Multiplexing Networks Research Articles

Drug Repositioning Based on a Multiplex Network by Integrating Disease, Gene, and Drug Information

Background: The research of new drugs is very expensive and the cycle is relatively long, so it has broad development prospects and good economic benefits to use validated drugs in the treatment of other diseases. Objective: The purpose of drug repositioning is to identify other indications for existing drugs. In addition to using disease and drug information for drug repositioning, other biomolecular information can also be integrated for drug repositioning. Integrating multiple biomolecular data of different types can improve the predictive performance of drug repositioning models. Methods: This paper proposes a drug repositioning algorithm based on a multiplex network (DRMN algorithm) by integrating disease, gene, and drug information. DRMN algorithm utilizes known disease-gene and gene-drug associations to connect disease phenotype similarity network, gene expression similarity network, and drug response similarity network. Then they are constructed into a multiplex network, and the importance score of each node is calculated by PageRank (PR) algorithm. Finally, disease-drug association scores are sorted to achieve drug repositioning. Results: DRMN algorithm is applied to two sets of sample data. Disease-drug association scores are calculated separately from disease PR values and drug PR values in both datasets. In top 50% of association scores, lots of disease-drug association prediction results have been verified by existing results. Compared with other algorithms, DRMN algorithm also shows better performance. Conclusion: DRMN algorithm can effectively integrate multi-omics data for drug repositioning and obtain better prediction results.

CRIME HOTSPOT EMERGENCE IN MEXICO CITY: A COMPLEXITY SCIENCE PERSPECTIVE

We present a dynamic model based on the theories proposed by environmental criminologists to explain the emergence of crime hotspots within cities; a pervasive phenomenon that is largely independent of cities size and cultural differences. The model is defined on a multiplex network that represents a city spatial tiling with its corresponding urban transport infrastructure, allowing to explore the relation between crime hotspot locations and the network topological features. It also allows to explore the effects that cities time evolution and police checkpoints might have on the emergence of crime hotspots. For Mexico City, the model shows that heterogeneous distributions of criminal activity arise from a diffusion-driven instability, as a self-organizing process. The results obtained for this city are in line with several insights from environmental criminology, such as the relationship between urban layout and crime hotspots locations, or the conceptual label assigned to specific locations as crime generators. They also uncover new relationships between cities design and crime hotspot locations, and suggest that routine activity theory alone cannot explain the emergence of heterogeneous crime distributions.

Q-Learning-Based Energy-Efficient Network Planning in IP-Over-EON

During network planning phase, optimal network planning implemented through efficient resource allocation and static traffic demand provisioning in IP-over-elastic optical network (IP-over-EON) is significantly challenging compared with the fixed-grid wavelength division multiplexing (WDM) network due to increased flexibility in IP-over-EON. Mathematical programming based optimization models used for this purpose may not provide solution for large networks due to large computational complexity. In this regard, a greedy heuristic may be used that intuitively selects traffic elements in sequence from static traffic demand matrix and provisions the traffic elements after necessary resource allocation. However, in general, such greedy heuristics offer suboptimal solutions, since appropriate traffic sequence offering the optimal performance is rarely selected. In this regard, we propose a reinforcement learning technique (in particular a Q-learning method), combined with an auxiliary graph (AG)-based energy efficient greedy method to be used for large network planning. The Q-learning method is used to decide the suitable sequence of traffic allocation such that the overall power consumption in the network reduces. In the proposed heuristic, each traffic from the given static traffic demand matrix is successively selected using the Q-learning method and provisioned using the AG-based greedy method.

Dynamic and Efficient Spectrum Utilization for 6G With THz, mmWave, and RF Band

The number of wireless communication devices has grown exponentially with the evolution of wireless systems. Moreover, the development of technologies such as the Internet of things (IoT) and massive machine-type communications (mMTC) are also fueling the rapid increase in the number of communication devices. It is anticipated that through 6G, the wireless communication networks will move in the direction of ultra-dense distribution of enormous devices with assorted service and rate requirements. This requires a huge network energy, capacity, and efficient deployment of the spectrum. This paper employs the massive multi-input multi-output (mMIMO) and non-orthogonal multiple access (NOMA) techniques that increase the multiplexing gain and network capacity. This paper utilizes the benefits of millimeter Wave (mmWave) and Terahertz (THz) channels that poseess considerably higher communication capacity. However, these schemes are highly complex. To mitigate this problem, we use BS caching as diverse applications in 6G will have varied rate requirements. In addition, we propose an application-specific channel selection (ASCS) scheme with a cache-enabled two-tier 6G Heterogeneous Network (HetNet). In the proposed scheme, bestowing to the application requirement of the small cell users, the small cell base station (SBS) switches downlink channels dynamically. The simulation results illustrate that the proposed ASCS scheme can achieve higher spectral efficiency, energy efficiency, and throughput for the cache-enabled two-tier 6G HetNet.

