Multilayer Network of Language: a Unified Framework for Structural Analysis of Linguistic Subsystems

Recently, the focus of complex networks research has shifted from the analysis of isolated properties of a system toward a more realistic modeling of multiple phenomena - multilayer networks. Motivated by the prosperity of multilayer approach in social, transport or trade systems, we propose the introduction of multilayer networks for language. The multilayer network of language is a unified framework for modeling linguistic subsystems and their structural properties enabling the exploration of their mutual interactions. Various aspects of natural language systems can be represented as complex networks, whose vertices depict linguistic units, while links model their relations. The multilayer network of language is defined by three aspects: the network construction principle, the linguistic subsystem and the language of interest. More precisely, we construct a word-level (syntax, co-occurrence and its shuffled counterpart) and a subword level (syllables and graphemes) network layers, from five variations of original text (in the modeled language). The obtained results suggest that there are substantial differences between the networks structures of different language subsystems, which are hidden during the exploration of an isolated layer. The word-level layers share structural properties regardless of the language (e.g. Croatian or English), while the syllabic subword level expresses more language dependent structural properties. The preserved weighted overlap quantifies the similarity of word-level layers in weighted and directed networks. Moreover, the analysis of motifs reveals a close topological structure of the syntactic and syllabic layers for both languages. The findings corroborate that the multilayer network framework is a powerful, consistent and systematic approach to model several linguistic subsystems simultaneously and hence to provide a more unified view on language

Impact of network characteristics on network reconstruction

When a network is inferred from data, two types of errors can occur: false positive and false negative conclusions about the presence of links. We focus on the influence of local network characteristics on the probability $\alpha$ - of type I false positive conclusions, and on the probability $\beta$ - of type II false negative conclusions, in the case of networks of coupled oscillators. We demonstrate that false conclusion probabilities are influenced by local connectivity measures such as the shortest path length and the detour degree, which can also be estimated from the inferred network when the true underlying network is not known a priory. These measures can then be used for quantification of the confidence level of link conclusions, and for improving the network reconstruction via advanced concepts of link thresholding

Iterative procedure for network inference

Acknowledgements This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 642563. The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.Peer reviewedPostprin

Humans best judge how much to cooperate when facing hard problems in large groups

We report the results of a game-theoretic experiment with human players who solve the problems of increasing complexity by cooperating in groups of increasing size. Our experimental environment is set up to make it complicated for players to use rational calculation for making the cooperative decisions. This environment is directly translated into a computer simulation, from which we extract the collaboration strategy that leads to the maximal attainable score. Based on this, we measure the error that players make when estimating the benefits of collaboration, and find that humans massively underestimate these benefits when facing easy problems or working alone or in small groups. In contrast, when confronting hard problems or collaborating in large groups, humans accurately judge the best level of collaboration and easily achieve the maximal score. Our findings are independent on groups' composition and players' personal traits. We interpret them as varying degrees of usefulness of social heuristics, which seems to depend on the size of the involved group and the complexity of the situation.Comment: 18 pages, 1 figure. In press for Scientific Report

The emergent integrated network structure of scientific research

The practice of scientific research is often thought of as individuals and small teams striving for disciplinary advances. Yet as a whole, this endeavor more closely resembles a complex system of natural computation, in which information is obtained, generated, and disseminated more effectively than would be possible by individuals acting in isolation. Currently, the structure of this integrated and innovative landscape of scientific ideas is not well understood. Here we use tools from network science to map the landscape of interconnected research topics covered in the multidisciplinary journal PNAS since 2000. We construct networks in which nodes represent topics of study and edges give the degree to which topics occur in the same papers. The network displays small-world architecture, with dense connectivity within scientific clusters and sparse connectivity between clusters. Notably, clusters tend not to align with assigned article classifications, but instead contain topics from various disciplines. Using a temporal graph, we find that small-worldness has increased over time, suggesting growing efficiency and integration of ideas. Finally, we define a novel measure of interdisciplinarity, which is positively associated with PNAS's impact factor. Broadly, this work suggests that complex and dynamic patterns of knowledge emerge from scientific research, and that structures reflecting intellectual integration may be beneficial for obtaining scientific insight

