Flow graphs: interweaving dynamics and structure

The behavior of complex systems is determined not only by the topological organization of their interconnections but also by the dynamical processes taking place among their constituents. A faithful modeling of the dynamics is essential because different dynamical processes may be affected very differently by network topology. A full characterization of such systems thus requires a formalization that encompasses both aspects simultaneously, rather than relying only on the topological adjacency matrix. To achieve this, we introduce the concept of flow graphs, namely weighted networks where dynamical flows are embedded into the link weights. Flow graphs provide an integrated representation of the structure and dynamics of the system, which can then be analyzed with standard tools from network theory. Conversely, a structural network feature of our choice can also be used as the basis for the construction of a flow graph that will then encompass a dynamics biased by such a feature. We illustrate the ideas by focusing on the mathematical properties of generic linear processes on complex networks that can be represented as biased random walks and also explore their dual consensus dynamics.Comment: 4 pages, 1 figur

Using citizen science to identify Australia’s least known birds and inform conservation action

Citizen science is a popular approach to biodiversity surveying, whereby data that are collected by volunteer naturalists may help analysts to understand the distribution and abundance of wild organisms. In Australia, birdwatchers have contributed to two major citizen science programs, eBird (run by the Cornell Lab of Ornithology) and Birdata (run by Birdlife Australia), which collectively hold more than 42 million records of wild birds from across the country. However, these records are not evenly distributed across space, time, or taxonomy, with particularly significant variation in the number of records of each species in these datasets. In this paper, we explore this variation and seek to determine which Australian bird species are least known as determined by rates of citizen science survey detections. We achieve this by comparing the rates of survey effort and species detection across each Australian bird species? range, assigning all 581 species to one of the four groups depending on their rates of survey effort and species observation. We classify 56 species into a group considered the most poorly recorded despite extensive survey effort, with Coxen?s Fig Parrot Cyclopsitta coxeni, Letter-winged Kite Elanus scriptus, Night Parrot Pezoporus occidentalis, Buff-breasted Buttonquail Turnix olivii and Red-chested Buttonquail Turnix pyrrhothorax having the very lowest numbers of records. Our analyses provide a framework to identify species that are poorly represented in citizen science datasets. We explore the reasons behind why they may be poorly represented and suggest ways in which targeted approaches may be able to help fill in the gaps.Publisher PDFPeer reviewe

An efficient and principled method for detecting communities in networks

A fundamental problem in the analysis of network data is the detection of network communities, groups of densely interconnected nodes, which may be overlapping or disjoint. Here we describe a method for finding overlapping communities based on a principled statistical approach using generative network models. We show how the method can be implemented using a fast, closed-form expectation-maximization algorithm that allows us to analyze networks of millions of nodes in reasonable running times. We test the method both on real-world networks and on synthetic benchmarks and find that it gives results competitive with previous methods. We also show that the same approach can be used to extract nonoverlapping community divisions via a relaxation method, and demonstrate that the algorithm is competitively fast and accurate for the nonoverlapping problem.Comment: 14 pages, 5 figures, 1 tabl

GEMSEC: Graph Embedding with Self Clustering

Modern graph embedding procedures can efficiently process graphs with millions of nodes. In this paper, we propose GEMSEC -- a graph embedding algorithm which learns a clustering of the nodes simultaneously with computing their embedding. GEMSEC is a general extension of earlier work in the domain of sequence-based graph embedding. GEMSEC places nodes in an abstract feature space where the vertex features minimize the negative log-likelihood of preserving sampled vertex neighborhoods, and it incorporates known social network properties through a machine learning regularization. We present two new social network datasets and show that by simultaneously considering the embedding and clustering problems with respect to social properties, GEMSEC extracts high-quality clusters competitive with or superior to other community detection algorithms. In experiments, the method is found to be computationally efficient and robust to the choice of hyperparameters

Health-related quality of life in patients with β-thalassemia: Data from the phase 3 BELIEVE trial of luspatercept

BACKGROUND: Patients with transfusion-dependent (TD) β-thalassemia require long-term red blood cell transfusions (RBCTs) that lead to iron overload, impacting health-related quality of life (HRQoL). METHODS: The impact of luspatercept, a first-in-class erythroid maturation agent, versus placebo on HRQoL of patients with TD β-thalassemia was evaluated in the phase 3 BELIEVE trial. HRQoL was assessed at baseline and every 12 weeks using the 36-item Short Form Health Survey (SF-36) and Transfusion-dependent Quality of Life questionnaire (TranQol). Mean change in HRQoL was evaluated from baseline to week 48 for patients receiving luspatercept + best supportive care (BSC) and placebo + BSC and between luspatercept responders and non-responders. RESULTS: Through week 48, for both groups, mean scores on SF-36 and TranQol domains were stable over time and did not have a clinically meaningful change. At week 48, more patients who achieved clinical response (≥50% reduction in RBCT burden over 24 weeks) in the luspatercept + BSC group had improvement in SF-36 Physical Function compared with placebo + BSC (27.1% vs. 11.5%; p = .019). CONCLUSIONS: Luspatercept + BSC reduced transfusion burden while maintaining patients' HRQoL. HRQoL domain improvements from baseline through 48 weeks were also enhanced for luspatercept responders

Circadian hormone secretory profiles in women with severe premenstrual tension syndrome.

The circadian secretory profiles of serum prolactin, growth hormone and cortisol were measured in two women suffering from severe premenstrual tension syndrome and in two asymptomatic control subjects. Subjects and controls were screened and included after a rigorous selection process. Blood samples were obtained every 30 min over a period of 24 h in each woman both on day 9 (follicular phase) and day 26 (luteal phase) of the menstrual cycle. There was no relationship between the hormonal secretory profiles and the premenstrual tension syndrome.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/75119/1/j.1471-0528.1984.tb04785.x.pd

Multiple-code simulation study of the long-term EDZ evolution of geological nuclear waste repositories

This simulation study shows how widely different model approaches can be adapted to model the evolution of the excavation disturbed zone (EDZ) around a heated nuclear waste emplacement drift in fractured rock. The study includes modeling of coupled thermal-hydrological-mechanical (THM) processes, with simplified consideration of chemical coupling in terms of time-dependent strength degradation or subcritical crack growth. The different model approaches applied in this study include boundary element, finite element, finite difference, particle mechanics, and elastoplastic cellular automata methods. The simulation results indicate that thermally induced differential stresses near the top of the emplacement drift may cause progressive failure and permeability changes during the first 100 years (i.e., after emplacement and drift closure). Moreover, the results indicate that time-dependent mechanical changes may play only a small role during the first 100 years of increasing temperature and thermal stress, whereas such time-dependency is insignificant after peak temperature, because decreasing thermal stress

Universal Properties of Mythological Networks

As in statistical physics, the concept of universality plays an important, albeit qualitative, role in the field of comparative mythology. Here we apply statistical mechanical tools to analyse the networks underlying three iconic mythological narratives with a view to identifying common and distinguishing quantitative features. Of the three narratives, an Anglo-Saxon and a Greek text are mostly believed by antiquarians to be partly historically based while the third, an Irish epic, is often considered to be fictional. Here we show that network analysis is able to discriminate real from imaginary social networks and place mythological narratives on the spectrum between them. Moreover, the perceived artificiality of the Irish narrative can be traced back to anomalous features associated with six characters. Considering these as amalgams of several entities or proxies, renders the plausibility of the Irish text comparable to the others from a network-theoretic point of view.Comment: 6 pages, 3 figures, 2 tables. Updated to incorporate corrections from EPL acceptance proces