Explore the vast range of titles available.

This accessible book provides an introduction to the analysis and design of dynamic multiagent networks. Such networks are of great interest in a wide range of areas in science and engineering, including: mobile sensor networks, distributed robotics such as formation flying and swarming, quantum networks, networked economics, biological synchronization, and social networks. Focusing on graph theoretic methods for the analysis and synthesis of dynamic multiagent networks, the book presents a powerful new formalism and set of tools for networked systems. The book's three sections look at foundations, multiagent networks, and networks as systems. The authors give an overview of important ideas from graph theory, followed by a detailed account of the agreement protocol and its various extensions, including the behavior of the protocol over undirected, directed, switching, and random networks. They cover topics such as formation control, coverage, distributed estimation, social networks, and games over networks. And they explore intriguing aspects of viewing networks as systems, by making these networks amenable to control-theoretic analysis and automatic synthesis, by monitoring their dynamic evolution, and by examining higher-order interaction models in terms of simplicial complexes and their applications. The book will interest graduate students working in systems and control, as well as in computer science and robotics. It will be a standard reference for researchers seeking a self-contained account of system-theoretic aspects of multiagent networks and their wide-ranging applications. This book has been adopted as a textbook at the following universities: University of Stuttgart, GermanyRoyal Institute of Technology, SwedenJohannes Kepler University, AustriaGeorgia Tech, USAUniversity of Washington, USAOhio University, USA

Recent studies have explored the addition of virtual edges to word co-occurrence networks using word embeddings to enhance graph representations, particularly for short texts. While these enriched networks have demonstrated some success, the impact of incorporating semantic edges into traditional co-occurrence networks remains uncertain. In this study, we investigate two key statistical properties of text-based network models. First, we assess whether network metrics can effectively distinguish between meaningless and meaningful texts. Second, we analyze whether these metrics are more sensitive to syntactic or semantic aspects of the text. Our results show that incorporating virtual edges can have both positive and negative effects, depending on the specific network metric. For instance, the informativeness of the average shortest path and closeness centrality improves in short texts, while the clustering coefficient’s informativeness decreases as more virtual edges are added. Additionally, we found that including stopwords affects the statistical properties of enriched networks. Our results, derived from enriching networks with FastText embeddings, offer a guideline for identifying the most appropriate network metrics for specific applications, based on typical text length and the nature of the task.

Background The rewiring of molecular interactions in various conditions leads to distinct phenotypic outcomes. Differential network analysis (DINA) is dedicated to exploring these rewirings within gene and protein networks. Leveraging statistical learning and graph theory, DINA algorithms scrutinize alterations in interaction patterns derived from experimental data. Results Introducing a novel approach to differential network analysis, we incorporate differential gene expression based on sex and gender attributes. We hypothesize that gene expression can be accurately represented through non-Gaussian processes. Our methodology involves quantifying changes in non-parametric correlations among gene pairs and expression levels of individual genes. Conclusions Applying our method to public expression datasets concerning diabetes mellitus and atherosclerosis in liver tissue, we identify gender-specific differential networks. Results underscore the biological relevance of our approach in uncovering meaningful molecular distinctions.