Dual adaptive learning multi-task multi-view for graph network representation learning

Graph network analysis, which achieves widely application, is to explore and mine the graph structure data. However, existing graph network analysis methods with graph representation learning technique ignore the correlation between multiple graph network analysis tasks, and they need massive repeated calculation to obtain each graph network analysis results. Or they cannot adaptively balance the relative importance of multiple graph network analysis tasks, that lead to weak model fitting. Besides, most of existing methods ignore multiplex views semantic information and global graph information, which fail to learn robust node embeddings resulting in unsatisfied graph analysis results. To solve these issues, we propose a multi-task multi-view adaptive graph network representation learning model, called M2agl. The highlights of M2agl are as follows: (1) Graph convolutional network with the linear combination of the adjacency matrix and PPMI (positive point-wise mutual information) matrix is utilized as encoder to extract the local and global intra-view graph feature information of the multiplex graph network. Each intra-view graph information of the multiplex graph network can adaptively learn the parameters of graph encoder. (2) We use regularization to capture the interaction information among different graph views, and the importance of different graph views are learned by view attention mechanism for further inter-view graph network fusion. (3) The model is trained oriented by multiple graph network analysis tasks. The relative importance of multiple graph network analysis tasks are adjusted adaptively with the homoscedastic uncertainty. The regularization can be considered as an auxiliary task to further boost the performance. Experiments on real-worlds attributed multiplex graph networks demonstrate the effectiveness of M2agl in comparison with other competing approaches.

A comprehensive framework for link prediction in multiplex networks

A comprehensive framework for link prediction in multiplex networks

The impact of the self-recognition ability and physical quality on coupled negative information-behavior-epidemic dynamics in multiplex networks

In recent years, as the COVID-19 global pandemic evolves, many unprecedented new patterns of epidemic transmission continue to emerge. Reducing the impact of negative information diffusion, calling for individuals to adopt immunization behaviors, and decreasing the infection risk are of great importance to maintain public health and safety. In this paper, we construct a coupled negative information-behavior-epidemic dynamics model by considering the influence of the individual's self-recognition ability and physical quality in multiplex networks. We introduce the Heaviside step function to explore the effect of decision-adoption process on the transmission for each layer, and assume the heterogeneity of the self-recognition ability and physical quality obey the Gaussian distribution. Then, we use the microscopic Markov chain approach (MMCA) to describe the dynamic process and derive the epidemic threshold. Our findings suggest that increasing the clarification strength of mass media and enhancing individuals' self-recognition ability can facilitate the control of the epidemic. And, increasing physical quality can delay the epidemic outbreak and leads to suppress the scale of epidemic transmission. Moreover, the heterogeneity of the individuals in the information diffusion layer leads to a two-stage phase transition, while it leads to a continuous phase transition in the epidemic layer. Our results can provide favorable references for managers in controlling negative information, urging immunization behaviors and suppressing epidemics.

Discovering Community Structure in Multiplex Networks via a Co-Regularized Robust Tensor-Based Spectral Approach

Complex networks arise in various fields, such as biology, sociology and communication, to model interactions among entities. Entities in many real-world systems exhibit different types of interactions, which requires modeling these type of systems properly. Multiplex networks are used to model these systems, as they can reflect the nodes’ pair-wise interactions as multiple distinct types of links across layers. Community detection is a widely studied application in network analysis as it provides insights into the structure and organization of the network. Even though multiple algorithms have been developed in the community detection field, many of them have a limited performance in the presence of noise. In this article, we develop a novel algorithm that combines tensor low-rank representation, spectral clustering and distance regularization to improve the accuracy in discovering communities in multiplex networks. The low-rank representation leads to reducing the noise and errors existing in the network and the optimization of an accurate consensus set of eigenvectors that reveals the communities in the network. Moreover, the proposed approach balances the agreement between the eigenvectors of each layer, i.e., individual subspaces, and the consensus set of eigenvectors, i.e., common subspaces, by minimizing the projection distance between them. The common and individual subspaces are computed efficiently through Tucker decomposition and modified spectral clustering, respectively. Finally, multiple experiments are conducted on real and simulated networks to evaluate the proposed approach and compare it to state-of-the-art algorithms. The proposed approach shows its robustness and efficiency in discovering the communities in multiplex networks.

