Cognitive Load Balancing Approach for 6G MEC Serving IoT Mashups

The sixth generation (6G) of communication networks represents more of a revolution than an evolution of the previous generations, providing new directions and innovative approaches to face the network challenges of the future. A crucial aspect is to make the best use of available resources for the support of an entirely new generation of services. From this viewpoint, the Web of Things (WoT), which enables Things to become Web Things to chain, use and re-use in IoT mashups, allows interoperability among IoT platforms. At the same time, Multi-access Edge Computing (MEC) brings computing and data storage to the edge of the network, which creates the so-called distributed and collective edge intelligence. Such intelligence is created in order to deal with the huge amount of data to be collected, analyzed and processed, from real word contexts, such as smart cities, which are evolving into dynamic and networked systems of people and things. To better exploit this architecture, it is crucial to break monolithic applications into modular microservices, which can be executed independently. Here, we propose an approach based on complex network theory and two weighted and interdependent multiplex networks to address the Microservices-compliant Load Balancing (McLB) problem in MEC infrastructure. Our findings show that the multiplex network representation represents an extra dimension of analysis, allowing to capture the complexity in WoT mashup organization and its impact on the organizational aspect of MEC servers. The impact of this extracted knowledge on the cognitive organization of MEC is quantified, through the use of heuristics that are engineered to guarantee load balancing and, consequently, QoS.

Relay interlayer synchronisation: invariance and stability conditions

In this paper, the existence (invariance) and stability (locally and globally) of relay interlayer synchronisation (RIS) are investigated in a chain of multiplex networks. The local dynamics of the nodes in the symmetric positions layers on both sides of the non-identical middlemost layer(s) are identical. The local and global stability conditions for this synchronisation state are analytically derived based on the master stability function approach and by constructing a suitable Lyapunov function, respectively. We propose an appropriate demultiplexing process for the existence of the RIS state. Then the variational equation transverse to the RIS manifold for demultiplexed networks is derived. In numerical simulations, the impact of interlayer and intralayer coupling strengths, variations of the system parameter in the relay layers and demultiplexing on the emergence of RIS in triplex and pentaplex networks are explored. Interestingly, in this multiplex network, enhancement of RIS is observed when a type of impurity via parameter mismatch in the local dynamics of the nodes is introduced in the middlemost layer. A common time-lag with small amplitude shift between the symmetric positions and central layers plays an important role for the enhancing of relay interlayer synchrony. This analysis improves our understanding of synchronisation states in multiplex networks with nonidentical layers.

A multilayer network model of the coevolution of the spread of a disease and competing opinions

During the COVID-19 pandemic, conflicting opinions on physical distancing swept across social media, affecting both human behavior and the spread of COVID-19. Inspired by such phenomena, we construct a two-layer multiplex network for the coupled spread of a disease and conflicting opinions. We model each process as a contagion. On one layer, we consider the concurrent evolution of two opinions — pro-physical-distancing and anti-physical-distancing — that compete with each other and have mutual immunity to each other. The disease evolves on the other layer, and individuals are less likely (respectively, more likely) to become infected when they adopt the pro-physical-distancing (respectively, anti-physical-distancing) opinion. We develop approximations of mean-field type by generalizing monolayer pair approximations to multilayer networks; these approximations agree well with Monte Carlo simulations for a broad range of parameters and several network structures. Through numerical simulations, we illustrate the influence of opinion dynamics on the spread of the disease from complex interactions both between the two conflicting opinions and between the opinions and the disease. We find that lengthening the duration that individuals hold an opinion may help suppress disease transmission, and we demonstrate that increasing the cross-layer correlations or intra-layer correlations of node degrees may lead to fewer individuals becoming infected with the disease.

Multiplex Embedding of Biological Networks Using Topological Similarity of Different Layers

Network embedding techniques, which provide low dimensional representations of the nodes in a network, have been commonly applied to many machine learning problems in computational biology. In most of these applications, multiple networks (e.g., different types of interactions/associations or semantically identical networks that come from different sources) are available. Multiplex network embedding aims to derive strength from these data sources by integrating multiple networks with a common set of nodes. Existing approaches to this problem treat all layers of the multiplex network equally while performing integration, ignoring the differences in the topology and sparsity patterns of different networks. Here, we formulate an optimization problem that accounts for inner-network smoothness, intra-network smoothness, and topological similarity of networks to compute diffusion states for each network. To quantify the topological similarity of pairs of networks, we use Gromov-Wasserteins discrepancy. Finally, we integrate the resulting diffusion states and apply dimensionality reduction (singular value decomposition after log-transformation) to compute node embeddings. Our experimental results in the context of drug repositioning and drug-target prediction show that the embeddings computed by the resulting algorithm, Hattusha, consistently improve predictive accuracy over algorithms that do not take into account the topological similarity of different networks.

ImmunAL: a frame to identify the immunological markers for Mild Moderated Alzheimer's Disease applying Multiplex Network Model

Identification of immunological markers for neurodegenerative diseases resolve issues related to diagnostic and therapeutic. Neuro-specific cells experience disruptive mechanisms in the early stages of disease progression. The autophagy mechanism, guided by the autoantibodies, is one of the prime indicators of neurodegenerative diseases. Identifying autoantibodies can show a new direction. Detecting influential autoantibodies from relational networks viz., co-expression, co-methylation, etc. is a well-studied area. However, none of the studies have considered the functional affinity among the autoantibodies while selecting them from a relational network. In this regard, a two-layered multiplex network based framework has been proposed,whereby the layers consist co-expression and co-semantic scores. The networks have been formed using three distinct cases viz., diseased, controlled, and a combination of both. Subsequently, a random walk with restart mechanism has been applied to identify the influential autoantibodies, where layer switching probability and restart probability are 0.5 and 0.4 respectively. Next, pathway semantic network has been formed considering the autoantibody associated pathways. EPO and IL1RN, associated with a maximum number of pathways, are identified as the two most influential autoantibodies. The network also provides insights into possible molecular mechanisms during the pathogenic progression. Finally, MDPI and CNN3 are also identified as important biomarkers.