Multiplex Network Analysis Research Articles

Modelling the bilingual lexicon as a multiplex phonological network.

Phonological word form networks of the mental lexicon are of psycholinguistic relevance, offering insights into the efficiency of lexical access. While much research has concentrated on first languages, there is growing evidence suggesting that phonological networks of second languages are equally significant for lexical processes. Bilingual language processing is proposed to involve the integration of first and second languages, with lexical activation spreading between similar word forms in both languages. Multiplex networks provide a framework to combine different phonological networks, allowing for the analysis of the integrated lexical system's behaviour during lexical processing. In the context of the present study, which focusses on German learners of English as a second language, a multiplex network analysis was constructed to model the interactive complexity of the bilingual mental lexicon. The study tested cross-linguistic effects in a word recognition task using English stimuli. Results revealed that during lexical processing in their second language English, German speakers also activate phonological neighbours from German. In addition, the bilinguals are attuned to the interconnectedness (i.e., clustering) of the German and English neighbours with one another in the phonological neighbourhood of the English target words. These findings can contribute to the ongoing debate on the degree of integration in the bilingual mental lexicon and shed light on the role that phonological networks can play in modelling bilingual lexical processing. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Characteristics of Air Traffic Flow in Terminal Airspace: A Multiplex Recurrence Network Analysis

Characteristics of Air Traffic Flow in Terminal Airspace: A Multiplex Recurrence Network Analysis

Understanding the developmental transition between parallel and social play during preschool: A multiplex social network analysis.

Understanding the developmental transition between parallel and social play during preschool: A multiplex social network analysis.

Supplemental Material for Understanding the Developmental Transition Between Parallel and Social Play During Preschool: A Multiplex Social Network Analysis

Supplemental Material for Understanding the Developmental Transition Between Parallel and Social Play During Preschool: A Multiplex Social Network Analysis

Multilayer analysis of energy networks

Modern interconnected and vulnerable energy infrastructures highlight the critical need for resilience and robustness to maintain the functioning of modern societies amidst growing environmental and operational challenges. This study introduces both multilayer and multiplex network analysis of Belgium’s electricity and gas systems, addressing the complex interdependence inherent in such infrastructure. The resilience is evaluated through random and targeted disruptions, while the robustness is assessed by employing the Molloy–Reed criteria for structural integrity and setting minimum operational thresholds for continuity. Multiplex layer similarities are assessed and subnetworks are identified. The coverage evolution metric quantifies the network’s adaptability under various disruption scenarios. Belgium’s energy infrastructure is used as a case study to uncover critical nodes from different centrality perspectives whose stability is essential for overall network resilience and identify vulnerabilities to disruptions, whether random or targeted. Our insights into the structural robustness, cluster cohesion, and Navigability of energy networks pave the way for more informed decisions in energy infrastructure planning and complex systems management, offering valuable lessons that can be applied globally.

Dynamic Analysis of Multiplex Networks With Hybrid Maintenance Strategies

Dynamic Analysis of Multiplex Networks With Hybrid Maintenance Strategies

Building Political Hashtag Communities: A Multiplex Network Analysis of U.S. Senators on Twitter during the 2022 Midterm Elections

This study examines how U.S. senators strategically used hashtags to create political communities on Twitter during the 2022 Midterm Elections. We propose a way to model topic-based implicit interactions among Twitter users and introduce the concept of Building Political Hashtag Communities (BPHC). Using multiplex network analysis, we provide a comprehensive view of elites’ behavior. Through AI-driven topic modeling on real-world data, we observe that, at a general level, Democrats heavily rely on BPHC. Yet, when disaggregating the network across layers, this trend does not uniformly persist. Specifically, while Republicans engage more intensively in BPHC discussions related to immigration, Democrats heavily rely on BPHC in topics related to identity and women. However, only a select group of Democratic actors engage in BPHC for topics on labor and the environment—domains where Republicans scarcely, if at all, participate in BPHC efforts. This research contributes to the understanding of digital political communication, offering new insights into echo chamber dynamics and the role of politicians in polarization.

