Network compression with configuration models and the minimum description length.

Random network models, constrained to reproduce specific statistical features, are often used to represent and analyze network data and their mathematical descriptions. Chief among them, the configuration model constrains random networks by their degree distribution and is foundational to many areas of network science. However, configuration models and their variants are often selected based on intuition or mathematical and computational simplicity rather than on statistical evidence.

Inference of dynamic hypergraph representations in temporal interaction data.

A range of systems across the social and natural sciences generate data sets consisting of interactions between two distinct categories of items at various instances in time. Online shopping, for example, generates purchasing events of the form (user, product, time of purchase), and mutualistic interactions in plant-pollinator systems generate pollination events of the form (insect, plant, time of pollination). These data sets can be meaningfully modeled as temporal hypergraph snapshots in which multiple items within one category (i.

Identifying hubs in directed networks.

Nodes in networks that exhibit high connectivity, also called "hubs," play a critical role in determining the structural and functional properties of networked systems. However, there is no clear definition of what constitutes a hub node in a network, and the classification of network hubs in existing work has either been purely qualitative or relies on ad hoc criteria for thresholding continuous data that do not generalize well to networks with certain degree sequences. Here we develop a set of efficient nonparametric methods that classify hub nodes in directed networks using the Minimum Description Length principle, effectively providing a clear and principled definition for network hubs.

Characterizing network circuity among heterogeneous urban amenities.

The spatial configuration of urban amenities and the streets connecting them collectively provide the structural backbone of a city, influencing its accessibility, vitality and ultimately the well-being of its residents. Most accessibility measures focus on the proximity of amenities in space or along transportation networks, resulting in metrics largely determined by urban density alone. These measures are unable to gauge how efficiently street networks can navigate between amenities, since they neglect the circuity component of accessibility.

Heterogeneous message passing for heterogeneous networks.

Message passing (MP) is a computational technique used to find approximate solutions to a variety of problems defined on networks. MP approximations are generally accurate in locally treelike networks but require corrections to maintain their accuracy level in networks rich with short cycles. However, MP may already be computationally challenging on very large networks and additional costs incurred by correcting for cycles could be prohibitive.

Implicit models, latent compression, intrinsic biases, and cheap lunches in community detection.

The task of community detection, which aims to partition a network into clusters of nodes to summarize its large-scale structure, has spawned the development of many competing algorithms with varying objectives. Some community detection methods are inferential, explicitly deriving the clustering objective through a probabilistic generative model, while other methods are descriptive, dividing a network according to an objective motivated by a particular application, making it challenging to compare these methods on the same scale. Here we present a solution to this problem that associates any community detection objective, inferential or descriptive, with its corresponding implicit network generative model.

Connecting intercity mobility with urban welfare.

While significant effort has been devoted to understand the role of intraurban characteristics on sustainability and growth, much remains to be understood about the effect of interurban interactions and the role cities have in determining each other's urban welfare. Here we consider a global mobility network of population flows between cities as a proxy for the communication between these regions, and analyze how it correlates with socioeconomic indicators. We use several measures of centrality to rank cities according to their importance in the mobility network, finding PageRank to be the most effective measure for reflecting these prosperity indicators.

Impact of urban structure on infectious disease spreading.

The ongoing SARS-CoV-2 pandemic has been holding the world hostage for several years now. Mobility is key to viral spreading and its restriction is the main non-pharmaceutical interventions to fight the virus expansion. Previous works have shown a connection between the structural organization of cities and the movement patterns of their residents.

Clustering of heterogeneous populations of networks.

Statistical methods for reconstructing networks from repeated measurements typically assume that all measurements are generated from the same underlying network structure. This need not be the case, however. People's social networks might be different on weekdays and weekends, for instance.

Belief propagation for networks with loops.

Belief propagation is a widely used message passing method for the solution of probabilistic models on networks such as epidemic models, spin models, and Bayesian graphical models, but it suffers from the serious shortcoming that it works poorly in the common case of networks that contain short loops. Here, we provide a solution to this long-standing problem, deriving a belief propagation method that allows for fast calculation of probability distributions in systems with short loops, potentially with high density, as well as giving expressions for the entropy and partition function, which are notoriously difficult quantities to compute. Using the Ising model as an example, we show that our approach gives excellent results on both real and synthetic networks, improving substantially on standard message passing methods.

Integrating online and offline data for crisis management: Online geolocalized emotion, policy response, and local mobility during the COVID crisis.

Integrating online and offline data is critical for uncovering the interdependence between policy and public emotional and behavioral responses in order to aid the development of effective spatially targeted interventions during crises. As the COVID-19 pandemic began to sweep across the US it elicited a wide spectrum of responses, both online and offline, across the population. Here, we analyze around 13 million geotagged tweets in 49 cities across the US from the first few months of the pandemic to assess regional dependence in online sentiments with respect to a few major COVID-19 related topics, and how these sentiments correlate with policy development and human mobility.

Publisher Correction: Robust design from systems physics.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Robust design from systems physics.

A crucial challenge in engineering modern, integrated systems is to produce robust designs. However, quantifying the robustness of a design is less straightforward than quantifying the robustness of products. For products, in particular engineering materials, intuitive, plain language terms of strong versus weak and brittle versus ductile take on precise, quantitative meaning in terms of stress-strain relationships.

Mixing Patterns in Interdisciplinary Co-Authorship Networks at Multiple Scales.

