Search

Existing regionalization methods tend to be either spatial attribute- or spatial interaction-based, while real-world tasks usually involve both considerations to satisfy multiple objectives simultaneously. In this research, we propose Multilayer Network Community Detection and Kernel Extension (MNCD-KE), a two-step regionalization framework, as a feasible solution for such tasks. First, spatial attributes are embedded into attributes of nodes in a spatial interaction-defined multilayer network, and the kernel and marginal parts of the regions are determined by giving the membership value of the regionalization units to network communities. Second, the final result is obtained through a kernel extension process considering geographical constraints, including spatial contiguity, size balance, morphological regularity, and existing boundary consistency of the regions. Empirical experiments show that the proposed method yields outcomes that, in maintaining comparable performances with most baseline algorithms with either 'attribute' or 'interaction' objectives as measured by the respective criteria, simultaneously meet the dual objectives with results intuitively comprehensible. Its low computing costs and parameter adjustment flexibility make the proposed framework a convenient approach for real-world multi-objective regionalization tasks. We conclude the research with discussions on the boundary conditions for the framework to work and their relevance to city science theories, along with practical implications. [ABSTRACT FROM AUTHOR]

Technology cooperation and technology transfer can be beneficial for realising the flow of innovative resources and promoting technological progress and growth, which is crucial to achieving sustainable and coordinated economic development. We investigate the structural characteristics of interprovincial innovation multilayer network, also called technology cooperation and transfer network, including the technology cooperation sub-network and technology transfer sub-network. Data are derived from Patent Information Service Platform for 2008–2018. The results are as follows: (1) the two sub-networks and multilayer network show the increasing density, and all of those networks display large cluster coefficients and short average paths. The degree between different networks is highly relevant. GDP, indegree and outdegree are linearly related. (2) As the two sub-networks in different provinces become increasingly integrated, the core provinces control the flow in the network and the edge provinces surround the core provinces, indicating a clear 'core-edge' structure. (3) The exponential random graph model regression results demonstrate a significant 'path dependence' property for technology cooperation-transfer network. GDP, R&D, distance and railway freight flow have a significant impact on network format. [ABSTRACT FROM AUTHOR]

Innovation in peripheral areas relies more on external linkages to overcome peripherality than in core areas. We study how these linkages are formed, especially through the interaction with diverse networks like transport and investment networks that connect the periphery with external areas. Using data from peripheral Zhejiang, China, we construct a four-layer network with co-patenting and three explanatory layers: transport, investment, and university headquarters-branch linkages. We use multilayer exponential random graph modelling to examine factors driving the formation of the co-patent network, specifically cross-layer interactions between co-patenting and the other three networks. Results show a tendency for the periphery to be linked to the core through co-patenting. All the other three layers have a positive association with the co-patent network, and their intersections show a stronger correlation with the network. This is especially true for the university headquarter-branch network and its intersection with the investment network. The main contribution of this paper is expanding the analysis of peripheral innovation by considering broader intercity networks rather than solely focusing on innovation linkages. Our findings also have policy implications for overcoming innovation constraints in peripheral areas and reducing spatial disparities in Zhejiang and similar regions. [ABSTRACT FROM AUTHOR]

Aquatic invasive species (AIS) are one of the greatest threats to the functioning of aquatic ecosystems worldwide. Once an invasive species has been introduced to a new region, many governments develop management strategies to reduce further spread. Nevertheless, managing AIS in a new region is challenging because of the vast areas that need protection and limited resources. Spatial heterogeneity in invasion risk is driven by environmental suitability and propagule pressure, which can be used to prioritize locations for surveillance and intervention activities. To better understand invasion risk across aquatic landscapes, we developed a simulation model to estimate the likelihood of a waterbody becoming invaded with an AIS. The model included waterbodies connected via a multilayer network that included boater movements and hydrological connections. In a case study of Minnesota, we used zebra mussels (Dreissena polymorpha) and starry stonewort (Nitellopsis obtusa) as model species. We simulated the impacts of management scenarios developed by stakeholders and created a decision‐support tool available through an online application provided as part of the AIS Explorer dashboard. Our baseline model revealed that 89% of new zebra mussel invasions and 84% of new starry stonewort invasions occurred through boater movements, establishing it as a primary pathway of spread and offering insights beyond risk estimates generated by traditional environmental suitability models alone. Our results highlight the critical role of interventions applied to boater movements to reduce AIS dispersal. [ABSTRACT FROM AUTHOR]

Abstract Background In bioinformatics, interactions are modelled as networks, based on graph models. Generally, these support a single-layer structure which incorporates a specific entity (i.e., node) and only one type of link (i.e., edge). However, real-world biological systems consisting of biological objects belonging to heterogeneous entities, and these operate and influence each other in multiple contexts, simultaneously. Usually, node similarities are investigated to assess the relatedness between biological objects in a network of interest, and node embeddings are widely used for studying novel interaction from a topological point of view. About that, the state-of-the-art presents several methods for evaluating the node similarity inside a given network, but methodologies able to evaluate similarities between pairs of nodes belonging to different networks are missing. The latter are crucial for studies that relate different biological networks, e.g., for Network Alignment or to evaluate the possible evolution of the interactions of a little-known network on the basis of a well-known one. Existing methods are ineffective in evaluating nodes outside their structure, even more so in the context of multilayer networks, in which the topic still exploits approaches adapted from static networks. In this paper, we presented pyMulSim, a novel method for computing the pairwise similarities between nodes belonging to different multilayer networks. It uses a Graph Isomorphism Network (GIN) for the representative learning of node features, that uses for processing the embeddings and computing the similarities between the pairs of nodes of different multilayer networks. Results Our experimentation investigated the performance of our method. Results show that our method effectively evaluates the similarities between the biological objects of a source multilayer network to a target one, based on the analysis of the node embeddings. Results have been also assessed for different noise levels, also through statistical significance analyses properly performed for this purpose. Conclusions PyMulSim is a novel method for computing the pairwise similarities between nodes belonging to different multilayer networks, by using a GIN for learning node embeddings. It has been evaluated both in terms of performance and validity, reporting a high degree of reliability.