Multiplex Social Network Analysis to Understand the Social Engagement of Patients in Online Health Communities

Social network analysis has been widely used in various fields including online health communities. However, it is still a challenge to understand how patients’ individual characteristics and online behaviors impact the formation of online health social networks. Furthermore, patients discuss various health topics and form multiplex social networks covering different aspects of their illnesses, including symptoms, treatment experiences, resource sharing, emotional expression, and new friendships. Further research is needed to investigate whether the factors influencing the formation of these topic-based networks are different and explore potential interconnections between various types of social relationships in these networks. To address these issues, this study applied exponential random graph models to characterize multiplex health social networks and conducted empirical research in a Chinese online mental health community. An integrated social network and five separate health-related topic-specific networks were constructed, each with 773 users as network nodes. The empirical findings revealed that patients’ demographic attributes (e.g., age, gender) and online behavioral features (e.g., emotional expression, online influence, participation duration) have significant impacts on the formation of online health social networks, and these patient characteristics have significantly different effects on various types of social relationships within multiplex networks. Additionally, significant cross-network effects, including entrainment and exchange effects, were found among multiple health topic-specific networks, indicating strong interdependencies between them. This research provides theoretical contributions to social network analysis and practical insights for the development of online healthcare social networks.

LPGRI: A Global Relevance-Based Link Prediction Approach for Multiplex Networks

The individuals of real-world networks participate in various types of connections, each forming a layer in multiplex networks. Link prediction is an important problem in multiplex network analysis owing to its wide range of practical applications, such as mining drug targets, recommending friends in social networks, and exploring network evolution mechanisms. A key issue of link prediction within multiplex networks is how to estimate the likelihood of potential links in the predicted layer by leveraging both interlayer and intralayer information. Several studies have shown that incorporating interlayer topological information can improve the performance of link prediction in the predicted layer. Therefore, this paper proposes the Link Prediction based on Global Relevance of Interlayer (LPGRI) method to estimate the likelihood of potential links in the predicted layer of multiplex networks, which comprehensively utilizes both types of information. In the LPGRI method, the contribution of interlayer information is determined using the global relevance (GR) index between layers. Experimental studies on six real multiplex networks demonstrate the competitive performance of our method.

Spreading of cross-market volatility information: Evidence from multiplex network analysis of volatility spillovers

We propose a multiplex network including layers of realized variance (RV), implied variance (IV), and variance risk premium (VRP) to investigate the volatility information transmission among 18 global financial markets through the spillover index in the variance decomposition framework. We innovatively study the connectedness of global volatility from aspects of RV, IV, and VRP to portray the interrelation between market volatility and investor sentiments. Upon inspecting and comparing static and dynamic topological characteristics on each layer, we find that information transmission mechanism is different among the three layers. Spillover effects are observed to be the strongest on the IV layer in the long term, while the most evident spillover shocks are observed on the VRP layer in the short term. At the market level, European and the US markets are the main spillover emitters, while Asian markets primarily are spillover recipients. Our study provides valuable insights on global risk management and prevention for portfolio managers and policy makers.

Structural-functional connectivity bandwidth predicts processing speed in mild traumatic brain Injury: A multiplex network analysis

An emerging body of work has revealed alterations in structural (SC) and functional (FC) brain connectivity following mild TBI (mTBI), with mixed findings. However, these studies seldom integrate complimentary neuroimaging modalities within a unified framework. Multilayer network analysis is an emerging technique to uncover how white matter organization enables functional communication. Using our novel graph metric (SC-FC Bandwidth), we quantified the information capacity of synchronous brain regions in 53 mild TBI patients (46 females; age mean = 40.2 years (y), σ = 16.7 (y), range: 18–79 (y). Diffusion MRI and resting state fMRI were administered at the acute and chronic post-injury intervals. Moreover, participants completed a cognitive task to measure processing speed (30 Seconds and Counting Task; 30-SACT). Processing speed was significantly increased at the chronic, relative to the acute post-injury intervals (p = <0.001). Nonlinear principal components of direct (t = -1.84, p = 0.06) and indirect SC-FC Bandwidth (t = 3.86, p = <0.001) predicted processing speed with a moderate effect size (R2 = 0.43, p < 0.001), while controlling for age. A subnetwork of interhemispheric edges with increased SC-FC Bandwidth was identified at the chronic, relative to the acute mTBI post-injury interval (pFDR = 0.05). Increased interhemispheric SC-FC Bandwidth of this network corresponded with improved processing speed at the chronic post-injury interval (partial r = 0.32, p = 0.02). Our findings revealed that mild TBI results in complex reorganization of brain connectivity optimized for maximum information flow, supporting improved cognitive performance as a compensatory mechanism. Moving forward, this measurement may complement clinical assessment as an objective marker of mTBI recovery.

