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Structure-based influential nodes identification is a long-term challenge in the study of complex networks. While global centrality-based approaches are generally considered to be more accurate and reliable, the requirements of complete network information and high computational complexity are hard to meet, limiting their applications in many practical scenarios. In addition, recent studies have highlighted the effect of cyclic structures introducing redundant paths in network connectivity and exaggerating the importance of traditional centrality measures. In this work, we develop a new centrality metric, called Multi-Spanning Tree-based Degree Centrality (MSTDC), to quantify node importance with linear complexity by leveraging redundant ties. MSTDC is calculated using the aggregation of degrees of a small number of spanning trees constructed with a few randomly selected root nodes. Experiments on synthetic and empirical networks reveal that MSTDC obtains superior performance than other benchmark network centralities in identifying influential nodes from the perspective of both maintaining network connectivity and maximizing spreading capacity. In addition, we find that MSTDC is extraordinarily effective in networks with high clustering coefficients. Our study provides novel insights into the role of redundant ties in network structural and functional analyses. • A novel centrality metric considering link redundancy is developed to identify influential nodes in complex networks. • The proposed metric, MSTDC, is superior in identifying influential nodes compared to other methods. • This study provides novel insights for considering the role of link redundancy by constructing spanning trees. • MSTDC has low computational complexity and is of potential to be applied to other network-related tasks. [ABSTRACT FROM AUTHOR]

Regulations that govern oversight of seclusion and/or restraint episodes (SREs) in the United States are relatively uniform and may assume that such events are normally distributed within the population generating them. This study illustrates that the distribution of patients who required one or more SREs within one state psychiatric hospital setting is heavy-tailed--that is, a small group of patients generated a disproportionate majority of the events: 20 percent of patients with the most SREs accounted for approximately 75 percent of the total number of SREs; l0 percent of patients accounted for 61 percent, and I percent of patients accounted for 21 percent. Characteristic features of heavy-tailed distributions are described and discussed in relation to the feasibility of eliminating SREs in mental health settings and the governance of SREs by uniform regulations. Attempts are made to model and subtype the distribution, and commentary is made as to potential clinical and policy relevance of the findings. [ABSTRACT FROM AUTHOR]

Misreporting is a common source of bias in population surveys involving sensitive topics such as sexual behaviours, abortion or criminal activity. To protect their privacy due to stigmatized or illegal behaviour, respondents tend to avoid fully disclosure of personal information deemed sensitive. This attitude however may compromise the results of survey studies. To circumvent this limitation, this article proposes a novel ego-centric sampling method (ECM) based on the respondent’s peer networks to make indirect inferences on sensitive traits anonymously. Other than asking the respondents to report directly on their own behaviour, ECM takes into account the knowledge the respondents have about their social contacts in the target population. By using various scenarios and sensitive analysis on model and real populations, we show the high performance, that is low biases, that can be achieved using our method and the novel estimator. The method is also applied on a real-world survey to study traits of college students. This real-world exercise illustrates that the method is easy-to-implement, requiring few amendments to standard sampling protocols, and provides a high level of confidence on privacy among respondents. The exercise revealed that students tend to under-report their own sensitive and stigmatized traits, such as their sexual orientation. Little or no difference was observed in reporting non-sensitive traits. Altogether, our results indicate that ECM is a promising method able to encourage survey participation and reduce bias due to misreporting of sensitive traits through indirect and anonymous data collection.