Background: In bioinformatics, interactions are modelled as networks, based on graph models. Generally, these support a single-layer structure which incorporates a specific entity (i.e., node) and only one type of link (i.e., edge). However, real-world biological systems consisting of biological objects belonging to heterogeneous entities, and these operate and influence each other in multiple contexts, simultaneously. Usually, node similarities are investigated to assess the relatedness between biological objects in a network of interest, and node embeddings are widely used for studying novel interaction from a topological point of view. About that, the state-of-the-art presents several methods for evaluating the node similarity inside a given network, but methodologies able to evaluate similarities between pairs of nodes belonging to different networks are missing. The latter are crucial for studies that relate different biological networks, e.g., for Network Alignment or to evaluate the possible evolution of the interactions of a little-known network on the basis of a well-known one. Existing methods are ineffective in evaluating nodes outside their structure, even more so in the context of multilayer networks, in which the topic still exploits approaches adapted from static networks. In this paper, we presented pyMulSim, a novel method for computing the pairwise similarities between nodes belonging to different multilayer networks. It uses a Graph Isomorphism Network (GIN) for the representative learning of node features, that uses for processing the embeddings and computing the similarities between the pairs of nodes of different multilayer networks. Results: Our experimentation investigated the performance of our method. Results show that our method effectively evaluates the similarities between the biological objects of a source multilayer network to a target one, based on the analysis of the node embeddings. Results have been also assessed for different noise levels, also through statistical significance analyses properly performed for this purpose. Conclusions: PyMulSim is a novel method for computing the pairwise similarities between nodes belonging to different multilayer networks, by using a GIN for learning node embeddings. It has been evaluated both in terms of performance and validity, reporting a high degree of reliability. [ABSTRACT FROM AUTHOR]

The interprovincial circulation of goods and services has formed virtual water flows between regions, which can redistribute water resources. Based on previous virtual water trade research, this study further explored the multiple dependencies of virtual water, i.e., direct, indirect, and complete dependence. This study examined the direct, indirect, and complete dependence of virtual water between provinces in China by constructing multilayer dependence networks and identified the key regions and paths of virtual water trade network. The results showed direct dependence was the densest and had the largest overall dependence degree, but indirect dependence was the most stable and orderly. Second, the dominant provinces were Guangxi, Hunan, Sichuan, Xinjiang, and Anhui, referred to as "core‒five‒region," and the flow relevant to them accounted for approximately 30% of the virtual water. The seven provinces of Shanxi, Zhejiang, Shandong, Hubei, Guangdong, Shaanxi, and Gansu depend both directly and indirectly on the "core‒five‒region." Shanxi and Zhejiang have close direct and indirect dependence, with more than one of the "core‒five‒region." Guangdong was the province with the most direct and indirect input of virtual water from the "core‒five‒region." The study provides a scientific basis for multiregional identification for the collaborative management of water resources in China from the perspective of dependence. [ABSTRACT FROM AUTHOR]

Abstract Background Biological networks have proven invaluable ability for representing biological knowledge. Multilayer networks, which gather different types of nodes and edges in multiplex, heterogeneous and bipartite networks, provide a natural way to integrate diverse and multi-scale data sources into a common framework. Recently, we developed MultiXrank, a Random Walk with Restart algorithm able to explore such multilayer networks. MultiXrank outputs scores reflecting the proximity between an initial set of seed node(s) and all the other nodes in the multilayer network. We illustrate here the versatility of bioinformatics tasks that can be performed using MultiXrank. Results We first show that MultiXrank can be used to prioritise genes and drugs of interest by exploring multilayer networks containing interactions between genes, drugs, and diseases. In a second study, we illustrate how MultiXrank scores can also be used in a supervised strategy to train a binary classifier to predict gene-disease associations. The classifier performance are validated using outdated and novel gene-disease association for training and evaluation, respectively. Finally, we show that MultiXrank scores can be used to compute diffusion profiles and use them as disease signatures. We computed the diffusion profiles of more than 100 immune diseases using a multilayer network that includes cell-type specific genomic information. The clustering of the immune disease diffusion profiles reveals shared shared phenotypic characteristics. Conclusion Overall, we illustrate here diverse applications of MultiXrank to showcase its versatility. We expect that this can lead to further and broader bioinformatics applications.