Local Oscillator Power Adjustment-Based Adaptive Amplification for Coherent TDM-PON With Wide Dynamic Range

Coherent technology is a promising solution for future 100-Gb/s and even 200-Gb/s single-wavelength time-division multiplexing passive optical network (TDM-PON) systems. One of the key issues for coherent TDM-PON is the upstream burst-mode amplification. The limited resolution of the analog-to-digital converter (ADC) makes it challenging to detect burst signals with widely varying power levels. Local oscillator (LO) power adjustment is a promising solution to this problem. In the present study, we verified the feasibility of LO power adjustment for adaptive amplification in a 200-G coherent TDM-PON system. We investigated the overall performance of the system under different implementation conditions, such as ADC sampling scales, TIA MGC mode control voltages, LO optical power levels, and the received optical power (ROP). Simulation and experimental results indicated that LO power adjustment can widen the dynamic range of the coherent PON system. As a proof of concept, we tested the system performance with continuous-mode signals and signals of isolated patterns with a burst-mode data frame. The dynamic range was extended to >30 dB via LO power adjustment, with 38- and 37-dB power budgets for 200-Gb/s/λ coherent TDM-PON continuous signals and signals of isolated patterns, respectively, after a 50-km standard single-mode fiber (SSMF).

Variation in Andorran Spanish past perfectives

AbstractRomance varieties differ in their usage of preterit and present perfect verb tenses. Both are past perfectives, but whereas Portuguese uses the preterit in most contexts, spoken French prefers the present perfect. Peninsular Spanish lies between the two, though evidence indicates that the present perfect is becoming the default past perfective (Schwenter and Torres Cacoullos 2008) in a process of ‘aoristic drift’ (Squartini and Bertinetto 2000). How does speaker multilingualism affect this? Semi-structured interviews were conducted with second-generation members of the Portuguese diaspora in Andorra. We might expect native competence in Portuguese to inhibit aoristic drift in Spanish, since contact has been shown to affect past perfective verb tense in other Romance varieties (Gili Gaya 1993;Hawkey 2020). Contrary to expectations, participants demonstrated aoristic drift. Dense and multiplex migrant networks are, however, shown to favour the maintenance of vernacular norms (Milroy 1980), including generalising the function of the present perfect.

A general higher-order supracentrality framework based on motifs of temporal networks and multiplex networks

As the dynamics and complexity of networks evolve, identifying the important nodes in temporal networks (i.e., the relationships change over time) and multiplex networks (i.e., the layers can encode different types of relationships) becomes a core topic in network analysis and data science. However, the existing researches merely focus on the first-order relationships (i.e., the interactions only lie in two nodes) regardless of the higher-order relationships (i.e., the interactions exist among multiple nodes) especially for intralayer node interactions, which has been proved to be significant to affect centralities of nodes in the temporal networks and multiplex networks. Motivated by this, by observing and analyzing the interaction patterns of nodes in the temporal networks and multiplex networks, and considering the importance of triadic closure, first we adopt triangular motifs, and reconstruct the real-world system as temporal networks or multiplex networks fused with higher-order motifs information to encode the interactions among multiple nodes. Then we design a higher-order supracentrality framework to mine critical nodes with higher-order features in temporal networks and multiplex networks. The framework we proposed can characterize how the importance of nodes changes over time or different relationships, and also illustrate a complicated dependency of the higher-order supracentrality on the network topology, tunable weights of interlayer coupling and layer teleportation probability. To demonstrate the effectiveness and superiority of higher-order supracentrality framework based on motifs, we provide theoretical analysis and sufficient experiments on two empirical datasets including temporal networks and multiplex networks, the experimental results show that the higher-order supracentrality framework can identify much more abundant important nodes accurately. More significantly, our framework can provide a unifying foundation for centrality analysis in the temporal networks and multiplex networks, and also serve as a guidance for practical applications.

Auxiliary graph-based energy-efficient routing resource allocation algorithm in space division multiplexing elastic optical networks

Space division multiplexing elastic optical networks (SDM-EONs) are considered as a efficient solution for increasing the link’s capacity. However, the SDM-EONs brought challenges to be explored, notably the routing, core and spectrum assignment (RCSA) problem. For the real network, another important issue should be considered is the problem of energy consumption. The auxiliary graph (AG) can abstract node devices into elements in it and support various routing policies for different purposes by adjusting the weights of AG edges. Therefore, this paper proposes an auxiliary graph-based energy efficient routing resource allocation algorithm (AG-EERRA) to jointly optimize the energy consumption and RCSA. Each node of the auxiliary graph is abstracted from the devices of physical node. The intra edge weights of nodes set the function associated with the energy consumption model. The inter edge weights of node are composed link length, free frequency slots, modulation efficiency and the value of core crosstalk innovatively. Meanwhile, the impact of inter-core crosstalk on request is reduced by crosstalk avoidance strategy and crosstalk aware strategy. In the proposed auxiliary graph, the shortest weight path is selected to transmit requests that can reduce network energy consumption and blocking probability. Under the high-load network, request splitting is performed for the requests that cannot successfully find a route to improve energy efficiency. Simulation results show that the proposed algorithm can improve the network energy efficiency while reducing request blocking probability compared to comparison algorithms.

