Effect of externality in multiplex networks on one-layer synchronization

Complex networks recently opened new ways for investigating how language use is influenced by the mental representation of word similarities. This work adopts the framework of multiplex lexical networks for investigating lexical retrieval from memory. The focus is on priming, i.e., exposure to a given stimulus facilitating or inhibiting retrieval of a given lexical item. Supported by recent findings of network distance influencing lexical retrieval, the multiplex network approach tests how the layout of hundreds of thousands of word‐word similarities in the mental lexicon can lead to priming effects on multiple combined semantic and phonological levels. Results provide quantitative evidence that phonological priming effects are encoded directly in the multiplex structure of the mental representation of words sharing phonemes either in their onsets (cohort priming) or at their ends (rhyme priming). By comparison with randomised null models, both cohort and rhyming effects are found to be emerging properties of the mental lexicon arising from its multiplexity. These priming effects are absent on individual layers but become prominent on the combined multiplex structure. The emergence of priming effects is displayed both when only semantic layers are considered, an approximated representation of the so‐called semantic memory, and when semantics is enriched with phonological similarities, an approximated representation of the lexical‐auditory nature of the mental lexicon. Multiplex lexical networks can account for connections between semantic and phonological information in the mental lexicon and hence represent a promising modelling route for shedding light on the interplay between multiple aspects of language and human cognition in synergy with experimental psycholinguistic data.

We investigate the phenomenon of first order transition (explosive\nsynchronization (ES)) in adaptively coupled phase-frustrated bi-layer multiplex\nnetwork. We consider Sakaguchi-Kuramoto (SK) dynamics over the top of multiplex\nnetworks and we establish that ES can emerge in all layers of a multiplex\nnetwork even when one of the layers may not exhibit ES in the absence of the\ninter layer connections. We clearly identify the regions of the parameter\nspace, in which the multiplexity wins over frustration parameter and network\nstructure for the emergence of ES. Based on the mean field analysis around the\ncoherent state and a perturbative approach around the incoherent state we\nanalytically derive the synchronization transition points (backward and\nforward) of all layers of multiplex network as well as its mono-layer\ncounterpart satisfying a close agreement with the numerical results.\n

Modern society is increasingly massively connected, reflecting an omnipresent tendency to organize social, economic, and technological structures in complex networks. Recently, with the advent of the so-called multiplex networks, new concepts and tools were necessary to better understand the characteristics of this type of system, as well as to analyze and quantify its performance and efficiency. The concept of diversity in multiplex networks is a striking example of this intrinsically interdisciplinary effort to better understand the nature of complex networks. In this work, we introduce the Multiplex Efficiency Index, which allows quantifying the temporal evolution of connectivity diversity, particularly when the number of layers of the multiplex network varies over time. Using data related to air passenger transportation in Brazil we investigate, through the new index, how the Brazilian air transportation network has being changing over the years due to the privatization processes of airports and mergers of airlines in Brazil. Besides that, we show how the Multiplex Efficiency Index is able to quantify fluctuations in network efficiency in a non-biased way, limiting its values between 0 and 1, taking into account the number of layers in the multiplex structure. We believe that the proposed index is of great value for the evaluation of the performance of any multiplex network, and to analyze, in a quantitative way, its temporal evolution independently of the variation in the number of layers.

