scholarly journals CONTROL CONTRIBUTION IDENTIFIES TOP DRIVER NODES IN COMPLEX NETWORKS

2019 ◽  
Vol 22 (07n08) ◽  
pp. 1950014 ◽  
Author(s):  
YAN ZHANG ◽  
ANTONIOS GARAS ◽  
FRANK SCHWEITZER
Keyword(s):  
Complex Networks ◽  
Real World ◽  
Optimization Method ◽  
Directed Network ◽  
Control Range ◽  
New Information ◽  
Random Samples ◽  
Text Ranking ◽  
The Impact ◽  
Synthetic Networks

We propose a new measure to quantify the impact of a node [Formula: see text] in controlling a directed network. This measure, called “control contribution” [Formula: see text], combines the probability for node [Formula: see text] to appear in a set of driver nodes and the probability for other nodes to be controlled by [Formula: see text]. To calculate [Formula: see text], we propose an optimization method based on random samples of minimum sets of drivers. Using real-world and synthetic networks, we find very broad distributions of [Formula: see text]. Ranking nodes according to their [Formula: see text] values allows us to identify the top driver nodes that can control most of the network. We show that this ranking is superior to rankings based on other control-based measures. We find that control contribution indeed contains new information that cannot be traced back to degree, control capacity or control range of a node.

scholarly journals Visualizing real-world networks

2021 ◽  
Author(s):  
Lyndsay Roach
Keyword(s):  
Complex Networks ◽  
Real World ◽  
The Internet ◽  
Network Data ◽  
Community Structures ◽  
Large Networks ◽  
Computing Power ◽  
On Line ◽  
Using Data ◽  
The Impact

The study of networks has been propelled by improvements in computing power, enabling our ability to mine and store large amounts of network data. Moreover, the ubiquity of the internet has afforded us access to records of interactions that have previously been invisible. We are now able to study complex networks with anywhere from hundreds to billions of nodes; however, it is difficult to visualize large networks in a meaningful way. We explore the process of visualizing real-world networks. We first discuss the properties of complex networks and the mechanisms used in the network visualizing software Gephi. Then we provide examples of voting, trade, and linguistic networks using data extracted from on-line sources. We investigate the impact of hidden community structures on the analysis of these real-world networks.

Relative Assortativity Index: A Quantitative Metric to Assess the Impact of Link Prediction Techniques on Assortativity of Complex Networks

2019 ◽  
Vol 63 (9) ◽  
pp. 1417-1437
Author(s):  
Natarajan Meghanathan
Keyword(s):  
Complex Networks ◽  
Real World ◽  
Biological Networks ◽  
Link Prediction ◽  
Preferential Attachment ◽  
Prediction Technique ◽  
Prediction Techniques ◽  
The Impact

Abstract We propose a quantitative metric (called relative assortativity index, RAI) to assess the extent with which a real-world network would become relatively more assortative due to link addition(s) using a link prediction technique. Our methodology is as follows: for a link prediction technique applied on a particular real-world network, we keep track of the assortativity index values incurred during the sequence of link additions until there is negligible change in the assortativity index values for successive link additions. We count the number of network instances for which the assortativity index after a link addition is greater or lower than the assortativity index prior to the link addition and refer to these counts as relative assortativity count and relative dissortativity count, respectively. RAI is computed as (relative assortativity count − relative dissortativity count) / (relative assortativity count + relative dissortativity count). We analyzed a suite of 80 real-world networks across different domains using 3 representative neighborhood-based link prediction techniques (Preferential attachment, Adamic Adar and Jaccard coefficients [JACs]). We observe the RAI values for the JAC technique to be positive and larger for several real-world networks, while most of the biological networks exhibited positive RAI values for all the three techniques.

scholarly journals Visualizing real-world networks

2021 ◽  
Author(s):  
Lyndsay Roach
Keyword(s):  
Complex Networks ◽  
Real World ◽  
The Internet ◽  
Network Data ◽  
Community Structures ◽  
Large Networks ◽  
Computing Power ◽  
On Line ◽  
Using Data ◽  
The Impact

The study of networks has been propelled by improvements in computing power, enabling our ability to mine and store large amounts of network data. Moreover, the ubiquity of the internet has afforded us access to records of interactions that have previously been invisible. We are now able to study complex networks with anywhere from hundreds to billions of nodes; however, it is difficult to visualize large networks in a meaningful way. We explore the process of visualizing real-world networks. We first discuss the properties of complex networks and the mechanisms used in the network visualizing software Gephi. Then we provide examples of voting, trade, and linguistic networks using data extracted from on-line sources. We investigate the impact of hidden community structures on the analysis of these real-world networks.