Collective dynamics analysis based on the multiplex network method to unravel the backbone of fluctuations in groundwater level data

Collective behavior of groundwater level (GWL) in the Tabriz plain, northwest Iran, was modeled in the framework of complex networks. The GWL is considerably influenced by the lack of recharge due to drought in recent years and the increasing water demand of the industrial city of Tabriz. In this order, convergent cross-mapping (CCM) method was utilized to infer the causal relations by adopting a robust statistical approach based on hypothesis testing. Reconstructing structure is a challenge in network-based approaches to capture the dynamical variability of the measurements.This study aims to construct functional connectivity for a better understanding of informational flows among the wells. Multiplex network (MUX) was used as a set of coupled networks through interconnected layers to structure the information obtained from collective dynamics and time-reversal. The proposed procedure was evaluated by performing a systematic analysis of random network (RN) as a well-known model to consider the topological characteristics. The influence of connectivity reconstruction on network topology was also investigated by the generated experiments for different numbers of the dynamical units and lengths of the simulations at each unit. The MUX was constructed from the GWL for monthly observations over 15 years (2001–2017). Afterwards, the time-series was divided into three categories to infer the network connections in different time periods for various sets of the monitoring wells.The analysis indicates that unphysical connections are reduced by increasing the number of the coupling units as a desirable feature for detecting the underlying connectivity. The MUX is employed with the proposed statistical approach to quantitatively unravel the backbone of fluctuations in the GWL whose indirect causal relations form the skeleton. The MUX dynamics efficiently determines the underlying processes and identifies spatio-temporal patterns of the GWL fluctuations in two layers of the constructed networks.

CoLM2S: Contrastive self‐supervised learning on attributed multiplex graph network with multi‐scale information

AbstractContrastive self‐supervised representation learning on attributed graph networks with Graph Neural Networks has attracted considerable research interest recently. However, there are still two challenges. First, most of the real‐word system are multiple relations, where entities are linked by different types of relations, and each relation is a view of the graph network. Second, the rich multi‐scale information (structure‐level and feature‐level) of the graph network can be seen as self‐supervised signals, which are not fully exploited. A novel contrastive self‐supervised representation learning framework on attributed multiplex graph networks with multi‐scale (named CoLM 2 S) information is presented in this study. It mainly contains two components: intra‐relation contrast learning and inter‐relation contrastive learning. Specifically, the contrastive self‐supervised representation learning framework on attributed single‐layer graph networks with multi‐scale information (CoLMS) framework with the graph convolutional network as encoder to capture the intra‐relation information with multi‐scale structure‐level and feature‐level self‐supervised signals is introduced first. The structure‐level information includes the edge structure and sub‐graph structure, and the feature‐level information represents the output of different graph convolutional layer. Second, according to the consensus assumption among inter‐relations, the CoLM2S framework is proposed to jointly learn various graph relations in attributed multiplex graph network to achieve global consensus node embedding. The proposed method can fully distil the graph information. Extensive experiments on unsupervised node clustering and graph visualisation tasks demonstrate the effectiveness of our methods, and it outperforms existing competitive baselines.

Vital node identification based on cycle structure in a multiplex network.

Multiplex networks frame the heterogeneous nature of real systems, where the multiple roles of nodes, both functionally and structurally, are well represented. We identify these vital nodes in a multiplex network so that we can control a pandemic outbreak like COVID-19, eliminate damage from a network attack, maintain traffic, and so on. Vital node identification has attracted scientists in various fields for decades. In this paper, we propose a hybrid supra-cycle number and hybrid supra-cycle ratio based on the cycle structure, and present an extensive experimental analysis by comparing our indexes and several different indexes in four real multiplex networks on layer nodes and multiplex nodes. The experimental results show that these proposed indexes have good robustness, synchronization, and transmission dynamics. Finally, we provide an in-depth understanding of multiplex networks and cycle structure, and we sincerely hope more valuable academic achievements are proposed in the future.

Robustness measurement of multiplex networks based on graph spectrum.