• A novel gravity model integrating multilayer network for waste trade is executed. • Multilayer network results show new patterns challenge the North-South pattern. • Northern waste havens indicate a recycling market for textile and metal scrap waste. • PHH is better supported for highly connected hubs and havens in SDT waste. • Stringent waste management policies reduce exports and imports in SDT waste. Waste management is an international enterprise, and it is important to understand global flows of recyclable materials. The Pollution Haven Hypothesis (PHH) suggests that waste moves from high income nations with stringent environmental policy to low income nations with less environmentally stringent policy, by exploiting low labor and regulatory costs. This paper assesses the PHH thesis for slag/dross and textiles (SDT) wastes in PHH through novel integration of the multilayer network and gravity models. The multilayer network model generates network effect that quantifies interlayer connections of multiple waste trade networks. Instead of North-South movement of waste, North-North, South-South, and even South-North are shown. Results from the gravity model indicate that stringent waste management policies reduce both waste exports and imports. PHH is not found for slags/dross where high income countries are importing the waste, contradicting PHH. On the other hand, PHH is more evident between highly connected hubs and havens in SDT waste trade networks. [ABSTRACT FROM AUTHOR]

This paper uses insights from new economic geography (NEG) to examine eight commodities that span the COVID-19 medical value chain from 2000 to 2020 based on the multilayer network approach. Such an approach is distinguished from single layer approaches by taking into consideration interlayer connections. Centrality measures are based on the HITs algorithm to identify dominant exporters (hubs) and importers (authorities). We further examine the determinants of the multilayer trade networks with a gravity model. Three results are reported. First, the core-periphery structure under NEG has weakened over time with the internationalization of production in certain Covid medical commodities (CMCs). Second, home market effect remains relatively strong over time reflecting the role of NEG's agglomeration economies in locations with large home market demand. Finally, countries that restricted certain CMCs (especially PPEs and masks) even as they simultaneously facilitated the imports of other CMCs saw a decrease in exports. [ABSTRACT FROM AUTHOR]

Graph distance measures have emerged as an effective tool for evaluating the similarity or dissimilarity between graphs. Recently, there has been a growing trend in the application of movie networks to analyze and understand movie stories. Previous studies focused on computing the distance between individual characters in narratives and identifying the most important ones. Unlike previous techniques, which often relied on representing movie stories through single-layer networks based on characters or keywords, a new multilayer network model was developed to allow a more comprehensive representation of movie stories, including character, keyword, and location aspects. To assess the similarities among movie stories, we propose a methodology that utilizes a multilayer network model and layer-to-layer distance measures. We aim to quantify the similarity between movie networks by verifying two aspects: (i) regarding many components of the movie story and (ii) quantifying the distance between their corresponding movie networks. We tend to explore how five graph distance measures reveal the similarity between movie stories in two aspects: (i) finding the order of similarity among movies within the same genre, and (ii) classifying movie stories based on genre. We select movies from various genres: sci-fi, horror, romance, and comedy. We extract movie stories from movie scripts regarding character, keyword, and location entities to perform this. Then, we compute the distance between movie networks using different methods, such as the network portrait divergence, the network Laplacian spectra descriptor (NetLSD), the network embedding as matrix factorization (NetMF), the Laplacian spectra, and D-measure. The study shows the effectiveness of different methods for identifying similarities among various genres and classifying movies across different genres. The results suggest that the efficiency of an approach on a specific network type depends on its capacity to capture the inherent network structure of that type. We propose incorporating the approach into movie recommendation systems. [ABSTRACT FROM AUTHOR]

This research extensively explores contemporary techniques in Underwater Wireless Power and Data Transfer (WPDT), as found in recent literature over the past 5 years. These techniques involve Magnetic Induction (MI), acoustic communication, and optical communication. The study aims to evaluate transmission efficiency, propose solutions to overcome range limitations, facilitate integration, and precisely map multilayer networks. The research methodology includes proposed solutions to address the limitations of each transmission technique, multilayer network mapping, and model validation. Evaluation is conducted on the relative efficiency of each method, including power loss at each layer, the connection matrix between layers, and transmission speed. Simulations reveal power loss at nodes in each layer and the connection matrix between layers. The power loss at each node shows random values, providing a realistic aspect close to real-world scenarios, offering in-depth insights into the characteristics and performance of multilayer networks in an underwater context. The research series, involving literature elaboration, technological approaches, illustrations, and simulations, demonstrates the effectiveness of the proposed model. Simulation results indicate the potential of this model in real-world scenarios, with tolerable power loss values, suggesting that multilayer networks could be a solution to classic challenges in underwater power and data transmission. The hope of this preliminary study is to provide new insights and make a significant contribution to understanding and designing reliable underwater power and data transfer systems in the future.

International ports play critical roles in maintaining transportation flows and sustaining the effectiveness of global maritime logistics. Although the concept of versatile ports has been introduced to represent the specific patterns that these ports play at the regional and global levels, there is still a need for the design and development of computational approaches that support these notions. This paper introduces a flexible multi-layer network approach for an international maritime network whose interest is that it offers a flexible model that favours the identification of the different transportation flows and versatile ports. The model is complemented by a series of structural indices complemented by a new overlap measure that evaluates the specific role of a given port across various transportation trades. The whole approach is implemented and experimented with using massive AIS maritime data that supports the automatic generation of the multi-layer network, and derivation of the structural measures while maintaining a flexible view of the maritime network. The experiments applied to the global maritime transportation network identify key versatile ports and highlight significant differences at the regional and trade flow levels.