Effects of seeds on cooperate epidemic spreading on complex networks

2021 ◽  
pp. 2150039
Author(s):  
Tianqiao Zhang ◽  
Ruijie Wang ◽  
Yang Zhang ◽  
Junliang Chen ◽  
Xuzhen Zhu
Keyword(s):  
Complex Networks ◽  
Real World ◽  
Continuous Phase ◽  
Selection Strategy ◽  
Eigenvector Centrality ◽  
Epidemic Spreading ◽  
Selection Strategies ◽  
Final Fraction ◽  
Artificial Network ◽  
The Impact

We study the impact of seeds on cooperate epidemic spreading on complex networks. A cooperative spreading model is proposed, in which two diseases are spreading simultaneously. Once the nodes are infected by one disease, they will have a larger probability of being infected by the other. Besides, we adopt five different selection strategies to choose the seeds, and the set size of seeds is fixed at five nodes. Through extensive Monte Carlo simulations, we find that the final fraction of nodes that have been infected by one or both diseases display continuous phase transition on both synthetic networks and real-world networks, and the selection strategy does not alter the transition type. Besides, we find that the eigenvector centrality promotes the cooperative spreading on the artificial network, and the degree centrality promotes the spreading of the two cooperative diseases on the real-world networks. The results of this study are of great significance for the development of the targeted strategies of disease control.

scholarly journals An Asymmetric Popularity-Similarity Optimization Method for Embedding Directed Networks into Hyperbolic Space

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-16 ◽  
Author(s):  
Zongning Wu ◽  
Zengru Di ◽  
Ying Fan
Keyword(s):  
Complex Networks ◽  
Hyperbolic Space ◽  
Optimization Method ◽  
Directed Network ◽  
Network Embedding ◽  
Directed Networks ◽  
Scale Free ◽  
Exponential Expansion ◽  
Asymmetric Networks ◽  
Symmetric Networks

Network embedding is a frontier topic in current network science. The scale-free property of complex networks can emerge as a consequence of the exponential expansion of hyperbolic space. Some embedding models have recently been developed to explore hyperbolic geometric properties of complex networks—in particular, symmetric networks. Here, we propose a model for embedding directed networks into hyperbolic space. In accordance with the bipartite structure of directed networks and multiplex node information, the method replays the generation law of asymmetric networks in hyperbolic space, estimating the hyperbolic coordinates of each node in a directed network by the asymmetric popularity-similarity optimization method in the model. Additionally, the experiments in several real networks show that our embedding algorithm has stability and that the model enlarges the application scope of existing methods.

The impact of local processes and the prohibition of multiple links in the topological properties of directed complex networks

2015 ◽  
Vol 26 (12) ◽  
pp. 1550142 ◽  
Author(s):  
J. Esquivel-Gómez ◽  
P. D. Arjona-Villicaña ◽  
J. Acosta-Elías
Keyword(s):  
Numerical Simulation ◽  
Complex Networks ◽  
Real World ◽  
Dynamic Properties ◽  
Topological Properties ◽  
A Value ◽  
Proposed Model ◽  
Accelerated Growth ◽  
Dynamical Properties ◽  
The Impact

Local processes exert influence on the growth and evolution of complex networks, which in turn shape the topological and dynamic properties of these networks. Some local processes have been researched, for example: Addition of nodes and links, rewiring of links between nodes, accelerated growth, link removal, aging, copying and multiple links prohibition. These processes impact directly into the topological and dynamical properties of complex networks. This paper introduces a new model for growth of directed complex networks which incorporates the prohibition of multiple links, addition of nodes and links, and rewiring of links. This paper also reports on the impact that these processes have in the topological properties of the networks generated with the proposed model. Numerical simulation shows that, when the frequency of rewiring increases in the proposed model, the γ exponent of the in-degree distribution approaches a value of 1.1. When the frequency of adding new links increases, the γ exponent approaches 1. That is the proposed model is able to generate all exponent values documented in real-world networks which range 1.05 < γ < 8.94.