Reconstructing dynamics of complex systems from noisy time series with hidden variables

Reconstructing the equation of motion and thus the network topology of a system from time series is a very important problem. Although many powerful methods have been developed, it remains a great challenge to deal with systems in high dimensions with partial knowledge of the states. In this paper, we propose a new framework based on a well-designed cost functional, the minimization of which transforms the determination of both the unknown parameters and the unknown state evolution into parameter learning. This method can be conveniently used to reconstruct structures and dynamics of complex networks, even in the presence of noisy disturbances or for intricate parameter dependence. As a demonstration, we successfully apply it to the reconstruction of different dynamics on complex networks such as coupled Lorenz oscillators, neuronal networks, phase oscillators and gene regulation, from only a partial measurement of the node behavior. The simplicity and efficiency of the new framework makes it a powerful alternative to recover system dynamics even in high dimensions, which expects diverse applications in real-world reconstruction.Comment: 23 pages,23 figure

Conformist-contrarian interactions and amplitude dependence in the Kuramoto model

We derive exact formulas for the frequency of synchronized oscillations in Kuramoto models with conformist–contrarian interactions, and determine necessary conditions for synchronization to occur. Numerical computations show that for certain parameters repulsive nodes behave as conformists, and that in other cases attractive nodes can display frustration, being neither conformist nor contrarian. The signs of repulsive couplings can be placed equivalently outside the sum, as proposed in Hong and Strogatz (2011 Phys. Rev. Lett. 106 054102), or inside the sum as in Hong and Strogatz (2012 Phys. Rev. E 85 056210), but the two models have different characteristics for small magnitudes of the coupling constants. In the latter case we show that the distributed coupling constants can be viewed as oscillator amplitudes which are constant in time, with the property that oscillators of small amplitude couple only weakly to connected nodes. Such models provide a means of investigating the effect of amplitude variations on synchronization properties.M A Loh

AN EXPLORATORY ANALYSIS OF HIGHER EDUCATION BUSINESS FACULTY

The purpose of this study was to contribute to the body of research on the factors which influence job satisfaction and retention of business faculty in higher education. This study utilized the National Study of Postsecondary Faculty NSOPF: 2004 which is a nationally representative sample of higher education faculty and was sponsored by the NCES and the U.S. Department of Education. It is important to investigate the characteristics which predict job satisfaction of business faculty as their expertise and field-based research affect the global economy. The study looked at the effect of several factors on the job satisfaction of business faculty: age, race, satisfaction with authority to make decisions, satisfaction with workload, demographics, highest degree, satisfaction with technology-based activities, satisfaction with salary, satisfaction with benefits, satisfaction with institutional support for teaching improvement and satisfaction with scholarly activities. Maslow’s needs hierarchy theory was one of the first theories to examine important contributors to job satisfaction. Professor’s job satisfaction appears to be the most widely studied factor in relation to professor self-efficacy. Without self-efficacy, people will not try hard to achieve anything because they will have the perception that their efforts will be pointless. Professor self-efficacy is a professor’s perceived capability to impart knowledge and to influence the behavior of students. Herzberg’s Motivational-Hygiene Theory is examined to determine the factors which influence job satisfaction. The paper examined if the characteristics of the structure of the population of business faculty predict job satisfaction. The findings were that faculty were equally satisfied based on gender and race. With respect to the factors which best predict levels of job satisfaction among business faculty, the findings were that faculty were satisfied with workload, technology-based activities, and scholarly activities. The results of the analysis indicated that business faculty comprise a distinct group among higher education faculty and possess a unique set of characteristics in terms of their demographic educational background employment status workload instructional practices and research activities. The researcher noted several features of the definition which make job satisfaction a fundamentally complex social attitude and there is confusion and debate about the applicability of Herzberg’s theor