Alcohol use disorder (AUD) is a chronic condition linked to allostatic neuroadaptations in the brain's reward circuitry, leading to compulsive and automatized alcohol use in response to craving or negative affect. There are only a few treatment options for AUD, and their efficacy and tolerance profiles remain suboptimal. New AUD management strategies are actively being investigated, and among these, non-invasive brain stimulation (NIBS) interventions. We are planning to conduct a systematic review and network meta-analysis to simultaneously compare different NIBS strategies for AUD, and the present protocol aims to document our methodological approaches and a priori decisions. We will include only randomized controlled trials involving adults with AUD, alcohol dependence, or alcohol abuse. The primary interest outcomes of our review will concern alcohol consumption in AUD population. In trials investigating NIBS as a strategy for alcohol use reduction, we will explore the effect of NIBS on the reduction in total alcohol consumption and the number of heavy drinking days among participants. In trials in recently detoxified AUD patients where the potential of NIBS to prevent relapse is explored, the primary outcome will concern the rate of relapse. Data on craving and safety parameters will be gathered as secondary interest outcomes. At the time of submitting this protocol, four electronic databases (EMBASE, PubMed, PsycINFO, and The Cochrane Library) and three clinical trial registries (Clinical Trials, EU Trials, WHO ICTRP) were searched. The results of the searches were screened in a blinded manner by two authors using titles and abstracts, with conflicts adjudicated by a third author. The second round of selection based on full texts will be performed after the protocol submission. Data will then be extracted independently by two authors using a predefined extraction form. Risk of bias evaluation for each trial will be performed independently by two authors using the revised Cochrane risk-of-bias tool for randomized trials (RoB 2). We will quantitatively synthesize the extracted results using mean differences and risk ratios as effect measures. Initially, a random-effects pairwise meta-analysis will be performed to compare treatment and control arms across different trials. A network meta-analysis will then be conducted. The results of the network meta-analysis will be presented as a network graph representing treatment nodes and direct comparisons, a league table with both direct and network meta-analysis (indirect or mixed) estimates, a net heat plot for inconsistency evaluation, and CINeMA evaluation of the confidence in our results. PROSPERO registration number CRD42024504362.

In this article, we use newly developed statistical methods to examine the generative processes that give rise to widespread patterns in friendship networks. The methods incorporate both traditional demographic measures on individuals (age, sex, and race) and network measures for structural processes operating on individual, dyadic, and triadic levels. We apply the methods to adolescent friendship networks in 59 U.S. schools from the National Longitudinal Survey of Adolescent Health (Add Health). We model friendship formation as a selection process constrained by individuals' sociality (propensity to make friends), selective mixing in dyads (friendships within race, grade, or sex categories are differentially likely relative to cross-category friendships), and closure in triads (a friend's friends are more likely to become friends), given local population composition. Blacks are generally the most cohesive racial category, although when whites are in the minority, they display stronger selective mixing than do blacks when blacks are in the minority. Hispanics exhibit disassortative selective mixing under certain circumstances; in other cases, they exhibit assortative mixing but lack the higher-order cohesion common in other groups. Grade levels are always highly cohesive, while females form triangles more than males. We conclude with a discussion of how network analysis may contribute to our understanding of sociodemographic structure and the processes that create it.

This study aims to provide a comprehensive bibliometric analysis covering the common areas between artificial intelligence (AI) applications and research focused on local or regional contexts. The analysis covers the period between the year 2002 and the year 2023, utilizing data sourced from the Web of Science database. Employing the Bibliometrix package within RStudio and VOSviewer software, the study identifies a significant increase in AI-related publications, with an annual growth rate of 22.67%. Notably, key journals such as Remote Sensing, PLOS ONE, and Sustainability rank among the top contributing sources. From the perspective of prominent contributing affiliations, institutions like Duy Tan University, Ton Duc Thang University, and the Chinese Academy of Sciences emerge as leading contributors, with Vietnam, Portugal, and China being the countries with the highest citation counts. Furthermore, a word cloud analysis is able to highlight the recurring keywords, including “model”, “classification”, “prediction”, “logistic regression”, “innovation”, “performance”, “random forest”, “impact”, “machine learning”, “artificial intelligence”, and “deep learning”. The co-occurrence network analysis reveals five clusters, amongst them being “artificial neural network”, “regional development”, “climate change”, “regional economy”, “management”, “technology”, “risk”, and “fuzzy inference system”. Our findings support the fact that AI is increasingly employed to address complex regional challenges, such as resource management and urban planning. AI applications, including machine learning algorithms and neural networks, have become essential for optimizing processes and decision-making at the local level. The study concludes with the fact that while AI holds vast potential for transforming local and regional research, ongoing international collaboration and the development of adaptable AI models are essential for maximizing the benefits of these technologies. Such efforts will ensure the effective implementation of AI in diverse contexts, thereby supporting sustainable regional development.