1607-P: Real-World Evidence of the Impact of Obesity on Residual Teeth in the Japanese Population

Diabetes ◽  
2020 ◽  
Vol 69 (Supplement 1) ◽  
pp. 1607-P
Author(s):  
MAYU HAYASHI ◽  
KATSUTARO MORINO ◽  
KAYO HARADA ◽  
MIKI ISHIKAWA ◽  
ITSUKO MIYAZAWA ◽  
...  
Keyword(s):  
Real World ◽  
Japanese Population ◽  
Real World Evidence ◽  
The Impact

scholarly journals POS1121 DEMOGRAPHICS, COMORBIDITIES, AND RENAL FUNCTION OF UNCONTROLLED GOUT PATIENTS WHO RECEIVED PEGLOTICASE: FINDING FROM A LARGE US CLAIMS DATABASE

2021 ◽  
Vol 80 (Suppl 1) ◽  
pp. 839.2-840
Author(s):  
C. Vesel ◽  
A. Morton ◽  
M. Francis-Sedlak ◽  
B. Lamoreaux
Keyword(s):  
Kidney Function ◽  
Real World ◽  
Randomized Clinical Trials ◽  
Small Subset ◽  
Phase 3 ◽  
Claims Database ◽  
Medical Diagnoses ◽  
Ckd Stage ◽  
Before And After ◽  
The Impact

Background:NHANES data indicate that approximately 9.2 million Americans have gout,1 with a small subset having uncontrolled disease.2 Pegloticase is a PEGylated recombinant uricase enzyme indicated for treating uncontrolled gout that markedly reduces serum uric acid levels (sUA)3 and resolves tophi in treatment responders.4 Despite pegloticase availability in the US for many years, real world demographics of pegloticase users in the treatment of uncontrolled gout have not been previously reported in a population-based cohort.Objectives:This study utilized a large US claims database to examine demographics and co-morbidities of uncontrolled gout patients treated with pegloticase. Kidney function before and after pegloticase treatment and concomitant therapy with immunomodulators were also examined.Methods:The TriNetX Diamond database includes de-identified data from 4.3 million US patients with gout (as of September 2019), including demographics, medical diagnoses, laboratory values, procedures (e.g. infusions, surgeries), and pharmacy data. Patients who had received ≥1 pegloticase infusion were included in these analyses. The number of infusions was evaluated for a subgroup of patients who were in the database ≥3 months before and ≥2 years after the first pegloticase infusion (i.e. first infusion prior to September 2017) to ensure only complete courses of therapy were captured. In this subpopulation, kidney function before and after pegloticase therapy was examined, along with the presence of immunomodulation prescriptions (methotrexate, mycophenolate mofetil, azathioprine, leflunomide) within 60 days prior to and 14 days after the first pegloticase infusion.Results:1494 patients treated with pegloticase were identified. Patients were 63.1 ± 14.0 years of age (range: 23–91), mostly male (82%), and white (76%). Mean sUA prior to pegloticase was 8.7 ± 2.4 mg/dL (n=50), indicating uncontrolled gout in the identified population. The most commonly reported comorbidities were chronic kidney disease (CKD, 48%), essential hypertension (71%), type 2 diabetes (39%), and cardiovascular disease (38%), similar to pegloticase pivotal Phase 3 trial populations. In patients with pre-therapy kidney function measures (n=134), pre-treatment eGFR averaged 61.2 ± 25.7 ml/min/1.73 m2, with 44% having Stage 3-5 CKD. In patients with complete therapy course capture and pre- and post-therapy eGFR measures (n=48), kidney function remained stable (change in eGFR: -2.9 ± 18.2 ml/min/1.73 m2) and CKD stage remained the same or improved in 81% of patients. In 791 patients with complete treatment course capture, patients had received 8.7 ± 13.8 infusions (median: 3, IQR: 2-10). Of these, 189 (24%) patients received only 1 pegloticase infusion and 173 (22%) received ≥12 infusions. As the data cut-off for this analysis pre-dated emerging data on the use of immunomodulation as co-therapy, only 19 of 791 (2%) patients received immunomodulation co-therapy with pegloticase.Conclusion:This relatively large group of patients with uncontrolled gout treated with pegloticase had similar patient characteristics of those studied in the phase 3 randomized clinical trials. Patients with uncontrolled gout are significantly burdened with systemic co-morbid diseases. The majority of patients had stable or improved kidney function following pegloticase treatment. As these results reflect patients initiating treatment prior to 2018, before co-treatment with immunomodulation was introduced, this cohort only included a small percentage of patients who were co-treated with an immunomodulator. Future studies using more current datasets are needed to evaluate real world outcomes in patients treated with pegloticase/immunomodulator co-therapy and to evaluate the impact of systemic co-morbid diseases.References:[1]Chen-Xu M, et al. Arthritis Rheumatol 2019 71:991-999.[2]Fels E, Sundy JS. Curr Opin Rheumatol 2008;20:198-202.[3]Sundy J, et al. JAMA 2011;306:711-720.[4]Mandell BF, et al. Arthritis Res Ther 2018;20:286.Disclosure of Interests:Claudia Vesel Shareholder of: Horizon Therapeutics plc, Employee of: Horizon Therapeutics plc, Allan Morton Speakers bureau: Sanofi, Amgen, and Horizon, Megan Francis-Sedlak Shareholder of: Horizon Therapeutics plc, Employee of: Horizon Therapeutics plc, Brian LaMoreaux Shareholder of: Horizon Therapeutics plc, Employee of: Horizon Therapeutics plc.

