Scale-free networks

Hein, Oliver; Schwind, Michael; König, Wolfgang

doi:10.1007/s11576-006-0058-2

Cited by 77 publications

(22 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…While heavily tailed degree distributions (Fig 3) are characteristic of ‘scale-free networks’ [40]. Small world and scale free networks, which are observed in many empirical networks (social networks, the world wide web, and biological networks), have properties that are well known to influence the function and resilience of the network, and provide useful insights into the function of the CLD.…”

Section: Discussionmentioning

confidence: 99%

Quantifying a Systems Map: Network Analysis of a Childhood Obesity Causal Loop Diagram

et al. 2016

View full text Add to dashboard Cite

Causal loop diagrams developed by groups capture a shared understanding of complex problems and provide a visual tool to guide interventions. This paper explores the application of network analytic methods as a new way to gain quantitative insight into the structure of an obesity causal loop diagram to inform intervention design. Identification of the structural features of causal loop diagrams is likely to provide new insights into the emergent properties of complex systems and analysing central drivers has the potential to identify leverage points. The results found the structure of the obesity causal loop diagram to resemble commonly observed empirical networks known for efficient spread of information. Known drivers of obesity were found to be the most central variables along with others unique to obesity prevention in the community. While causal loop diagrams are often specific to single communities, the analytic methods provide means to contrast and compare multiple causal loop diagrams for complex problems.

show abstract

Section: Discussionmentioning

confidence: 99%

Quantifying a Systems Map: Network Analysis of a Childhood Obesity Causal Loop Diagram

et al. 2016

View full text Add to dashboard Cite

show abstract

“…The core drug patterns must confirm to the CM theory and be accepted by the prestigious Chinese clinicians. The multi-dimension association rule and scale-free networks method will be applied to mine the core drug patterns and drug utilization rule of three Chinese clinicians [15,20-22]. In CM, the disease syndromes and signs, disease pattern and treatment are a dynamic procedure.…”

Section: Methodsmentioning

confidence: 99%

Exploring effective core drug patterns in primary insomnia treatment with Chinese herbal medicine: study protocol for a randomized controlled trial

Yan

Zhang

Zhou

et al. 2013

Trials

View full text Add to dashboard Cite

BackgroundChinese herbal medicine is one of the most popular Chinese medicine (CM) therapies for primary insomnia. One of the important characteristics of CM is that different Chinese clinicians give different prescriptions even for the same patient. However, there must be some fixed drug patterns in every clinician’s prescriptions. This study aims to screen the effective core drug patterns in primary insomnia treatment of three prestigious Chinese clinicians.Methods/designA triple-blind, randomized, placebo-controlled, parallel-group clinical trial will be performed. Three clinicians will diagnose and treat every eligible patient individually and independently, producing three prescriptions from three clinicians for every patient. Patients will equally be randomized to one of four groups – medical group A, medical group B, medical group C, or placebo group – and observed for efficacy of treatment. The sample will include primary insomnia patients meeting DSM IV-TR criteria, Spiegel scale score >18, and age 18 to 65 years. A sequential design is employed. Interim analysis will be conducted when between 80 and 160 patients complete the study. The interim study could be stopped and treated as final if a statistically significant difference between treatment and placebo groups can be obtained and core effective drug patterns can be determined. Otherwise, the study continues until the maximum sample size reaches 300. Treatment of the CM group is one of three Chinese clinicians’ prescriptions, who provide independently prescriptions based on their own CM theory and the patient’s disease condition. Assessment will be by sleep diary and Pittsburgh sleep quality index, and CM symptoms and signs will be measured. Primary outcome is total sleep time. Assessment will be carried out at the washout period, weeks 1, 2, 3, and 4 and 4th week after the end of treatment. Effectiveness analysis will be per intent to treat. A multi-dimension association rule and scale-free networks method will be used to explore the effective core drug patterns.DiscussionThe effective core drug patterns will be found through analyzing several prestigious CM clinicians’ treatment information. Screening the effective core drug patterns from prestigious clinicians can accelerate the development of new CM drugs.Trial registrationNCT01613183

show abstract

“…A direct consequence of Equation 1, is that there is a relatively high likelihood to have hubs, which characterise the topological properties of the corresponding networks, as well as the way information spreads across them [11], [12]. As discussed above, an important feature of such networks refers to the fact that new nodes are created over time, which are likely to be connected to existing nodes that are already well connected.…”

Section: B Scale-free Networkmentioning

confidence: 99%

An influence assessment method based on co-occurrence for topologically reduced big data sets

Trovati

Bessis

2015

Soft Comput

View full text Add to dashboard Cite

Abstract-The extraction of meaningful, accurate, and relevant information is at the core of Big Data research. Furthermore, the ability to obtain an insight is essential in any decision making process, even though the diverse and complex nature of big datasets raises a multitude of challenges.In this paper, we propose a novel method to address the automated assessment of influence among concepts in big data sets. This is carried out by investigating their mutual cooccurrence, which is determined via topologically reducing the corresponding network. The main motivation is to provide a toolbox to classify and analyse influence properties, which can be used to investigate their dynamical and statistical behaviour, which would potentially lead to a better understanding and prediction of the properties of the system(s) they model.An evaluation was carried out on two real-world data-sets, which were analysed to test the capabilities of our system. The results show the potential of our approach, indicating both accuracy and efficiency.

show abstract

Scale-free networks

Cited by 77 publications

References 27 publications

Quantifying a Systems Map: Network Analysis of a Childhood Obesity Causal Loop Diagram

Quantifying a Systems Map: Network Analysis of a Childhood Obesity Causal Loop Diagram

Exploring effective core drug patterns in primary insomnia treatment with Chinese herbal medicine: study protocol for a randomized controlled trial

An influence assessment method based on co-occurrence for topologically reduced big data sets

Contact Info

Product

Resources

About