MULTIPLEX NETWORK APPROACH FOR FARMERS' FRIENDSHIP RELATIONSHIP

Background. In recent years, social network analysis methods have been applied to study the relationships among farmers. However, most studies have not paid attention to the different types of relationships that can be found in these networks, particularly with regard to the interactions between farmers. Hence, in this study, a multiplex network approach was used Purpose. to examine the interdependencies between farmers' relationships and the distinct relationship layers built around them were analyzed. We used data collected from a rural farming Methodology. community in Ghana and analyze our data using multiplex network analysis. The re Findings. sults showed that each layer generated by each type of relationship had a distinct structure and that most relationships were independent. Relationships among farmers for higher yields can facilitate the development of policies and strategies that encourage knowledge exchange and collaboration, funding access, and joint ventures. Appropriate management of the relationships can help maximize benets to farmers. Our Originality. theory-building study has implications for enhancing the productivity and performance of farmers.

Impact of farmers’ social relationships on productivity: a multiplex network analysis

Abstract An individual’s productivity is strongly related to work- and non-work-related interactions. Thus, the literature on farmers’ productivity often explores single-layer networks that illustrate the single categories of social relationships. In this study, we investigated farmers’ productivity using a multiplex structure underlying social interaction networks. Relational data were obtained from farmers in four different categories of social relationships. The multiplex network was analysed by applying multiplex degree centrality and layer-by-layer comparison. Also, power and role were analysed through the use of external data by determining their intra-layer correlation. The findings show that diverse types of relationships exist together and they positively affect farmers’ productivity in multiple ways and enhance their innovation capacity. Only 6 out of the 73 farmers had high-degree centrality (&amp;gt; 10), with 18–63$\%$ relevance for the six farmers in the two layers—farming advice (FA) and loans (LO) layers—that the farmers considered important to their productivity. These farmers were more likely to be productive and help improve the productivity of others linked to them. Further, 62$\%$ of the edges in the social gathering and personal advice layers were similar, whereas only 3$\%$ of those in the FA and LO layers were similar, confirming the significance of the latter layers. The influence of social structures on farmers’ productivity implies that social connections enhance farmers’ confidence. The external data further confirm that the formation of some links depends on trust and power, whereas others do not.

What Drives Economic Growth across European Countries? A Multimodal Approach

This research study examines the factors that determined economic growth measured by gross domestic product, gross value added, final consumption expenditure of households, and gross fixed capital formation across a sample of 36 European countries during the time frame Q3 2018−Q3 2021. We conducted a panel data analysis with the first-difference generalized method of moments (GMM) approach and cross-section fixed effects. Empirical results estimated with four econometric models indicated a significant robust impact of the independent variables exports, imports, foreign direct investment inflow, foreign direct investment outflow, social contributions, and wages on economic growth proxies. In addition, multiplex network analysis was used to investigate the connection architectures of the 36 countries, and it yielded statistical measurements for all layers in the multi-layered structure. Relevant policy implications of reported empirical results are also addressed.

Orientations and matrix function-based centralities in multiplex network analysis of urban public transport

We study urban public transport systems by means of multiplex networks in which stops are represented as nodes and each line is represented by a layer. We determine and visualize public transport network orientations and compare them with street network orientations of the 36 largest German as well as 18 selected major European cities. We find that German urban public transport networks are mainly oriented in a direction close to the cardinal east-west axis, which usually coincides with one of two orthogonal preferential directions of the corresponding street network. While this behavior is present in only a subset of the considered European cities it remains true that none but one considered public transport network has a distinct north-south-like preferential orientation. Furthermore, we study the applicability of the class of matrix function-based centrality measures, which has recently been generalized from single-layer networks to layer-coupled multiplex networks, to our more general urban multiplex framework. Numerical experiments based on highly efficient and scalable methods from numerical linear algebra show promising results, which are in line with previous studies. The centrality measures allow detailed insights into geometrical properties of urban systems such as the spatial distribution of major transport axes, which can not be inferred from orientation plots. We comment on advantages over existing methodology, elaborate on the comparison of different measures and weight models, and present detailed hyper-parameter studies. All results are illustrated by demonstrative graphical representations.