The multilayer stochastic block model is one of the fundamental models in multilayer networks and is often used to represent multiple types of relations between different individuals. In this paper, we extend the profile‐pseudo likelihood method for the single‐layer stochastic block model to the case of the multilayer stochastic block model. Specifically, by assuming all network layers have identical community membership labels, we investigate the multilayer stochastic block model with a common community structure. In this paper, we develop a profile‐pseudo likelihood algorithm to fit a multilayer stochastic block model and estimate the community label. Meantime, we prove that the algorithm has convergence guarantee and that the estimated community label is strongly consistent. Further, for estimating the number of communities K$$ K $$, we extend the corrected Bayesian information criterion to multilayer stochastic block models. We also extend this algorithm to fit the multilayer degree‐corrected stochastic block model. Both simulation studies and real‐world data examples indicate that the proposed method works well. [ABSTRACT FROM AUTHOR]

We study the emergence of meandering spiral waves in a multilayer structure where two spirals, originating independently, evolve in space--time. The FitzHugh-Nagumo model, enhanced with electromagnetic induction effects, defines the nodal dynamics. The layers are chemically coupled, and the drift of spirals is influenced by interlayer flux coupling. An external magnetic flux force is also necessary to destabilize and unpin spiral rotors. The effects of unique characteristics of spiral patterns, like chirality and tip curvature, are assessed. We find that spirals often drift within a bounded meandering area; however, determining the drift directions and overall meandering regions is complex. The forced spiral generally drifts a greater distance, except for identical co-rotating spirals, which drift synchronously. Spirals with opposite chirality exhibit asynchronous drift and wavefront asymmetry, even with identical tip curvature. Regardless of chirality, non-identical spirals are geometrically aligned and amplified before drifting. Stimulating the more loosely curved spiral ensures both spirals drift. [ABSTRACT FROM AUTHOR]

Brain functional network disruption and neurocognitive dysfunction have been reported in obstructive sleep apnea (OSA) patients. Nevertheless, most research studies static networks, while brain evolution continues dynamically. To investigate the characteristics of dynamical networks in moderate-to-severe OSA patients using multilayer network analysis of dynamic networks and compare their association with neurocognitive function. Twenty-seven moderate-to-severe OSA patients and twenty-five matched healthy controls (HCs) who completed the examination of the Epworth sleepiness scale (ESS), neurocognitive function, polysomnography, and functional magnetic resonance imaging (fMRI) were prospectively included. The dynamic variations of resting-state functional networks in both groups were described via network switching rate. Switching rates and their correlation with clinical parameters were analyzed. At the global level, network switching rates were notably lower in the OSA group than in the HCs group (p = 0.002). More specifically, the differences include the default mode network (DMN), auditory network, and ventral attention network at the subnetwork level, and the right rolandic operculum, left middle temporal gyrus, and right precentral gyrus at the nodal level. Furthermore, these altered switching rates have a close correlation with ESS, sleep parameters, and neurocognitive function. Patients with moderate-to-severe OSA showed lower network switching rates, especially in the DMN, auditory network, and ventral attention network. The disruption of dynamic functional networks may be a potentially crucial mechanism of neurocognitive dysfunction in moderate-to-severe OSA patients. • Multilayer network analysis captures dynamic variations in the brain. • Moderate-to-severe OSA patients experience lower brain network switching rates. • Disruption of the dynamic functional networks closely links to neurocognition. • Multilayer network analysis offers new insights into cognitive deficits in OSA. [ABSTRACT FROM AUTHOR]

A Multilayer network is a potent platform that paves the way for the study of the interactions among entities in various networks with multiple types of relationships. This study explores the dynamics of discrete-time quantum walks on a multilayer network. We derive a recurrence formula for the coefficients of the wave function of a quantum walker on an undirected graph with a finite number of nodes. By extending this formula to include extra layers, we develop a simulation model to describe the time evolution of the quantum walker on a multilayer network. The time-averaged probability and the return probability of the quantum walker are studied with Fourier, and Grover walks on multilayer networks. Furthermore, we analyze the impact of decoherence on quantum transport, shedding light on how environmental interactions may impact the behavior of quantum walkers on multilayer network structures. [ABSTRACT FROM AUTHOR]

Abstract In times of a global pandemic, public transit can be crucial to spreading viruses, especially in big cities. Many works have shown that the human infection risk could be extremely high due to the length of exposure time, transmission routes, and structural characteristics during public transportation, and this can result in the rapid spread of the infection. Vaccines are often part of strategies to reduce contagion; however, they can be scarce in pandemic settings. Considering real-world and large-scale traffic data, this work proposes using time-varying multilayer networks to identify the main critical places to be prioritized in interventions, such as vaccination campaigns, to help reduce contagion on public transit. We exemplify our strategy in different vaccination scenarios. First, when considering only critical bus stops as priority vaccination points, determined by our approach, we indicate that focusing on vaccination in these locations reduces the spread of infection using fewer doses than a random vaccination. In another experiment, we demonstrate the flexibility of our approach in identifying other critical points of interest, healthcare units in this case. Vaccination in these vital health units could also be a viable strategy to curb contagion using a predetermined number of doses. The approach proposed in this study is not limited to vaccination strategies. It also applies to other problems that share similar properties, even in several different contexts, such as optimization in public transit or exploring different points of interest to gather insights from other issues of interest.