Networks can provide effective representations of the relationships between elements in complex systems through nodes and links. On this basis, relationships between multiple systems are often characterized as multilayer networks (or networks of networks). As a typical representative, a multiplex network is often used to describe a system in which there are many replaceable or dependent relationships among elements in different layers. This paper studies robustness measures for different types of multiplex networks by generalizing the natural connectivity calculated from the graph spectrum. Experiments on model and real multiplex networks show a close correlation between the robustness of multiplex networks consisting of connective or dependent layers and the natural connectivity of aggregated networks or intersections between layers. These indicators can effectively measure or estimate the robustness of multiplex networks according to the topology of each layer. Our findings shed new light on the design and protection of coupled complex systems.

Superdiffusion induced by complete structure in multiplex networks.

After the groundbreaking work by Gómez et al., the superdiffusion phenomenon on multiplex networks begins to attract researchers' attention. The emergence of superdiffusion means that the time scale of the diffusion process of the multiplex network is shorter than that of each layer. Using the optimization theory, the manuscript studies the greatest impact of one edge on the network diffusion speed. It is proved that by deleting any edge from a given network, the drop of the second smallest eigenvalue of its Laplacian matrix is at most 2. Based on the conclusion, the relation between the complete structure and the superdiffusible network is studied, and, further, some superdiffusion criteria on general duplex networks are proposed. Interestingly, the theoretical results indicate that the emergence of superdiffusion depends on the complete structure rather than the overlap one. Some numerical examples are shown to verify the effectiveness of the theoretical results.

Analysis of Epidemic Spreading With Opinion Evolution in Multiplex Network

In this brief, dynamical aspects of a coupled epidemic-opinion dynamical model are studied, in that the disease spreading network model namely Susceptible-Exposed-Infected-Vigilant (SEIV) compartmental model is employed over a physical transmission network, while the opinion evolution model is employed over a social network. In particular, the opinion evolution captures how severe the disease is perceived to be for communities. Through rigorous analysis, it has been shown that if the opinion-dependent basic reproduction rate <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$(R_{0})$ </tex-math></inline-formula> is less than unity, then the epidemic reaches disease free state, while if it is greater than unity, the disease free state is unstable. Also, sufficient condition for endemic state of the disease, where the disease persists in the network is obtained. The theoretical findings are corroborated by the simulation results.

A new full-duplex analog RoF transmission system for 5G/5G+ broadband mobile communication

Abstract A new cost-effective mobile fronthaul architecture has been proposed by designing promising analog radio over fiber (ARoF) transmission system compliant with the next generation ultra-dense wavelength division multiplexing passive optical network (UDWDM-PON). It is conceived for standalone 5G/5G+ mobile communication systems driven by broadband multi-input multi-output (MIMO) millimeter wave data signals using two high spectral efficient modulations. The feasibility of the proposed system was firstly proven in terms of spectral tracing supported by theoretical analysis that disclosed good agreement. Furthermore, the obtained simulation results regarding the system performance evaluation after 22 km bidirectional optical fiber links were revealed high reliable characteristics including error-free transmissions at low receiver sensitivities, and measured power penalties less than 1 dB. In addition, the recovered eye diagrams maintained wide openings with high-quality patterns.

The impact of disclosure of risk information on risk propagation in the industrial symbiosis network.

The interdependent symbiotic relationship between enterprises may bring potential risks to the stability of the industrial symbiosis network (ISN). In order to reduce the damage caused by further risk propagation to the system, this paper establishes the multiplex network to study the impact of disclosure of risk information on risk propagation. In the multiplex network, we use a small-world network to simulate a social network and propose an evolutionary model with scale-free characteristics to simulate the symbiotic relationships between enterprises. Then we establish a risk propagation model by defining transition rules among various states. Through theoretical analysis using the Microscopic Markov Chain Approach (MMCA), we find that the proportion of disclosed enterprises, the network structure of the ISN, the recovery rate of enterprises, and the degree of symbiotic dependence affect the risk propagation threshold of the ISN. Numerical simulation results show that increasing the disclosure probability of risk information can reduce the scope of risk propagation. Moreover, once the disclosure probability of risk information reaches a certain value, the risk propagation threshold can be increased. Finally, relevant suggestions are put forward: (i) strengthening the information communication between symbiotic enterprises may reduce risks caused by information asymmetry. (ii) In addition to the authenticity and integrity of risk information, it is necessary to prevent risk information from being over-interpreted or exaggerated. (iii) Enterprises should strengthen the ability to recover from risks, appropriately reduce the degree of symbiotic dependence, and enhance risk awareness to reduce the possibility of risk occurrence.