Event Abstract Back to Event The multiplex interplay between phonological and semantic networks impacts word production across different types of aphasia Nichol Castro1* and Massimo Stella2 1 Georgia Institute of Technology, United States 2 University of Southampton, United Kingdom The structure of mental lexicon networks, where similar words are connected, influences several language processes in healthy (e.g., De Deyne, Navarro, & Storms, 2013; Vitevitch, Goldstein, Siew, & Castro, 2015) and clinical (e.g., Castro, Pelczarski, & Vitevitch, 2017) populations, above and beyond traditional psycholinguistic measures. Specific to aphasia, phonological networks were found to influence picture naming performance by people with Broca’s and Wernicke’s aphasia (Vitevitch & Castro, 2015). However, these previous endeavors only examined one type of word-word similarity (e.g., phonological similarity; Vitevitch, 2008). The more recent approach of multiplex lexical networks (Stella, Beckage, & Brede, 2017; Stella, Beckage, Brede, & De Domenico, 2018) allows consideration of semantic and phonological relationships among words simultaneously. We extend the previous analysis of picture naming by people with aphasia to include the multiplex lexical network defined in Stella et al. (2018), where words are connected on four layers representing free associations, synonyms, generalizations, and phonological similarities. We focused on three network measures, namely degree, PageRank, and closeness centrality, for each word on each layer and over the entire multiplex. Degree is a local network measure equal to the number of immediate links of a node. PageRank highlights meso-scale network patterns by identifying the most important nodes in the network. Closeness centrality is a global network measure quantifying how fast information can flow from one node to all others. As multiplex generalizations of these measures we used multidegree (the sum of word degrees across layers), PageRank versatility (De Domenico, Solé-Ribalta, Omodei, Gómez, & Arenas, 2015), and multiplex closeness centrality, respectively. To assess picture naming performance, we conducted logistic regression models predicting the probability of correct picture naming from traditional psycholinguistic predictors (word length, frequency, and age of acquisition), type of aphasia (Anomic, Conduction, Wernicke, Broca, and healthy controls), and each of the network measures (layer-specific and multiplex variants) using archival data. We found that for local and meso-structural measures (i.e. degree and PageRank), the layer-specific variants were more predictive of picture naming performance than the multiplex variants. Conversely, for the global measure of closeness centrality, we found the multiplex variant being twice more predictive of picture naming performance than layer-specific variants. Furthermore, the difference in production likelihood between words with high and low multiplex closeness centrality is considerably greater for people with different types of aphasia than healthy controls, suggesting that this measure is particularly sensitive to picture naming performance in this clinical population (Fig. 1). These findings indicate that picture naming by people with aphasia crucially depends on the global interplay between phonology and semantics, which is captured by the whole multiplex structure. These findings may have particular relevance for the treatment of aphasia. Potentially, selecting words with high multiplex closeness centrality for treatment will lead to greater generalization of treatment to untrained words than when selecting words with low closeness centrality, given that words with high multiplex closeness centrality are 1) already better accessed by people with aphasia and 2) well-connected within the entire multiplex lexical network through semantic and phonological connections. Figure 1 Acknowledgements Portions of this work were made possible with a Ruth L. Kirschstein National Research Service Award (NRSA) Institutional Research Training Grant from the National Institutes of Health (National Institute on Aging) 5T32AG000175 to NC. MS acknowledges support from a Doctoral Training Centre grant (EP/G03690X/1). References Castro, N., Pelczarski, K. M., & Vitevitch, M. S. (2017). Using Network Science Measures to Predict the Lexical Decision Performance of Adults Who Stutter. Journal of Speech, Language, and Hearing Research, 60(7), 1911-1918. De Deyne, S., Navarro, D. J., & Storms, G. (2013). Better explanations of lexical and semantic cognition using networks derived from continued rather than single-word associations.Behavior research methods, 45(2), 480-498. De Domenico, M., Solé-Ribalta, A., Omodei, E., Gómez, S., & Arenas, A. (2015). Ranking in interconnected multilayer networks reveals versatile nodes. Nature communications, 6, 6868. Stella, M., Beckage, N. M., & Brede, M. (2017). Multiplex lexical networks reveal patterns in early word acquisition in children.Scientific Reports, 7, 46730. Stella, M., Beckage, N. M., Brede, M., & De Domenico, M. (2018). Multiplex model of mental lexicon reveals explosive learning in humans. Scientific reports, 8(1), 2259. Vitevitch, M. S. (2008). What can graph theory tell us about word learning and lexical retrieval?. Journal of Speech, Language, and Hearing Research, 51(2), 408-422. Vitevitch, M. S., & Castro, N. (2015). Using network science in the language sciences and clinic. International journal of speech-language pathology, 17(1), 13-25. Vitevitch, M. S., Goldstein, R., Siew, C. S., & Castro, N. (2014). Using complex networks to understand the mental lexicon. In Yearbook of the Poznan Linguistic Meeting, 1(1), 119-138. Keywords: Aphasia, multiplex lexical networks, Psycholinguistics, complex networks, picture naming, clinical applications Conference: Academy of Aphasia 56th Annual Meeting, Montreal, Canada, 21 Oct - 23 Oct, 2018. Presentation Type: poster presentation Topic: not eligible for a student prize Citation: Castro N and Stella M (2019). The multiplex interplay between phonological and semantic networks impacts word production across different types of aphasia. Conference Abstract: Academy of Aphasia 56th Annual Meeting. doi: 10.3389/conf.fnhum.2018.228.00035 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 16 Apr 2018; Published Online: 22 Jan 2019. * Correspondence: Dr. Nichol Castro, Georgia Institute of Technology, Atlanta, United States, nichol.castro@psych.gatech.edu Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Nichol Castro Massimo Stella Google Nichol Castro Massimo Stella Google Scholar Nichol Castro Massimo Stella PubMed Nichol Castro Massimo Stella Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.