Network intrusion detection has been widely discussed and studied as an important part of protecting network security. Therefore, this paper presents an in-depth study of the application of an improved V-detector algorithm in network intrusion detection. In this paper, we construct a V-detector intrusion detection model, adopt the “self-oriented” identification principle, and randomly generate detectors with large differences from the health library. A smaller number of detectors are used to compare the data information generated by the computer, and if they are similar, they are judged as intrusions. Intrusion detection experiments are performed on multiple types of networks by using classifiers to determine whether the access to be detected is an attack access. The experimental results show that the model has the lowest false alarm rate for mixed feature networks, with a false alarm rate of only 13% and a detection rate of 89%, with a sample size of 25,987. After the improvement of the V-detector intrusion detection model, the error correction output problem leads to a network intrusion with a miss rate of only 11% and a protection rate of 85%. The experimental data proved that the model has the advantages of large data size and comprehensive intrusion attack types.

Multilayer Network (MN) is a complex network comprising a set of graphs (also referred to as layers) interconnected by edges or interlayer edges (or inter-edges) linking the nodes of different layers. In biology, a MN may model interactions among diseases, genes, and drugs, only using its structure. Recently, there has been a growing interest in comparing two MNs by revealing local regions of similarity as a counterpart of Network Alignment algorithms (NA) for simple networks. However, classical algorithms for NA such as Local NA (LNA) cannot be applied on multilayer networks, since they are not able to deal with interlayer edges. Therefore, there is a need for the introduction of novel algorithms. In this paper, we present MuLan, an algorithm for the local alignment of multilayer networks. MuLan is based on the building of a multilayer alignment graph starting from a set of seed nodes. Then it analyses such a graph by revealing conserved regions. We first show as proof of concept the performances of MuLaN on a set of synthetic multilayer networks. Then, we used as a case study a real multilayer network in the biomedical domain. Our results show that MuLaN can build high-quality alignments and can extract knowledge about the aligned multilayer networks. MuLaN is available at https://github.com/pietrocinaglia/mulan.

This paper proposes a low-profile highisolation dual-polarization array antenna operating in the L/S band. The radiating unit of the array antenna adopts U-shaped bending dipoles to realize the staggered arrangement of high- and low-band units, and is connected to the feeding network by a coaxial cable. To achieve the non-influence of high-band and low-band feed networks, multi-layer printing technology is used and grooves are etched on the metal plate. In order to reduce the coupling of the high- and low-band dipoles at low frequency, a bandpass filter is added to the highband feed network, which greatly improves the isolation at low-frequency. The measured results show that the impedance bandwidth of the array is 4.5% (0.971-1.015 GHz) and 7.5% (1.89-2.04 GHz), with gains of 15 and 19 dBi at 1 GHz and 2 GHz, respectively. And the isolation between the dual-polarized ports more than 50 dB and 35 dB was achieved at 1 GHz and 2 GHz, respectively. The proposed array will have practical value in L/S band full-duplex wireless communication systems. [ABSTRACT FROM AUTHOR]

In this paper, we formulate and solve the urban line planning problem considering a multilayer representation of a bimodal transportation network. Classical formulations are usually constructed over a planar network, which implies the need to introduce several strong non-linearities in terms of frequencies when modeling transfer times. In the proposed network representation, each candidate line is stored in a specific layer and the passengers' movements for each origin–destination pair are modelled considering a strategy subgraph, contributing to a sparse model formulation that guarantees feasibility and simplifies the assignment process. The methodology is first tested using the Mandl network, obtaining results that are comparable in terms of quality with the best metaheuristic approaches proposed in the scientific literature. With the aim of testing its applicability to large scenarios, the proposed approach is then used to design the main urban transit network of Seville, a large scenario with 141 nodes and 454 links, considering artificial unfavorable demand data. The reasonable computation time required to exactly solve the problem to optimality confirms the possibility of using the multilayer approach to deal with multimodal network design strategic problems. [ABSTRACT FROM AUTHOR]

Social organisms often show collective behaviors such as group foraging or movement. Collective behaviors can emerge from interactions between group members and may depend on the behavior of key individuals. When social interactions change over time, collective behaviors may change because these behaviors emerge from interactions among individuals. Despite the importance of, and growing interest in, the temporal dynamics of social interactions, it is not clear how to quantify changes in interactions over time or measure their stability. Furthermore, the temporal scale at which we should observe changes in social networks to detect biologically meaningful changes is not always apparent. Here we use multilayer network analysis to quantify temporal dynamics of social networks of the social spider Stegodyphus dumicola and determine how these dynamics relate to individual and group behaviors. We found that social interactions changed over time at a constant rate. Variation in both network structure and the identity of a keystone individual was not related to the mean or variance of the collective prey attack speed. Individuals that maintained a large and stable number of connections, despite changes in network structure, were the boldest individuals in the group. Therefore, social interactions and boldness are linked across time, but group collective behavior is not influenced by the stability of the social network. Our work demonstrates that dynamic social networks can be modeled in a multilayer framework. This approach may reveal biologically important temporal changes to social structure in other systems.

