Model for efficient dynamical ranking in networks.

We present a physics-inspired method for inferring dynamic rankings in directed temporal networks-networks in which each directed and timestamped edge reflects the outcome and timing of a pairwise interaction. The inferred ranking of each node is real-valued and varies in time as each new edge, encoding an outcome like a win or loss, raises or lowers the node's estimated strength or prestige, as is often observed in real scenarios including sequences of games, tournaments, or interactions in animal hierarchies. Our method works by solving a linear system of equations and requires only one parameter to be tuned.

The Clinical and Psychopathological Profile of Inpatients with Eating Disorders: Comparing Vomiting, Laxative Abuse, and Combined Purging Behaviors.

Background/objectives: The previous literature on purging behavior in eating disorders (EDs) suggests an overall more complicated clinical picture for individuals with this symptomatology. So far, no studies have analyzed the possible differences between the specific types of purging among ED inpatients.

Methods: A clinical sample of 302 inpatients with EDs was classified according to no purging behaviors, vomiting, the abuse of laxatives, and both vomiting and the abuse of laxatives.

Similarity and economy of scale in urban transportation networks and optimal transport-based infrastructures.

Designing and optimizing the structure of urban transportation networks is a challenging task. In this study, we propose a method inspired by optimal transport theory and the principle of economy of scale that uses little information in input to generate structures that are similar to those of public transportation networks. Contrarily to standard approaches, it does not assume any initial backbone network infrastructure but rather extracts this directly from a continuous space using only a few origin and destination points, generating networks from scratch.

Optimal Transport with Constraints: From Mirror Descent to Classical Mechanics.

Finding optimal trajectories for multiple traffic demands in a congested network is a challenging task. Optimal transport theory is a principled approach that has been used successfully to study various transportation problems. Its usage is limited by the lack of principled and flexible ways to incorporate realistic constraints.

Structure and inference in hypergraphs with node attributes.

Many networked datasets with units interacting in groups of two or more, encoded with hypergraphs, are accompanied by extra information about nodes, such as the role of an individual in a workplace. Here we show how these node attributes can be used to improve our understanding of the structure resulting from higher-order interactions. We consider the problem of community detection in hypergraphs and develop a principled model that combines higher-order interactions and node attributes to better represent the observed interactions and to detect communities more accurately than using either of these types of information alone.

Framework to generate hypergraphs with community structure.

In recent years hypergraphs have emerged as a powerful tool to study systems with multibody interactions which cannot be trivially reduced to pairs. While highly structured methods to generate synthetic data have proved fundamental for the standardized evaluation of algorithms and the statistical study of real-world networked data, these are scarcely available in the context of hypergraphs. Here we propose a flexible and efficient framework for the generation of hypergraphs with many nodes and large hyperedges, which allows specifying general community structures and tune different local statistics.

Bilevel Optimization for Traffic Mitigation in Optimal Transport Networks.

Global infrastructure robustness and local transport efficiency are critical requirements for transportation networks. However, since passengers often travel greedily to maximize their own benefit and trigger traffic jams, overall transportation performance can be heavily disrupted. We develop adaptation rules that leverage optimal transport theory to effectively route passengers along their shortest paths while also strategically tuning edge weights to optimize traffic.

Community detection in large hypergraphs.

Hypergraphs, describing networks where interactions take place among any number of units, are a natural tool to model many real-world social and biological systems. Here, we propose a principled framework to model the organization of higher-order data. Our approach recovers community structure with accuracy exceeding that of currently available state-of-the-art algorithms, as tested in synthetic benchmarks with both hard and overlapping ground-truth partitions.

Infrastructure adaptation and emergence of loops in network routing with time-dependent loads.

Network routing approaches are widely used to study the evolution in time of self-adapting systems. However, few advances have been made for problems where adaptation is governed by time-dependent inputs. In this work we study a dynamical systems where the edge conductivities of a network are regulated by time-varying mass loads injected on nodes.

Diagnostic Concordance between Research and Clinical-Based Assessments of Psychiatric Comorbidity in Anorexia Nervosa.

The literature has reported poor concordance in the assessment of psychiatric conditions, and inhomogeneity in the prevalence of psychiatric comorbidities in Anorexia Nervosa (AN). We aimed to investigate concordance level between clinicians' and researchers' diagnoses of psychiatric comorbidity in AN and differences in eating and general psychopathology between patients with and without psychiatric comorbidity assessed by clinicians versus researchers. A clinical psychiatrist interviewed 122 patients with AN; then a researcher administered the Structured and Clinical Interview for DSM-5 (SCID-5).

Inference of hyperedges and overlapping communities in hypergraphs.

Hypergraphs, encoding structured interactions among any number of system units, have recently proven a successful tool to describe many real-world biological and social networks. Here we propose a framework based on statistical inference to characterize the structural organization of hypergraphs. The method allows to infer missing hyperedges of any size in a principled way, and to jointly detect overlapping communities in presence of higher-order interactions.

