Are your students safe to learn? The role of lecturer’s authentic leadership in the creation of psychologically safe environments and their impact on academic performance:The role of teacher's authentic leadership on the creation of psychologically safe environments and their impact on academic performance

As the role of students and lecturers in higher education changes, several questions emerge about the role of each of them on students? academic performance. This includes questions regarding the impact of the relationships between students, lecturer?s characteristics and the social environment on students? performance. To address these questions, this article reports a study of the impact of lecturer authentic leadership, psychological safety and network density on academic performance. It explores the relationship between network density, psychological safety and lecturer authentic leadership. A questionnaire was distributed to undergraduate students. A positive impact of lecturer authentic leadership and psychological safety on academic performance was found. Students from high-density groups tended to show better academic performance, higher psychological safety and tended to see their lecturers as being more authentic. A reflection on the role of the lecturer in higher education settings is presented. It also presents some recommendations on how student academic performance can be improved by the adoption of specific behaviours by their lecturer

Multitask Learning on Graph Neural Networks: Learning Multiple Graph Centrality Measures with a Unified Network

The application of deep learning to symbolic domains remains an active research endeavour. Graph neural networks (GNN), consisting of trained neural modules which can be arranged in different topologies at run time, are sound alternatives to tackle relational problems which lend themselves to graph representations. In this paper, we show that GNNs are capable of multitask learning, which can be naturally enforced by training the model to refine a single set of multidimensional embeddings $\in \mathbb{R}^d$ and decode them into multiple outputs by connecting MLPs at the end of the pipeline. We demonstrate the multitask learning capability of the model in the relevant relational problem of estimating network centrality measures, focusing primarily on producing rankings based on these measures, i.e. is vertex $v_1$ more central than vertex $v_2$ given centrality $c$?. We then show that a GNN can be trained to develop a \emph{lingua franca} of vertex embeddings from which all relevant information about any of the trained centrality measures can be decoded. The proposed model achieves $89\%$ accuracy on a test dataset of random instances with up to 128 vertices and is shown to generalise to larger problem sizes. The model is also shown to obtain reasonable accuracy on a dataset of real world instances with up to 4k vertices, vastly surpassing the sizes of the largest instances with which the model was trained ($n=128$). Finally, we believe that our contributions attest to the potential of GNNs in symbolic domains in general and in relational learning in particular.Comment: Published at ICANN2019. 10 pages, 3 Figure

Omnivorousness in sport: The importance of social capital and networks

There has been for some time a significant and growing body of research around the relationship between sport and social capital. Similarly, within sociology there has been a corpus of work that has acknowledged the emergence of the omnivore–univore relationship. Surprisingly, relatively few studies examining sport and social capital have taken the omnivore–univore framework as a basis for understanding the relationship between sport and social capital. This gap in the sociology of sport literature and knowledge is rectified by this study that takes not Putnam, Coleman or Bourdieu, but Lin’s social network approach to social capital. The implications of this article are that researchers investigating sport and social capital need to understand more about how social networks and places for sport work to create social capital and, in particular, influence participating in sporting activities. The results indicate that social networks both facilitate and constrain sports participation; whilst family and friendship networks are central in active lifestyles, those who are less active have limited networks

Communities as Well Separated Subgraphs With Cohesive Cores: Identification of Core-Periphery Structures in Link Communities

Communities in networks are commonly considered as highly cohesive subgraphs which are well separated from the rest of the network. However, cohesion and separation often cannot be maximized at the same time, which is why a compromise is sought by some methods. When a compromise is not suitable for the problem to be solved it might be advantageous to separate the two criteria. In this paper, we explore such an approach by defining communities as well separated subgraphs which can have one or more cohesive cores surrounded by peripheries. We apply this idea to link communities and present an algorithm for constructing hierarchical core-periphery structures in link communities and first test results.Comment: 12 pages, 2 figures, submitted version of a paper accepted for the 7th International Conference on Complex Networks and Their Applications, December 11-13, 2018, Cambridge, UK; revised version at http://141.20.126.227/~qm/papers

The impact of partially missing communities~on the reliability of centrality measures

Network data is usually not error-free, and the absence of some nodes is a very common type of measurement error. Studies have shown that the reliability of centrality measures is severely affected by missing nodes. This paper investigates the reliability of centrality measures when missing nodes are likely to belong to the same community. We study the behavior of five commonly used centrality measures in uniform and scale-free networks in various error scenarios. We find that centrality measures are generally more reliable when missing nodes are likely to belong to the same community than in cases in which nodes are missing uniformly at random. In scale-free networks, the betweenness centrality becomes, however, less reliable when missing nodes are more likely to belong to the same community. Moreover, centrality measures in scale-free networks are more reliable in networks with stronger community structure. In contrast, we do not observe this effect for uniform networks. Our observations suggest that the impact of missing nodes on the reliability of centrality measures might not be as severe as the literature suggests