Graph distance measures have emerged as an effective tool for evaluating the similarity or dissimilarity between graphs. Recently, there has been a growing trend in the application of movie networks to analyze and understand movie stories. Previous studies focused on computing the distance between individual characters in narratives and identifying the most important ones. Unlike previous techniques, which often relied on representing movie stories through single-layer networks based on characters or keywords, a new multilayer network model was developed to allow a more comprehensive representation of movie stories, including character, keyword, and location aspects. To assess the similarities among movie stories, we propose a methodology that utilizes a multilayer network model and layer-to-layer distance measures. We aim to quantify the similarity between movie networks by verifying two aspects: (i) regarding many components of the movie story and (ii) quantifying the distance between their corresponding movie networks. We tend to explore how five graph distance measures reveal the similarity between movie stories in two aspects: (i) finding the order of similarity among movies within the same genre, and (ii) classifying movie stories based on genre. We select movies from various genres: sci-fi, horror, romance, and comedy. We extract movie stories from movie scripts regarding character, keyword, and location entities to perform this. Then, we compute the distance between movie networks using different methods, such as the network portrait divergence, the network Laplacian spectra descriptor (NetLSD), the network embedding as matrix factorization (NetMF), the Laplacian spectra, and D-measure. The study shows the effectiveness of different methods for identifying similarities among various genres and classifying movies across different genres. The results suggest that the efficiency of an approach on a specific network type depends on its capacity to capture the inherent network structure of that type. We propose incorporating the approach into movie recommendation systems.

Networks (or graphs) are used to model the dyadic relations between entities in complex systems. Analyzing the properties of the networks reveal important characteristics of the underlying system. However, in many disciplines, including social sciences, bioinformatics, and technological systems, multiple relations exist between entities. In such cases, a simple graph is not sufficient to model these multiple relations, and a multilayer network is a more appropriate model. In this paper, we explore community detection in multilayer networks. Specifically, we propose a novel network decoupling strategy for efficiently combining the communities in the different layers using the Boolean primitives AND, OR, and NOT. Our proposed method, network decoupling, is based on analyzing the communities in each network layer individually and then aggregating the analysis results. We (i) describe our network decoupling algorithms for finding communities, (ii) present how network decoupling can be used to express different types of communities in multilayer networks, and (iii) demonstrate the effectiveness of using network decoupling for detecting communities in real-world and synthetic data sets. Compared to other algorithms for detecting communities in multilayer networks, our proposed network decoupling method requires significantly lower computation time while producing results of high accuracy. Based on these results, we anticipate that our proposed network decoupling technique will enable a more detailed analysis of multilayer networks in an efficient manner.

With the increasing success of mapping brain networks and availability of multiple MR- and PET-based connectivity measures, the need for novel methodologies to unravel the structure and function of the brain at multiple spatial and temporal scales is emerging. Therefore, in this work, we used hybrid PET-MR data of healthy volunteers (n = 67) to identify multiplex core nodes in the human brain. First, monoplex networks of structural, functional and metabolic connectivity were constructed, and consequently combined into a multiplex SC-FC-MC network by linking the same nodes categorically across layers. Taking into account the multiplex nature using a tensorial approach, we identified a set of core nodes in this multiplex network based on a combination of eigentensor centrality and overlapping degree. We introduced a coreness coefficient, which mitigates the effect of modeling parameters to obtain robust results. The proposed methodology was applied onto young and elderly healthy volunteers, where differences observed in the monoplex networks persisted in the multiplex as well. The multiplex core showed a decreased contribution to the default mode and salience network, while an increased contribution to the dorsal attention and somatosensory network was observed in the elderly population. Moreover, a clear distinction in eigentensor centrality was found between young and elderly healthy volunteers.

This work proposes a sentence network-based approach for performing the task of multi-document text summarization. The sentences of the input set of documents are represented by the nodes of the network. Weighted edges are added between the nodes to represent the semantic similarity between the corresponding sentences. The network has a multilayer structure, where each layer corresponds to an individual input document. This helps in effective differentiation between the inter-document and intra-document edges. A hyperparameter, namely layering factor, has been used to alter the strength of inter-document connections through reinforcement or weakening. It is hypothesized that the summary sentence nodes must act as effective information spreaders in the sentence network. Summary generation is performed by identifying the influential nodes of the network using VoteRank scheme. A comparative study with different network measures, such as Weighted Degree, PageRank, Betweenness centrality, and Closeness centrality reveals the efficacy of the proposed VoteSumm technique for multi-document text summarization. Improved performance is observed when an additional pre-processing step of syntactic simplification is applied on the raw text. Performance is further improved when keyword information is included in the simplified texts. [ABSTRACT FROM AUTHOR]

Networks are now widely used to represent, quantify and model animal behaviour. These approaches have proved valuable in linking individual behaviours to emergent population level patterns and quantifying the implications of these population structures for wider ecological and evolutionary processes. However, there are diverse conceptual representations of network data and choosing the right tool to answer a particular question can be challenging. Here I provide an overview of different network representations, highlighting their potential applications in behavioural ecology and drawing attention to key resources to help with their implementation. My aim is to provide an accessible guide that helps behavioural ecologists take full advantage of the potential of the different ways in which their data can be used to generate social other networks. • A guide of different approaches to represent animal social network data. • An overview of how to employ and when best to use different network representations. • Introduction to less used methods such as multigraphs, and signed and ego networks. • Case study showing association data as bipartite, dyadic and higher-order networks. [ABSTRACT FROM AUTHOR]

