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Interplay between topology and edge weights in real-world graphs: concepts, patterns, and an algorithm
What are the relations between the edge weights and the topology in real-world graphs? Given only the topology of a graph, how can we assign realistic weights to its edges based on the relations? Several trials have been done for edge-weight prediction where some unknown edge weights are predicted with most edge weights known. There are also existing works on generating both topology and edge weights of weighted graphs. Differently, we are interested in generating edge weights that are realistic in a macroscopic scope, merely from the topology, which is unexplored and challenging. To this end, we explore and exploit the patterns involving edge weights and topology in real-world graphs. Specifically, we divide each graph into layers where each layer consists of the edges with weights at least a threshold. We observe consistent and surprising patterns appearing in multiple layers: the similarity between being adjacent and having high weights, and the nearly-linear growth of the fraction of edges having high weights with the number of common neighbors. We also observe a power-law pattern that connects the layers. Based on the observations, we propose PEAR, an algorithm assigning realistic edge weights to a given topology. The algorithm relies on only two parameters, preserves all the observed patterns, and produces more realistic weights than the baseline methods with more parameters.
Journal Article
Assessing the Latent Structure of the Multidimensional Test Anxiety Scale in a University Sample: An Exploratory Graph Analysis Study
The Multidimensional Test Anxiety Scale is a self-report instrument designed to assess the cognitive, affective, and physiological manifestations of test anxiety. To date, researchers have provided evidence of the instrument’s psychometric properties when administered in K–12 and university samples in institutions in England and Wales. Thus, the current study was designed to investigate the latent structure of the instrument within a sample of United States university students using a novel statistical technique. University students (N = 412,
X
¯
Age = 26.95, SDAge = 8.76, 84.34% female, 65.06% Caucasian) completed the Multidimensional Test Anxiety Scale, Reactions to Tests – Reduced, and Well-being Profile-Short Form, and a demographics form. Using bootstrapped exploratory graph analysis, we determined a three-dimensional solution consisting of worry, cognitive interference, and physiological indicators dimensions was optimal. Further, reliability analyses revealed internal consistency estimates for the extracted factors were within acceptable limits. Finally, correlational analyses provided evidence of the concurrent validity of the Multidimensional Test Anxiety Scale.
Journal Article
Networks of networks in biology : concepts, tools and applications
Biological systems are extremely complex and have emergent properties that cannot be explained or even predicted by studying their individual parts in isolation. The reductionist approach, although successful in the early days of molecular biology, underestimates this complexity. As the amount of available data grows, so it will become increasingly important to be able to analyse and integrate these large data sets. This book introduces novel approaches and solutions to the Big Data problem in biomedicine, and presents new techniques in the field of graph theory for handling and processing multi-type large data sets. By discussing cutting-edge problems and techniques, researchers from a wide range of fields will be able to gain insights for exploiting big heterogonous data in the life sciences through the concept of 'network of networks'.
BOOK
SCANPY: large-scale single-cell gene expression data analysis
Scanpy
is a scalable toolkit for analyzing single-cell gene expression data. It includes methods for preprocessing, visualization, clustering, pseudotime and trajectory inference, differential expression testing, and simulation of gene regulatory networks. Its Python-based implementation efficiently deals with data sets of more than one million cells (
https://github.com/theislab/Scanpy
). Along with
Scanpy
, we present
AnnData
, a generic class for handling annotated data matrices (
https://github.com/theislab/anndata
).
Journal Article
High Resolution Postmortem MRI Discovers Developing Structural Connectivity in the Human Ascending Arousal Network
Human arousal is essential to survival and mediated by the ascending arousal network (AAN) and its connections. It spans from the brainstem to the diencephalon, basal forebrain, and cerebral cortex. Despite advances in mapping the AAN in adults, it is unexplored in fetal and early infant life, especially with high‐resolution magnetic resonance imaging techniques. In this study, we conducted—for the first time—high‐resolution ex vivo diffusion MRI‐based analysis of the AAN in seven fetal, infant, and adult brains, incorporating probabilistic tractography and quantifying connectivity using graph theory. We observed that AAN structural connectivity becomes increasingly integrated during development, progressively reaching rostrally during the first postconceptional year. We quantitatively identified the dorsal raphe (DR) nucleus and ventral tegmental area (VTA) as AAN connectivity hubs already in the fetus persisting into adulthood. The DR appears to form a local hub of short‐range connectivities, while the VTA evolves as a long‐range global hub. The identified connectivity maps advance our understanding of AAN architecture changes due to normative human brain development, as well as disorders of arousal, such as coma and sudden infant death syndrome. We used high‐resolution ex vivo diffusion MRI and graph theory to analyze ascending arousal network development in fetal to adult brains, revealing increasing rostral integration postnatally and identifying the dorsal raphe nucleus and ventral tegmental area as persistent connectivity hubs from fetal stages through adulthood.