Inferring the connectivity of coupled oscillators and anticipating their transition to synchrony through lag-time analysis

Multiplex networks describe a large number of systems ranging from social networks to the brain. These multilayer structure encode information in their structure. This information can be extracted by measuring the correlations present in the multiplex networks structure, such as the overlap of the links in different layers. Many multiplex networks are also weighted, and the weights of the links can be strongly correlated with the structural properties of the multiplex network. For example, in multiplex network formed by the citation and collaboration networks between PRE scientists it was found that the statistical properties of citations to coauthors differ from the one of citations to noncoauthors, i.e., the weights depend on the overlap of the links. Here we present a theoretical framework for modeling multiplex weighted networks with different types of correlations between weights and overlap. To this end, we use the framework of canonical network ensembles, and the recently introduced concept of multilinks, showing that null models of a large variety of network structures can be constructed in this way. In order to provide a concrete example of how this framework apply to real data we consider a multiplex constructed from gene expression data of healthy and cancer tissues.

Universal spectral properties of multiplex networks allow us to assess the nature of the transition between disease-free and endemic phases in the SIS epidemic spreading model. In a multiplex network, depending on a coupling parameter, p, the inverse participation ratio (IPR) of the leading eigenvector of the adjacency matrix can be in two different structural regimes: (i) layer-localized and (ii) delocalized. Here we formalize the structural transition point, p*, between these two regimes, showing that there are universal properties regarding both the layer size n and the layer configurations. Namely, we show that IPR ∼ n −δ , with δ ≈ 1, and revealed an approximately linear relationship between p* and the difference between the layers’ average degrees. Furthermore, we showed that this multiplex structural transition is intrinsically connected with the nature of the SIS phase transition, allowing us to both understand and quantify the phenomenon. As these results are related to the universal properties of the leading eigenvector, we expect that our findings might be relevant to other dynamical processes in complex networks.