Comparative Network Analysis of Preterm vs. Full-Term Infant-Mother Interactions

Several studies have reported that interactions of mothers with preterm infants show differential characteristics compared to that of mothers with full-term infants. Interaction of preterm dyads is often reported as less harmonious. However, observations and explanations concerning the underlying mechanisms are inconsistent. In this work 30 preterm and 42 full-term mother-infant dyads were observed at one year of age. Free play interactions were videotaped and coded using a micro-analytic coding system. The video records were coded at one second resolution and studied by a novel approach using network analysis tools. The advantage of our approach is that it reveals the patterns of behavioral transitions in the interactions. We found that the most frequent behavioral transitions are the same in the two groups. However, we have identified several high and lower frequency transitions which occur significantly more often in the preterm or full-term group. Our analysis also suggests that the variability of behavioral transitions is significantly higher in the preterm group. This higher variability is mostly resulted from the diversity of transitions involving non-harmonious behaviors. We have identified a maladaptive pattern in the maternal behavior in the preterm group, involving intrusiveness and disengagement. Application of the approach reported in this paper to longitudinal data could elucidate whether these maladaptive maternal behavioral changes place the infant at risk for later emotional, cognitive and behavioral disturbance

Conversations about the elections on Twitter: Towards a structural understanding of Twitter’s relation with the political and the media field

This study uses network analysis to examine Twitter’s level of autonomy from external influences, being the political and the media field. The conceptual framework builds upon Bourdieu’s field theory, appropriated on social media as mediated social spaces. The study investigates conversation patterns on Twitter between political, media and citizen agents during election times in Belgium. Through the comparison of conversational practices with the positions users hold as political, media or citizen agents, we understand how the former is related to the latter. The analysis of conversation patterns (based on replies and mentions) shows a decentralized and loosely knit network, in which primarily citizen agents are present. Nonetheless, the prominence of citizens in the debate, mentions or replies to political and media agents are significantly higher, placing them more centrally in the network. In addition, politicians and media actors are closely connected within the network, and reciprocal communication of these established agents is significantly lower compared to citizen agents. We understand different aspects of autonomy related to the presence, positions and practices of the agents on Twitter and their relative positions as politicians, media or citizens. To conclude, we discuss the promises of Bourdieu’s relational sociology and the limitations of our study. The approach proposed here is an attempt to integrate existing work and evolve towards a systematic understanding of the interrelations between political, media and citizen agents in a networked media environment

@THEVIEWER: Analyzing the offline and online impact of a dedicated conversation manager in the newsroom of a public broadcaster

This study is built around the appointment of a dedicated “conversation manager” at the Flemish public broadcaster VRT. We focus on (1) the impact of the conversation manager on Twitter activity of the viewers and (2) the impact of the tweeting audience in the newsroom. Our framework combines journalistic as well as social media logics in Bourdieu’s field framework, for which we combine Twitter data and newsroom inquiry. The network analysis of Twitter activity shows the impact of the conversation manager, although his activities are primarily guided by traditional journalistic values. In turn, the tweeting audience impacts newsroom practices, predominantly as an indicator of audience appreciation. To conclude, social media data further complicate the definition and understanding of “the public.

Hierarchy measure for complex networks

Nature, technology and society are full of complexity arising from the intricate web of the interactions among the units of the related systems (e.g., proteins, computers, people). Consequently, one of the most successful recent approaches to capturing the fundamental features of the structure and dynamics of complex systems has been the investigation of the networks associated with the above units (nodes) together with their relations (edges). Most complex systems have an inherently hierarchical organization and, correspondingly, the networks behind them also exhibit hierarchical features. Indeed, several papers have been devoted to describing this essential aspect of networks, however, without resulting in a widely accepted, converging concept concerning the quantitative characterization of the level of their hierarchy. Here we develop an approach and propose a quantity (measure) which is simple enough to be widely applicable, reveals a number of universal features of the organization of real-world networks and, as we demonstrate, is capable of capturing the essential features of the structure and the degree of hierarchy in a complex network. The measure we introduce is based on a generalization of the m-reach centrality, which we first extend to directed/partially directed graphs. Then, we define the global reaching centrality (GRC), which is the difference between the maximum and the average value of the generalized reach centralities over the network. We investigate the behavior of the GRC considering both a synthetic model with an adjustable level of hierarchy and real networks. Results for real networks show that our hierarchy measure is related to the controllability of the given system. We also propose a visualization procedure for large complex networks that can be used to obtain an overall qualitative picture about the nature of their hierarchical structure.Comment: 29 pages, 9 figures, 4 table