This paper focuses on the application of higher-order and multilayer networks in identifying critical causes and relationships contributing to hazardous materials transportation accidents. There were 792 accidents of hazardous materials transportation that occurred on the road from 2017 to 2021 which have been investigated. By considering time sequence and dependency of causes, the hazardous materials transportation accidents causation network (HMTACN) was described using the higher-order model. To investigate the structure of HMTACN such as the importance of causes and links, HMTACN was divided into three layers using the weighted k-core decomposition: the core layer, the bridge layer and the peripheral layer. Then causes and links were analyzed in detail. It was found that the core layer was tightly connected and supported most of the causal flows of HMTACN. The results showed that causes should be given hierarchical attention. This study provides an innovative method to analyze complicated accidents, which can be used in identifying major causes and links. And this paper brings new ideas about safety network study and extends the applications of complex network theory. [ABSTRACT FROM AUTHOR]

Complex networks in reality are not just single-layer networks. The connection of nodes in an urban metro network includes two kinds of connections: line and passenger flow. In fact, it is a multilayer network. The line network constructed by the Space L model based on a complex network reflects the geographical proximity of stations, which is an undirected and weightless network. The passenger flow network constructed with smart card big data reflects the passenger flow relationship between stations, which is a directed weighted network. The construction of a line-flow multilayer network can reflect the actual situation of metro traffic passenger flow, and the node clustering coefficient can measure the passenger flow clustering effect of the station on adjacent stations. Combined with the situation of subway lines in Nanjing and card-swiping big data, this research constructs the line network with the Space L model and the passenger flow network with smart card big data, and uses these two networks to construct the multilayer network of line flow. This research improves the calculation method of the clustering coefficient of weighted networks, proposes the concept of node group, distinguishes the inflow and outflow, and successively calculates the clustering coefficient of nodes and the whole network in the multilayer network. The degree of passenger flow activity in the network thermal diagram is used to represent the passenger flow activity of the line-flow network. This method can be used to evaluate the clustering effect of metro stations and identify the business districts in the metro network, so as to improve the level of intelligent transportation management and provide a theoretical basis for transportation construction and business planning. [ABSTRACT FROM AUTHOR]

We present a numerical implementation for a multilayer network to model the transmission of Covid-19 or other diseases with a similar transmission mechanism. The model incorporates different contact types between individuals (household, social and sporadic networks) and includes an SEIR type model for the transmission of the virus. The algorithm described in this paper includes the main ideas of the model used to give public health authorities an additional tool for the decision-making process in Costa Rica by simulating extensive possible scenarios and projections. We include two simulations: a study of the effect of restrictions on the transmission of the virus and a Costa Rica case study that was shared with the Costa Rican health authorities.

Introduction. Determination of mechanical properties of layered structures of highways is an urgent task. This is due, firstly, to the need to control the quality of new sections during the construction of highways. Secondly, to assess the condition of existing roads with the accumulation of damage and defects is of interest. The formation of multiple defects (cracks) changes the averaged viscoelastic properties of the components of the structure, specifically, the surface asphalt-concrete layers. The article discusses the use of neural network technologies to improve the accuracy of the recovery of viscoelastic properties. This approach is based on experimental methods. As an example, we can give the definition of the dynamic deflection of a structure from a falling weight, FWD.Materials and Methods. The elastic modulus of a three-layer structure was determined on the basis of a neural network. To find out the solution accuracy, it was compared to the results of mathematical modeling and experimental data.Results. The experimental and calculated parameters of the elastic modulus of individual layers of the road structure turned out to be very close. The proposed approach to determining the mechanical properties of materials of road structures allowed us to apply the obtained results to examination of the condition of individual elements and the entire road structure.Discussion and Conclusions. The prospects of using artificial intelligence to determine the mechanical properties of layered structures was shown. Further improvement of methods and tools for analyzing the behavior of road structures under dynamic loading will expand existing approaches to assessing the condition of road structures.

Abstract Background Gene expression is regulated at different molecular levels, including chromatin accessibility, transcription, RNA maturation, and transport. These regulatory mechanisms have strong connections with cellular metabolism. In order to study the cellular system and its functioning, omics data at each molecular level can be generated and efficiently integrated. Here, we propose BRANEnet, a novel multi-omics integration framework for multilayer heterogeneous networks. BRANEnet is an expressive, scalable, and versatile method to learn node embeddings, leveraging random walk information within a matrix factorization framework. Our goal is to efficiently integrate multi-omics data to study different regulatory aspects of multilayered processes that occur in organisms. We evaluate our framework using multi-omics data of Saccharomyces cerevisiae, a well-studied yeast model organism. Results We test BRANEnet on transcriptomics (RNA-seq) and targeted metabolomics (NMR) data for wild-type yeast strain during a heat-shock time course of 0, 20, and 120 min. Our framework learns features for differentially expressed bio-molecules showing heat stress response. We demonstrate the applicability of the learned features for targeted omics inference tasks: transcription factor (TF)-target prediction, integrated omics network (ION) inference, and module identification. The performance of BRANEnet is compared to existing network integration methods. Our model outperforms baseline methods by achieving high prediction scores for a variety of downstream tasks.