Multiplex community detection in complex networks using an evolutionary approach

High-centrality nodes have disproportionate influence on the behavior of a network; therefore controlling such nodes can efficiently steer the system to a desired state. Existing multiplex centrality measures typically rank nodes assuming the layers are qualitatively similar. Many real systems, however, are comprised of networks heterogeneous in nature, for example, social networks may have both agnostic and affiliative layers. Here, we use rank aggregation methods to identify intervention targets in multiplex networks when the structure, the dynamics, and our intervention goals are qualitatively different for each layer. Our approach is to rank the nodes separately in each layer considering their different function and desired outcome, and then we use Borda count or Kemeny aggregation to identify a consensus ranking—top nodes in the consensus ranking are expected to effectively balance the competing goals simultaneously among all layers. To demonstrate the effectiveness of consensus ranking, we apply our method to a degree-based node removal procedure such that we aim to destroy the largest component in some layers, while maintaining large-scale connectivity in others. For any multi-objective intervention, optimal targets only exist in the Pareto-sense; we, therefore, use a weighted generalization of consensus ranking to investigate the trade-off between the competing objectives. We use a collection of model and real networks to systematically investigate how this trade-off is affected by multiplex network structure. We use the copula representation of the multiplex centrality distributions to generate model multiplex networks with given rank correlations. This allows us to separately manipulate the marginal centrality distribution of each layer and the interdependence between the layers, and to investigate the role of the two using both analytical and numerical methods.

Who influences whom and how in river-basin governance?A participatory stakeholder and social network analysis in Zayandeh-Rud basin, Iran

In many complex systems, the interactions between objects span multiple aspects. Multiplex networks are accurate paradigms to model such systems, where each edge is associated with a type. A key graph mining primitive is extracting dense subgraphs, and this has led to interesting notions such as k-cores, known as building blocks of complex networks. Despite recent attempts to extend the notion of core to multiplex networks, existing studies suffer from a subset of the following limitations: They (1) force all nodes to exhibit their high degree in the same set of relation types while in multiplex networks some connection types can be noisy for some nodes, (2) either require high computational cost or miss the complex information of multiplex networks, and (3) assume the same importance for all relation types. We introduce Score, a novel and unifying family of dense structures in multiplex networks that uses a function S(.) to summarize the degree vector of each node. We then discuss how one can choose a proper S(.) from the data. To demonstrate the usefulness of Scores, we focus on finding the densest subgraph as well as modeling user engagement in multiplex networks. We present a new density measure in multiplex networks and discuss its advantages over existing density measures. We show that the problem of finding the densest subgraph in multiplex networks is NP-hard and design an efficient approximation algorithm based on Scores. Finally, we present a new mathematical model of user engagement in the presence of different relation types. Our experiments shows the efficiency and effectiveness of our algorithms and supports the proposed mathematical model of user engagement.

Multiplex network community detection algorithm based on motif awareness

Explosive synchronization transition in a ring of coupled oscillators

Multiplex networks are representations of multilayer interconnected complex networks where the nodes are the same at every layer. They turn out to be good abstractions of the intricate connectivity of multimodal transportation networks, among other types of complex systems. One of the most important critical phenomena arising in such networks is the emergence of congestion in transportation flows. Here, we prove analytically that the structure of multiplex networks can induce congestion for flows that otherwise would be decongested if the individual layers were not interconnected. We provide explicit equations for the onset of congestion and approximations that allow us to compute this onset from individual descriptors of the individual layers. The observed cooperative phenomenon is reminiscent of Braess' paradox in which adding extra capacity to a network when the moving entities selfishly choose their route can in some cases reduce overall performance. Similarly, in the multiplex structure, the efficiency in transportation can unbalance the transportation loads resulting in unexpected congestion.

This chapter presents the problem of link prediction in complex networks. It provides general description, formal definition of the problem and applications. It gives a state-of-art of various existing link prediction approaches concentrating more on topological approaches. It presents the main challenges of link prediction task in real networks. There is description of our new link prediction approach based on supervised rank aggregation and our attempts to deal with two of the challenges to improve the prediction results. One approach is to extend the set of attributes describing an example (pair of nodes) calculated in a multiplex network that includes the target network. Multiplex networks have a layered structure, each layer having different kinds of links between same sets of nodes. The second way is to use community information for sampling of examples to deal with the problem of class imbalance. All experiments have been conducted on real networks extracted from well-known DBLP bibliographic database.