in neuronal networks are comprehensively reviewed.•Different types of synaptic connections, networks, and coupling setups are considered.•Future research directions in related physical and biological systems are outlined.
The introduction of new services requiring large and dynamic bitrate connectivity can cause changes in the direction of the traffic in metro and even core network segments throughout the day. This ...leads to large overprovisioning in statically managed virtual network topologies (VNTs), which are designed to cope with the traffic forecast. To reduce expenses while ensuring the required grade of service, in this paper we propose a VNT reconfiguration approach based on data analytics for traffic prediction (VENTURE). It regularly reconfigures the VNT based on the predicted traffic, thus adapting the topology to both the current and the predicted traffic volume and direction. A machine learning algorithm based on an artificial neural network is used to provide robust and adaptive traffic models. The reconfiguration problem that takes as its input the traffic prediction is modeled mathematically, and a heuristic is proposed to solve it in practical times. To support VENTURE, we propose an architecture that allows collecting and storing data from monitoring at the routers and that is used to train predictive models for every origin-destination pair. Exhaustive simulation results of the algorithm, together with the experimental assessment of the proposed architecture, are finally presented.
Traditionally, the evolution of cooperation on structured population assumed the uniform interaction partner between gaming and learning. Yet in real-world society, individuals often act different ...roles in which environments gaming partners differ from learning partners. This investigation studies the evolution of cooperation under the effects of the diverse selection intensity induced by network asymmetry on two-layer networks, where the gaming and learning environments are modeled by different layers, respectively. It is found that heterogeneous selection intensity can alleviate the cooperation dilemma induced by asymmetry between gaming and learning environments. When selection intensity has a correlation with the edge overlap level of two layers, it is found that both positive correlation and negative correlation can optimize the evolution of cooperation for a moderate overlap level. However, positive correlation performs better than negative correlation in promoting the evolution of cooperation. Moreover, the increasing heterogeneity of selection enhances the evolution of cooperation under positive correlation, yet has different effects on cooperation under negative correlation for different temptations. Furthermore, we prove that the results are robust to the deterministic learning process as well as a higher noise.
•Diverse selection intensity solves the cooperation dilemma induced by network asymmetry.•Both positive and negative correlation enhance the evolution of cooperation.•A moderate overlap level optimizes the evolution of cooperation.•Positive correlation performs better in promoting the evolution of cooperation.
•The model of FDMSNs is established, where fractional dynamics of networks, multiple time delays, and multiple connections between nodes are considered at the same time. There is no restriction ...placed on the topological structures of subnetworks in FDMSNs.•Adaptive control and impulsive control are respectively designed to investigate BLS of FDMSNs and fractional-order coupled Chua’s circuits based on delayed multilayer signed networks. The obtained BLS criteria are related to the coupling strength, control gain, and the order of fractional derivative.•A Halanay inequality of fractional version is established, which provides an effective tool to deal with the stability and synchronization of fractional-order delayed complex networks.
This article addresses the problem of bipartite leader-following synchronization of fractional-order delayed multilayer signed networks. It is worth emphasizing that fractional dynamics and multiple connections between nodes with multiple time delays are considered into signed networks. In our main results, adaptive control (continuous control) and impulsive control (discontinuous control) are both designed to reach the bipartite leader-following synchronization of the underlying networks. In addition, a Halanay inequality of fractional version is established. With the help of it, by using signed graph theory and Lyapunov method, some bipartite synchronization criteria are derived under the Lyapunov-type theorems and coefficients-type theorems. Then, as an application of theoretical results, the bipartite leader-following synchronization of fractional-order coupled Chua’s circuits modeled on delayed multilayer signed networks is considered. Finally, two numerical examples are exhibited to exemplify the usefulness and feasibility of the theoretical results.
Epidemic outbreaks are often accompanied by the unconfirmed information propagation Especially in the early stages of an epidemic outbreak, because of the lack of adequate verification, some ...unconfirmed information appears, which has a significant impact on the epidemic. Meanwhile, individuals under different emotions can also have different response to epidemics. In the paper, an interplay between epidemic spreading and unconfirmed information propagation model is established considering individuals’ emotional factors in multilayer networks. The mean-field method is used to analyze the interaction dynamic propagation process and the threshold of epidemic is obtained. Finally through the theoretical analysis and numerical simulations of scale-free network, the validity of the results is verified. The results show that the information, although unconfirmed, is still conducive to curb the spread of the epidemic. In addition, individuals with different emotions will adopt different self-protective behaviors, so as to further affect the spread of the epidemic.
•An interplay between unconfirmed information propagation and epidemic spreading model is proposed.•The individuals’ emotional factors are considered in multilayer network.•The mean-field method is used to analyze the interaction dynamic propagation process.•The unconfirmed information propagation have an effect on the spread of the epidemic.•The individuals with anxious make less favorable for the epidemic control.
Studying parasitoids can provide insights into global diversity estimates, climate change impacts, and agroecosystem service provision. However, this potential remains largely untapped due to a lack ...of data on how parasitoids interact with other organisms. Ecological networks are a useful tool for studying and exploiting the impacts of parasitoids, but their construction is hindered by the magnitude of undescribed parasitoid species, a sparse knowledge of host ranges, and an under-representation of parasitoids within DNA-barcode databases (we estimate <5% have a barcode). Here, we advocate the use of DNA metabarcoding to construct the host–parasitoid component of multilayer networks. While the incorporation of parasitoids into network-based analyses has far ranging applications, we focus on its potential for assessing ecosystem service provision within agroecosystems.
Parasitoids are key ecosystem service providers within sustainable agriculture and integrated pest-management strategies due to their function as biocontrol agents.There is a dearth of data regarding how parasitoids fit within wider communities of interacting species, but such information is essential for the successful implementation of conservation biological control in open-field agroecosystems.DNA barcoding is a useful tool for the establishment of host–parasitoid interactions (and many other associations) and can enable the rapid and relatively cost-effective construction of ecological networks.Ecological networks, specifically multilayer ecological networks constructed using DNA-based methods, can significantly aid our understanding of how land management can influence multiple ecosystem services and lead to enhanced agricultural sustainability.
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