Feed-forward effect strongly modulates collective behavior of a multiple-layer neuron network and usually facilitates synchronization as signals are propagated to deep layers. However, a full synchronization of neuron system corresponds to functional disorder. In this work, we focus on a network containing two layers as the simplest model for multiple layers to investigate pattern selection during interaction between two layers. We first confirm that the chimera state emerges in layer 1 and it also induces chimera in layer 2 when the feed-forward effect is strong enough. A cluster is discovered as a transient state which separates full synchronization and chimera state and occupy a narrow region. Second, both feed-forward and back-forward effects are considered and we discover chimera states in both layers 1 and 2 under the same parameter for a large range of parameters selection. Finally, we introduce adaptive dynamics into inter-layer rather than intra-layer couplings. Under this circumstance, chimera state can still be induced and coupling matrix will be self-organized under suitable phase parameter to guarantee chimera formation. Indeed, chimera, cluster and synchronization can propagate from one layer to another in a regular multiple network for a corresponding parameter selection. More importantly, adaptive coupling is proved to control pattern selection of neuron firing in a network and this plays a key role in encoding scheme. [ABSTRACT FROM AUTHOR]

Community detection remains a challenging research hotspot in network analysis. With the complexity of the network data structures increasing, multilayer networks, in which entities interact through multiple types of connections, prove to be effective in describing complex networks. The layers in a multilayer network may not share a common community structure. In this paper, we propose a joint method based on matrix factorization and spectral embedding to recover the groups not only for the layers but also for nodes. Specifically, the layers are grouped via the matrix factorization method with layer similarity-based regularization in the perspective of a mixture multilayer stochastic block model, and then the node communities within a layer group are revealed by clustering a combination of the spectral embedding derived from the adjacency matrices and the shared approximation matrix. Numerical studies show that the proposed method achieves competitive clustering results as the number of nodes and/or number of layers vary, together with different topologies of network layers. Additionally, we apply the proposed method on two real-world multilayer networks and obtain interesting findings which again highlight the effectiveness of our method. [ABSTRACT FROM AUTHOR]

Analyzing the structures of multilayer networks (MLNs) has been a hot research topic in network science. Community detection algorithms are important tools for analyzing MLNs. In the literature, several community detection algorithms for MLNs have been proposed. Moreover, there are several options for the graph representation of an MLN: for example, directed or undirected, weighted or unweighted, and using information from all or only some layers. Although these options may affect the results of community detection in MLNs, representations that are effective for community detection have not yet been clarified. In this paper, we experimentally evaluate the effectiveness of three types of community detection algorithms for MLNs and examine how the graph representation of an MLN affects the results of these algorithms. Our main findings are as follows: (1) The flattening approach is particularly effective, whereas the layer-by-layer approach is not applicable to detecting communities in MLNs of Twitter users. (2) Using a directed graph for each layer of an MLN increases the accuracy of community detection. (3) The Leiden method, which is a community detection algorithm for single-layer networks, achieves comparable accuracy with the community detection algorithms for MLNs, which suggests that there exists room for improvement in multilayer community detection algorithms for effectively utilizing the multilayer structures of MLNs. [ABSTRACT FROM AUTHOR]

Introduction: The study of brain function has been favored by scientists, but the mechanism of short-term memory formation has yet to be precise. Research problem: Since the formation of short-term memories depends on neuronal activity, we try to explain the mechanism from the neuron level in this paper. Research contents and methods: Due to the modular structures of the brain, we analyze the pattern properties of the FitzHugh-Nagumo model (FHN) on a multilayer network (coupled by a random network). The conditions of short-term memory formation in the multilayer FHN model are obtained. Then the time delay is introduced to more closely match patterns of brain activity. The properties of periodic solutions are obtained by the central manifold theorem. Conclusion: When the diffusion coeffcient, noise intensity np, and network connection probability p reach a specific range, the brain forms a relatively vague memory. It is found that network and time delay can induce complex cluster dynamics. And the synchrony increases with the increase of p. That is, short-term memory becomes clearer. [ABSTRACT FROM AUTHOR]

Objective: It is indisputable that the functional connectivity of the brain network in juvenile myoclonic epilepsy (JME) patients is abnormal. As a mathematical extension of the traditional network model, the multilayer network can fully capture the fluctuations of brain imaging data with time, and capture subtle abnormal dynamic changes. This study assumed that the dynamic structure of JME patients is abnormal and used the multilayer network framework to analyze the change brain community structure in JME patients from the perspective of dynamic analysis. Methods: First, functional magnetic resonance imaging (fMRI) data were obtained from 35 JME patients and 34 healthy control subjects. In addition, the communities of the two groups were explored with the help of a multilayer network model and a multilayer community detection algorithm. Finally, differences were described by metrics that are specific to the multilayer network. Results: Compared with healthy controls, JME patients had a significantly lower modularity degree of the brain network. Furthermore, from the level of the functional network, the integration of the default mode network (DMN) and visual network (VN) in JME patients showed a significantly higher trend, and the flexibility of the attention network (AN) also increased significantly. At the node level, the integration of seven nodes of the DMN was significantly increased, the integration of five nodes of the VN was significantly increased, and the flexibility of three nodes of the AN was significantly increased. Moreover, through division of the core-peripheral system, we found that the left insula and left cuneus were core regions specific to the JME group, while most of the peripheral systems specific to the JME group were distributed in the prefrontal cortex and hippocampus. Finally, we found that the flexibility of the opercular part of the inferior frontal gyrus was significantly correlated with the severity of JME symptoms. Conclusion: Our findings indicate that the dynamic community structure of JME patients is indeed abnormal. These results provide a new perspective for the study of dynamic changes in communities in JME patients. [ABSTRACT FROM AUTHOR]