Spatial community detection is a method that divides geographic spaces into several sub-regions based on spatial interactions, reflecting the regional spatial structure against the background of human mobility. In recent years, spatial community detection has attracted extensive research in the field of geographic information science. However, mining the community structures and their evolutionary patterns from spatial interaction data remains challenging. Most existing methods for spatial community detection rely on representing spatial interaction networks in Euclidean space, which results in significant distortion when modeling spatial interaction networks; since spatial community detection has no ground truth, this results in the detection and evaluation of communities being difficult. Furthermore, most methods usually ignore the dynamics of these spatial interaction networks, resulting in the dynamic evolution of spatial communities not being discussed in depth. Therefore, this study proposes a framework for community detection and evolutionary analysis for spatial interaction networks. Specifically, we construct a spatial interaction network based on network science theory, where geographic units serve as nodes and interaction relationships serve as edges. In order to fully learn the structural features of the spatial interaction network, we introduce a hyperbolic graph convolution module in the community detection phase to learn the spatial and non-spatial attributes of the spatial interaction network, obtain vector representations of the nodes, and optimize them based on a graph generation model to achieve the final community detection results. Considering the dynamics of spatial interactions, we analyze the evolution of the spatial community over time. Finally, using taxi trajectory data as an example, we conduct relevant experiments within the fifth ring road of Beijing. The empirical results validate the community detection capabilities of the proposed method, which can effectively describe the dynamic spatial structure of cities based on human mobility and provide an effective analytical method for urban spatial planning.

Purpose Traditional techniques for automating the planning of brain electrode placement based on multi-objective optimization involving many parameters are subject to limitations, especially in terms of sensitivity to local optima, and tend to be replaced by machine learning approaches. This paper explores the feasibility of using deep reinforcement learning (DRL) in this context, starting with the single-electrode use-case of deep brain stimulation (DBS). Methods We propose a DRL approach based on deep Q-learning where the states represent the electrode trajectory and associated information, and actions are the possible motions. Deep neural networks allow to navigate the complex state space derived from MRI data. The chosen reward function emphasizes safety and accuracy in reaching the target structure. The results were compared with a reference (segmented electrode) and a conventional technique. Results The DRL approach excelled in navigating the complex anatomy, consistently providing safer and more precise electrode placements than the reference. Compared to conventional techniques, it showed an improvement in accuracy of 2.3% in average proximity to obstacles and 19.4% in average orientation angle. Expectedly, computation times rose significantly, from 2 to 18 min. Conclusion Our investigation into DRL for DBS electrode trajectory planning has showcased its promising potential. Despite only delivering modest accuracy gains compared to traditional methods in the single-electrode case, its relevance for problems with high-dimensional state and action spaces and its resilience against local optima highlight its promising role for complex scenarios. This preliminary study constitutes a first step toward the more challenging problem of multiple-electrodes planning.

 Predicting graduates’ first destinations in advance is crucial for targeted career planning and strategic curriculum development. The relationship between first destinations and academic performance exhibits typical high-dimensional networks and varying degrees of correlation characteristics. To reveal this complex relationship, we developed a prediction and visual analysis method based on causal inference for predicting graduates’ first destinations. First, we collaborated with university administrators to conceive the First Destination and Academic Performance (FDAP) dataset, which aims to define the available attributes related to academic performance and first destination. Second, we propose a key feature selection method based on the causal inference and random forest (CIRF), which both extracts key features in FDAP for training models and reveals causal relationships in the FDAP dataset. Third, we propose a novel prediction model, CIRF-MLP, which combines the CIRF method with a multilayer perceptron neural network that can predict students’ first destination based on their current academic performance. When benchmarked against four other baseline prediction models on the real FDAP dataset, our model showcased exceptional performance, and the ablation experiment results demonstrate the necessity of each model component. Fourth, we developed CausalCareerVis, a visual analysis system built on the CIRF-MLP model, to analyze the causality and correlation between graduates’ First Destination and Academic Performance, and to predict their first destinations based on academic performance. Three case studies highlighted the effectiveness and practicality of our work. Graphic abstract