scholarly journals Network robustness improvement via long-range links

2019 ◽  
Vol 6 (1) ◽  
Author(s):  
Vincenza Carchiolo ◽  
Marco Grassia ◽  
Alessandro Longheu ◽  
Michele Malgeri ◽  
Giuseppe Mangioni
Keyword(s):  
Complex Networks ◽  
Long Range ◽  
Real World ◽  
Network Robustness ◽  
Scale Free ◽  
Area Of Interest ◽  
Before And After ◽  
Significant Area ◽  
Better Than

AbstractMany systems are today modelled as complex networks, since this representation has been proven being an effective approach for understanding and controlling many real-world phenomena. A significant area of interest and research is that of networks robustness, which aims to explore to what extent a network keeps working when failures occur in its structure and how disruptions can be avoided. In this paper, we introduce the idea of exploiting long-range links to improve the robustness of Scale-Free (SF) networks. Several experiments are carried out by attacking the networks before and after the addition of links between the farthest nodes, and the results show that this approach effectively improves the SF network correct functionalities better than other commonly used strategies.

PP469 From Theory To Practice: Which Value Framework Is Applied For Onco-Hematology Therapies In Italy? A 5-year Retrospective Analysis

2020 ◽  
Vol 36 (S1) ◽  
pp. 37-37
Author(s):  
Americo Cicchetti ◽  
Rossella Di Bidino ◽  
Entela Xoxi ◽  
Irene Luccarini ◽  
Alessia Brigido
Keyword(s):  
Real World ◽  
Ad Hoc ◽  
Grey Literature ◽  
National Level ◽  
Pharmaceutical Products ◽  
European Medicines Agency ◽  
The Real ◽  
R Process ◽  
The One ◽  
The Impact

IntroductionDifferent value frameworks (VFs) have been proposed in order to translate available evidence on risk-benefit profiles of new treatments into Pricing & Reimbursement (P&R) decisions. However limited evidence is available on the impact of their implementation. It's relevant to distinguish among VFs proposed by scientific societies and providers, which usually are applicable to all treatments, and VFs elaborated by regulatory agencies and health technology assessment (HTA), which focused on specific therapeutic areas. Such heterogeneity in VFs has significant implications in terms of value dimension considered and criteria adopted to define or support a price decision.MethodsA literature research was conducted to identify already proposed or adopted VF for onco-hematology treatments. Both scientific and grey literature were investigated. Then, an ad hoc data collection was conducted for multiple myeloma; breast, prostate and urothelial cancer; and Non Small Cell Lung Cancer (NSCLC) therapies. Pharmaceutical products authorized by European Medicines Agency from January 2014 till December 2019 were identified. Primary sources of data were European Public Assessment Reports and P&R decision taken by the Italian Medicines Agency (AIFA) till September 2019.ResultsThe analysis allowed to define a taxonomy to distinguish categories of VF relevant to onco-hematological treatments. We identified the “real-world” VF that emerged given past P&R decisions taken at the Italian level. Data was collected both for clinical and economical outcomes/indicators, as well as decisions taken on innovativeness of therapies. Relevant differences emerge between the real world value framework and the one that should be applied given the normative framework of the Italian Health System.ConclusionsThe value framework that emerged from the analysis addressed issues of specific aspects of onco-hematological treatments which emerged during an ad hoc analysis conducted on treatment authorized in the last 5 years. The perspective adopted to elaborate the VF was the one of an HTA agency responsible for P&R decisions at a national level. Furthermore, comparing a real-world value framework with the one based on the general criteria defined by the national legislation, our analysis allowed identification of the most critical point of the current national P&R process in terms ofsustainability of current and future therapies as advance therapies and agnostic-tumor therapies.

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