There are inherent challenges to interdisciplinary research collaboration, such as bridging cognitive gaps and balancing transaction costs with collaborative benefits. This raises the question: Does interdisciplinary research collaboration necessarily result in disciplinary diversity among collaborators? We aim to explore this question by assessing collaborative preferences in interdisciplinary research at multiple scales through the examinination of disciplinary mixing patterns at the individual, dyadic, and team level in a coauthor network from the field of artificial intelligence in education, an emerging interdisciplinary area. Our key finding is that disciplinary diversity is reflected by diverse research experiences of individual researchers rather than diversity within pairs or groups of researchers.

Arsenic Exposure Decreases Adiposity During High-Fat Feeding.

Objective: Arsenic is an endocrine-disrupting chemical associated with diabetes risk. Increased adiposity is a significant risk factor for diabetes and its comorbidities. Here, the impact of chronic arsenic exposure on adiposity and metabolic health was assessed in mice.

Developmental exposure to the endocrine disruptor tolylfluanid induces sex-specific later-life metabolic dysfunction.

Endocrine-disrupting chemicals (EDCs) are implicated in the developmental mis-programming of energy metabolism. This study examined the impact of combined gestational and lactational exposure to the fungicide tolylfluanid (TF) on metabolic physiology in adult offspring. C57BL/6 J dams received standard rodent chow or the same diet containing 67 mg/kg TF.

Balance in signed networks.

We consider signed networks in which connections or edges can be either positive (friendship, trust, alliance) or negative (dislike, distrust, conflict). Early literature in graph theory theorized that such networks should display "structural balance," meaning that certain configurations of positive and negative edges are favored and others are disfavored. Here we propose two measures of balance in signed networks based on the established notions of weak and strong balance, and we compare their performance on a range of tasks with each other and with previously proposed measures.

Arsenic modifies serotonin metabolism through glucuronidation in pancreatic β-cells.

In arsenic-endemic regions of the world, arsenic exposure correlates with diabetes mellitus. Multiple animal models of inorganic arsenic (iAs, as As) exposure have revealed that iAs-induced glucose intolerance manifests as a result of pancreatic β-cell dysfunction. To define the mechanisms responsible for this β-cell defect, the MIN6-K8 mouse β-cell line was exposed to environmentally relevant doses of iAs.

From the betweenness centrality in street networks to structural invariants in random planar graphs.

The betweenness centrality, a path-based global measure of flow, is a static predictor of congestion and load on networks. Here we demonstrate that its statistical distribution is invariant for planar networks, that are used to model many infrastructural and biological systems. Empirical analysis of street networks from 97 cities worldwide, along with simulations of random planar graph models, indicates the observed invariance to be a consequence of a bimodal regime consisting of an underlying tree structure for high betweenness nodes, and a low betweenness regime corresponding to loops providing local path alternatives.

Diet-dependence of metabolic perturbations mediated by the endocrine disruptor tolylfluanid.

Emerging evidence implicates environmental endocrine-disrupting chemicals (EDCs) in the pathogenesis of metabolic diseases such as obesity and diabetes; however, the interactions between EDCs and traditional risk factors in disease pathogenesis remain incompletely characterized. The present study interrogates the interaction of the EDC tolylfluanid (TF) and traditional dietary stressors in the promotion of metabolic dysfunction. Eight-week-old male C57BL/6 mice were fed a high-fat, high-sucrose diet (HFHSD) or a high-sucrose diet (HSD), with or without TF supplementation at 100 μg/g, for 12 weeks.

Arsenic exposure induces glucose intolerance and alters global energy metabolism.

Environmental pollutants acting as endocrine-disrupting chemicals (EDCs) are recognized as potential contributors to metabolic disease pathogenesis. One such pollutant, arsenic, contaminates the drinking water of ~100 million people globally and has been associated with insulin resistance and diabetes in epidemiological studies. Despite these observations, the precise metabolic derangements induced by arsenic remain incompletely characterized.

Dietary exposure to the endocrine disruptor tolylfluanid promotes global metabolic dysfunction in male mice.

Environmental endocrine disruptors are implicated as putative contributors to the burgeoning metabolic disease epidemic. Tolylfluanid (TF) is a commonly detected fungicide in Europe, and previous in vitro and ex vivo work has identified it as a potent endocrine disruptor with the capacity to promote adipocyte differentiation and induce adipocytic insulin resistance, effects likely resulting from activation of glucocorticoid receptor signaling. The present study extends these findings to an in vivo mouse model of dietary TF exposure.

Electrothermal arthroscopic capsulorrhaphy: old technology, new evidence. A multicenter randomized clinical trial.

Background: Radiofrequency technology for shoulder instability was rapidly adopted despite limited clinical evidence and a poor understanding of its indications. Reports of serious adverse events followed, leading to its abandonment. This paper presents findings from a multicenter randomized clinical trial evaluating the safety and efficacy of electrothermal arthroscopic capsulorrhaphy (ETAC) compared with open inferior capsular shift (ICS) and reviews the role of randomized trials in adopting new technology.

Environmental endocrine disruption of energy metabolism and cardiovascular risk.

Rates of metabolic diseases have increased at an astounding rate in recent decades. Even though poor diet and physical inactivity are central drivers, these lifestyle changes alone fail to fully account for the magnitude and rapidity of the epidemic. Thus, attention has turned to identifying novel risk factors, including the contribution of environmental endocrine disrupting chemicals.

Operative versus nonoperative treatment of acute Achilles tendon ruptures: a multicenter randomized trial using accelerated functional rehabilitation.

Background: To date, studies directly comparing the rerupture rate in patients with an Achilles tendon rupture who are treated with surgical repair with the rate in patients treated nonoperatively have been inconclusive but the pooled relative risk of rerupture favored surgical repair. In all but one study, the limb was immobilized for six to eight weeks. Published studies of animals and humans have shown a benefit of early functional stimulus to healing tendons.