Shame, Guilt, and Self-Consciousness in Anorexia Nervosa.

Background: the role of self-conscious emotions (SCE) such as shame and guilt in eating disorders (ED) has been systematically studied only in recent years, but it is still debated. This study aims to investigate the role of SCE in anorexia nervosa (AN), evaluating the role of self-consciousness.

Methods: fifty-five individuals with AN and seventy-four healthy controls (HC) were enrolled and completed a battery of tests evaluating the proneness to feel shame and guilt, as well as comparing self-consciousness, eating, and general psychopathology.

Community detection in networks by dynamical optimal transport formulation.

Detecting communities in networks is important in various domains of applications. While a variety of methods exist to perform this task, recent efforts propose Optimal Transport (OT) principles combined with the geometric notion of Ollivier-Ricci curvature to classify nodes into groups by rigorously comparing the information encoded into nodes' neighborhoods. We present an OT-based approach that exploits recent advances in OT theory to allow tuning between different transportation regimes.

Sustainable optimal transport in multilayer networks.

Traffic congestion is one of the major challenges faced by the transportation industry. While this problem carries a high economic and environmental cost, the need for an efficient design of optimal paths for passengers in multilayer network infrastructures is imperative. We consider an approach based on optimal transport theory to route passengers preferably along layers that are more carbon-efficient than the road, e.

The interplay between ranking and communities in networks.

Community detection and hierarchy extraction are usually thought of as separate inference tasks on networks. Considering only one of the two when studying real-world data can be an oversimplification. In this work, we present a generative model based on an interplay between community and hierarchical structures.

Multicommodity routing optimization for engineering networks.

Optimizing passengers routes is crucial to design efficient transportation networks. Recent results show that optimal transport provides an efficient alternative to standard optimization methods. However, it is not yet clear if this formalism has empirical validity on engineering networks.

Psychiatric patients at the emergency department: factors associated with length of stay and likelihood of hospitalization.

Emergency department (ED) care for psychiatric patients is currently understudied despite being highly utilized. Therefore, we aimed to analyze psychiatric patients' length of stay (LOS) and LOS-related factors at the ED and to investigate and quantify the likelihood of being hospitalized after an emergency psychiatric evaluation. Charts of 408 individuals who sought help at the ED were retrospectively assessed to identify patients' sociodemographic and clinical data upon ED admission and discharge.

Principled network extraction from images.

Images of natural systems may represent patterns of network-like structure, which could reveal important information about the topological properties of the underlying subject. However, the image itself does not automatically provide a formal definition of a network in terms of sets of nodes and edges. Instead, this information should be suitably extracted from the raw image data.

Young Patients with Anorexia Nervosa: The Contribution of Post-Traumatic Stress Disorder and Traumatic Events.

: Anorexia nervosa (AN) is a complex disorder whose etiopathogenesis involves both biological and environmental factors. The aims of the present study were to retrospectively analyze risk factors in young patients with AN and to assess differences in clinical and eating-related symptoms between patients with and without a diagnosis of post-traumatic stress disorder (PTSD) and with or without a history of acknowledged risk factors. : Sixty-four patients with AN (<25 years old) were recruited and completed an anamnestic evaluation and the following self-report measures: Eating Disorder Examination Questionnaire (EDE-Q), Childhood Trauma Questionnaire (CTQ), State-Trait Anxiety Inventory (STAI-Y), Beck Depression Inventory (BDI), Life Events Checklist (LEC), and Dissociative Experience Scale (DES).

Network extraction by routing optimization.

Routing optimization is a relevant problem in many contexts. Solving directly this type of optimization problem is often computationally intractable. Recent studies suggest that one can instead turn this problem into one of solving a dynamical system of equations, which can instead be solved efficiently using numerical methods.

Community detection with node attributes in multilayer networks.

Community detection in networks is commonly performed using information about interactions between nodes. Recent advances have been made to incorporate multiple types of interactions, thus generalizing standard methods to multilayer networks. Often, though, one can access additional information regarding individual nodes, attributes, or covariates.

Affective temperaments and eating psychopathology in anorexia nervosa: Which role for anxious and depressive traits?

Background: Anorexia nervosa (AN) is a severe mental illness. Personality traits and comorbidity with affective and anxiety disorders are key-aspects of its pathogenesis but little attention has been paid so far to affective temperaments in AN. Also, childhood anxiety is proposed to impact on AN clinical severity.

Dynamics of beneficial epidemics.

Pathogens can spread epidemically through populations. Beneficial contagions, such as viruses that enhance host survival or technological innovations that improve quality of life, also have the potential to spread epidemically. How do the dynamics of beneficial biological and social epidemics differ from those of detrimental epidemics? We investigate this question using a breadth-first modeling approach involving three distinct theoretical models.