Journal Article
The dynamic functional connectome: State-of-the-art and perspectives
Resting-state functional magnetic resonance imaging (fMRI) has highlighted the rich structure of brain activity in absence of a task or stimulus. A great effort has been dedicated in the last two decades to investigate functional connectivity (FC), i.e. the functional interplay between different regions of the brain, which was for a long time assumed to have stationary nature. Only recently was the dynamic behaviour of FC revealed, showing that on top of correlational patterns of spontaneous fMRI signal fluctuations, connectivity between different brain regions exhibits meaningful variations within a typical resting-state fMRI experiment. As a consequence, a considerable amount of work has been directed to assessing and characterising dynamic FC (dFC), and several different approaches were explored to identify relevant FC fluctuations. At the same time, several questions were raised about the nature of dFC, which would be of interest only if brought back to a neural origin. In support of this, correlations with electroencephalography (EEG) recordings, demographic and behavioural data were established, and various clinical applications were explored, where the potential of dFC could be preliminarily demonstrated. In this review, we aim to provide a comprehensive description of the dFC approaches proposed so far, and point at the directions that we see as most promising for the future developments of the field. Advantages and pitfalls of dFC analyses are addressed, helping the readers to orient themselves through the complex web of available methodologies and tools.
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•A great effort has been spent on dynamic functional connectivity characterization.•We exhaustively describe existing approaches, their advantages and pitfalls.•We discuss future analytical directions: frame-wise analysis and temporal modeling.•Frame-wise analysis extracts the meaningful functional networks from events.•Temporal modeling parameterizes brain dynamics in flexible and realistic manners.
Journal Article
Revolutionizing Education: Harnessing Graph Machine Learning for Enhanced Problem-Solving in Environmental Science and Pollution Technology
Amidst the shifting tides of the educational landscape, this research article embarks on a transformative journey delving into the fusion of theoretical principles and pragmatic implementations within the realm of Graph Machine Learning (GML), particularly accentuated within the sphere of nature, environment, and pollution technology. GML emerges as a potent and indispensable tool, adeptly leveraging the intrinsic interconnectedness embedded within environmental datasets. Its application extends far beyond mere analysis towards the profound ability to forecast ecological patterns, prescribe sustainable interventions, and tailor pollution mitigation strategies with precision and efficacy. This article does not merely scratch the surface of GML’s applications but dives deep into its tangible implementations, unraveling its potential to revolutionize environmental science and pollution technology. It endeavors to bridge the gap between theory and practice, weaving together relevant ecological theories and empirical evidence that underpin the theoretical foundations supporting GML’s practical utility in environmental domains. By synthesizing theoretical insights with real-world applications, this research elucidates the profound transformative potential of GML, paving the way for proactive and data-driven approaches toward addressing pressing environmental challenges. In essence, this harmonization of theory and application catalyzes advancing the adoption of GML in environmental science and pollution technology. It not only illuminates the path towards sustainable practices but also lays the groundwork for fostering a holistic understanding of our ecosystem. Through this integration, GML emerges as a beacon guiding us toward a future where environmental stewardship is informed by data-driven insights, leading to more effective and sustainable solutions for the benefit of our planet and future generations.
Journal Article
A Network Analysis of the Five Facets Mindfulness Questionnaire (FFMQ)
Objectives
The Five Facet Mindfulness Questionnaire (FFMQ) is a popular self-report instrument for mindfulness assessment. However, several studies report mixed evidence regarding its reliability and validity. While recent replication studies have shown several issues regarding its latent structure, first-order facets seemed to replicate successfully. This study proposes an exploratory approach to these facets on an item level in one sample, with cross-validation in another sample.
Methods
Using a snowball sampling, 1008 participants were recruited in the first sample. Psychometric networks were applied to explore relations between items and item clusters. We compared these exploratory latent variable proposals with previous literature. A second sample of 1210 participants was collected from an FFMQ validation study, and confirmatory factor analyses were applied to cross-validate findings on the first sample.
Results
The FFMQ showed a positively correlated network. Exploratory analyses suggested the 5-facet structure as stable with alternatives of 4-facet (merging Observe and Non-Judging) and 6-facet (splitting Acting with Awareness in two) solutions. However, the CFAs in the second sample did not provide clear support to any solution.
Conclusions
The FFMQ showed unclear evidence on its latent structure. We propose researchers and users of the FFMQ to use the most fitting solution between the 5 and 6-facet solutions in their data, since the 4-facet solution is difficult to interpret. We also propose cautionary notes and guidelines for researchers and applied users of the FFMQ and regarding this instrument. We conclude that more research is needed in mindfulness assessment to provide robust measurements.
Journal Article