Random matrix analysis of multiplex networks

We investigate the spectra of adjacency matrices of multiplex networks under random matrix theory (RMT) framework. Through extensive numerical experiments, we demonstrate that upon multiplexing two random networks, the spectra of the combined multiplex network exhibit superposition of two Gaussian orthogonal ensemble (GOE)s for very small multiplexing strength followed by a smooth transition to the GOE statistics with an increase in the multiplexing strength. Interestingly, randomness in the connection architecture, introduced by random rewiring to 1D lattice, of at least one layer may govern nearest neighbor spacing distribution (NNSD) of the entire multiplex network, and in fact, can drive to a transition from the Poisson to the GOE statistics or vice versa. Notably, this transition transpires for a very small number of the random rewiring corresponding to the small-world transition. Ergo, only one layer being represented by the small-world network is enough to yield GOE statistics for the entire multiplex network. Spectra of adjacency matrices of underlying interaction networks have been contemplated to be related with dynamical behavior of the corresponding complex systems, the investigations presented here have implications in achieving better structural and dynamical control to the systems represented by multiplex networks against structural perturbation in only one of the layers.

The evolution of triangular research and innovation collaborations in the European area

• A research and innovation geographical multiplex network is created. • The temporal evolution of a multiplex systems collaboration structure is monitored. • Geographical regions with high average clustering coefficient are identified. • A growth mechanism with preference in triangular collaborations exists in research. • A growth mechanism with aversion to triangular collaborations exists in innovation. In the current study, we examine the multiplex network of patents and European Research Framework Programmes (FPs) aiming to uncover temporal variations in the formation patterns of triangles (a fully connected network between any three nodes). The multiplex network that we study consists of two layers whose nodes are the NUTS2 regions, and collaborations between scientists or inventors of different regions result to a link. We split the network temporally into 28 shorter sub-networks with a span of 6 years each, and calculate the number of triangles formed at the end of the 6-year period. Next, we shuffle the data creating 50 randomized networks for each of the 28 six-year sub-networks, in order to identify whether there is a hidden mechanism that favors a non-random behavior. Real and shuffled data are compared using a z-score, a measure of the differences of standard deviations between them. In addition, we repeat the same analysis using the clustering coefficient whose large value can signify a strong collaboration pattern for a given node. The results from the temporal analysis of real vs randomized multiplex networks, show that triangular FP collaborations tend to be favored over random ones, while in patents the case is strongly the opposite. Furthermore, results using triangles tend to be more comprehensive as opposed to those of the clustering coefficient. Finally, we identify which NUTS2 regions frequently exhibit a high clustering coefficient in either of the layers, and we present a map with these values for all regions. The results of this research can help policy making organizations understand the spatial dimension of subsidized research and patented innovation collaboration networks and perhaps to strengthen regional collaboration.

A scenario-based interval-input output model to analyze the risk of COVID-19 pandemic in port logistics

PurposePresently, Indian sectors are manifesting a higher level of interdependency and making the economy more vulnerable to human-caused and natural disasters. COVID-19 pandemic creates a devastating effect on the world economy. The Indian economy was expected to lose around ₹ 32,000 crores every day during the first 21 days of complete lockdown. This motivates to conduct the research on how the COVID-19 pandemic affects the port logistics sector and how the effects of COVID-19 on port logistics propagate to other sectors owing to its interconnectedness and affect the economy of the country.Design/methodology/approachThe purpose of the study is analyze how perturbation in one sector can affect the system of interdependent sectors and it is done with interdependency analysis. It uses Wassily Leontief’s inoperability input-output model (IIM) and interval programming (IP) to develop a framework. IP is used to address situations where assumptions are not valid because of uncertainties associated with disruptive events.FindingsThe model helps in describing how the effect of the COVID-19 pandemic in port logistics can propagate owing to the interconnectedness across other sectors. The model uses the latest five-year data available on the Organisation for Economic Co-operation and Development database. It uses metrics like inoperability and economic loss to study the consequences of COVID-19 pandemic on various sectors. This study also presents the ranking of the affected sectors based on their inoperability and economic lossResearch limitations/implicationsIn the future study, other techniques like dynamic evolution, multiplex network analysis, analytical hierarchy process, pinch analysis, stochastic evolution and pinch graph could be integrated with input-output (I-O) modelling. Integrated stochastic evolution with an I-O model allows capturing the likelihood of the events; it includes probability distributions instead of point estimates for scenario parameters. Methods like dynamic evolution and multiplex network analysis can be introduced in future work to shed lights on interdependency among the sector, which could potentially provide additional insights for transport policy formulations.Originality/valueThis study discusses the theory, methodology and application of the IIM-IP model in the domain of port logistics. The developed IIM-IP model helps decision-makers to manage risk in port logistics. Firstly, it studies how different sectors are interconnected with each other. Secondly, it helps in identifying the most vulnerable sectors based on economic loss and inoperability. Thirdly, it provides the ranking of the sectors based on their economic losses.

Learners' interaction patterns in asynchronous online discussions: An integration of the social and cognitive interactions

AbstractStudying the networked nature of social and cognitive aspects of learner interactions is the key to understanding how successful collaborative learning occurs in asynchronous online discussion forums (AODFs). Guided by network science and multiplex network analysis, this study compared the differences of network structure and properties between the social (learners as nodes and commenting on the others' contributions as edges) and cognitive (learners as nodes and explicitly quoting the others' contributions as edges) networks within an AODF. It additionally examined the differences of individual measures between these two monolayer networks and their integrated two multiplex networks. The two multiplex networks were respectively framed in superimposed and unfold approaches. Moreover, correlation analysis was conducted to examine the relationships between individual measures and learning performance across the four networks. Results showed key differences at the network and individual levels between the social and cognitive networks. The two networks had different compositions of participants, and students in the cognitive network occupied more central positions in general. Besides, certain individual measures in the multiplex networks were relatively higher and more related to learning performance than those in the monolayer networks. These results indicate that integrating multi‐aspect information of collaboration may be more conducive to unveiling learners' interaction patterns in asynchronous online discussions. Practitioner notesWhat is already known about this topic? Studying the networked nature of the social and cognitive aspects of learner interactions is the key to understanding how successful collaborative learning occurs. Social network analysis and content analysis are two commonly used methods to analyse learner interactions in asynchronous online discussions. There is a lack of research examining learners' overall interaction patterns by integrating social and cognitive interactions. What this paper adds? This study framed cognitive interactions as a networked phenomenon to unveil the way learners participate in the cognitive aspect of learner interactions. Multiplex network analysis (MNA) was introduced to integrate social and cognitive interactions to understand learners' overall interaction patterns. Integrating social and cognitive interactions led to a more closer relationship between learner interactions and learning performance. Implications for practice and/or policy? The proposed approach for framing the cognitive network and the introduced approach of MNA could be jointly employed to discern learners' overall interaction pattern in asynchronous online discussions. Course facilitators and instructors should be aware of the inconsistencies in social and cognitive interactions and then simultaneously examine them so as to acquire an accurate understanding of learners' diverse interactions. Pedagogical strategies for simultaneously facilitating social and cognitive interactions could be used to better improve learning performance.

Identifying board of director network influence for firm characteristics

We utilize multiplex network analysis to evaluate the relationship between firms’ characteristics and board of director networks. Studying a sample of 384 board members of 20 largest US energy and utility firms, 10 from each sector, the firms are differentiated by a clustering analysis on their firm-level characteristics. The firms’ board member connections are represented in a multiplex network consisting of two layers, one for the board members’ direct connections and another for their indirect connections. Four additional sub-layers are identified for each major layer of the multiplex network to represent corporate, non-profit organization, education and government/military connections between the board members. Weights assigned to each layer of the multiplex network allow combining their effect in the resulting single layered directors’ network. The weights of the multiplex directors’ network are learned by optimally relating firms’ director network characteristics with firm clusters’ characteristics. We observe that director network displays significant connectivity at all multiplex network layers and firms belonging to the same cluster display similar director network characteristics, specifically enhanced by appropriate weighting of director network layers. The optimal value of network sub-layer weights indicate that, while corporate board member connections play the most significant role, the non-profit and military & government connections are also important, where non-profit connections lead to inferior firm outcomes.