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Nine exported cases of 2019-nCoV infection have been reported in Thailand, Japan, Korea, the USA, Vietnam, and Singapore to date, and further dissemination through air travel is likely.1–5 As of Jan 23, 2020, confirmed cases were consecutively reported in 32 provinces, municipalities, and special administrative regions in China, including Hong Kong, Macau, and Taiwan.3 These cases detected outside Wuhan, together with the detection of infection in at least one household cluster—reported by Jasper Fuk-Woo Chan and colleagues6 in The Lancet—and the recently documented infections in health-care workers caring for patients with 2019-nCoV indicate human-to-human transmission and thus the risk of much wider spread of the disease. An efficient system is ready for monitoring and responding to infectious disease outbreaks and the 2019-nCoV pneumonia has been quickly added to the Notifiable Communicable Disease List and given the highest priority by Chinese health authorities. [...]the 2019-nCoV outbreak has led to implementation of extraordinary public health measures to reduce further spread of the virus within China and elsewhere. Unfortunately, 16 health-care workers, some of whom were working in the same ward, have been confirmed to be infected with 2019-nCoV to date, although the routes of transmission and the possible role of so-called super-spreaders remain to be clarified.9 Epidemiological studies need to be done to assess risk factors for infection in health-care personnel and quantify potential subclinical or asymptomatic infections.

The dynamics and influence of fake news on Twitter during the 2016 US presidential election remains to be clarified. Here, we use a dataset of 171 million tweets in the five months preceding the election day to identify 30 million tweets, from 2.2 million users, which contain a link to news outlets. Based on a classification of news outlets curated by www.opensources.co , we find that 25% of these tweets spread either fake or extremely biased news. We characterize the networks of information flow to find the most influential spreaders of fake and traditional news and use causal modeling to uncover how fake news influenced the presidential election. We find that, while top influencers spreading traditional center and left leaning news largely influence the activity of Clinton supporters, this causality is reversed for the fake news: the activity of Trump supporters influences the dynamics of the top fake news spreaders. The influence of 'fake news’, spread via social media, has been much discussed in the context of the 2016 US presidential election. Here, the authors use data on 30 million tweets to show how content classified as fake news diffused on Twitter before the election.

We examine a general stochastic rumor model characterized by specific parameters that govern the interaction rates among individuals. Our model includes the \\((\\alpha, p)\\)-probability variants of the well-known Daley--Kendall and Maki--Thompson models. In these variants, a spreader involved in an interaction attempts to transmit the rumor with probability \\(p\\); if successful, any spreader encountering an individual already informed of the rumor has probability \\(\\alpha\\) of becoming a stifler. We prove that the maximum proportion of spreaders throughout the process converges almost surely, as the population size approaches~\\(\\infty\\). For both the classical Daley--Kendall and Maki--Thompson models, the asymptotic proportion of the rumor peak is \\(1 - \\log 2 \\approx 0.3069\\).

Identifying key spreaders in a network is one of the fundamental problems in the field of complex network research, and accurately identifying influential propagators in a network holds significant practical implications. In recent years, numerous effective methods have been proposed and widely applied. However, many of these methods still have certain limitations. For instance, some methods rely solely on the global position information of nodes to assess their propagation influence, disregarding local node information. Additionally, certain methods do not consider clustering coefficients, which are essential attributes of nodes. Inspired by the quality formula, this paper introduces a method called Structural Neighborhood Centrality (SNC) that takes into account the neighborhood information of nodes. SNC measures the propagation power of nodes based on first and second-order neighborhood degrees, local clustering coefficients, structural hole constraints, and other information, resulting in higher accuracy. A series of pertinent experiments conducted on 12 real-world datasets demonstrate that, in terms of accuracy, SNC outperforms methods like CycleRatio and KSGC. Additionally, SNC demonstrates heightened monotonicity, enabling it to distinguish subtle differences between nodes. Furthermore, when it comes to identifying the most influential Top-k nodes, SNC also displays superior capabilities compared to the aforementioned methods. Finally, we conduct a detailed analysis of SNC and discuss its advantages and limitations.

Spreaders detection is a vital issue in complex networks because spreaders can spread information to a massive number of nodes in the network. There are many centrality measures to rank nodes based on their ability to spread information. Some local and global centrality measures including DIL, degree centrality, closeness centrality, betweenness centrality, eigenvector centrality, PageRank centrality and k-shell decomposition method are used to identify spreader nodes. However, they may have some problems such as finding inappropriate spreaders, unreliable spreader detection, higher time complexity or incompatibility with some networks. In this paper, a new local ranking measure is proposed to identify the influence of a node. The proposed method measures the spreading ability of nodes based on their important location parameters such as node degree, the degree of its neighbors, common links between a node and its neighbors and inverse cluster coefficient. The main advantage of the proposed method is to clear important hubs and low-degree bridges in an efficient manner. To test the efficiency of the proposed method, experiments are conducted on eight real and four synthetic networks. Comparisons based on Susceptible Infected Recovered and Susceptible Infected models reveal that the proposed method outperforms the compared well-known centralities.

The network, with some or all characteristics of scale-free, self-similarity, self-organization, attractor and small world, is defined as a complex network. The identification of significant spreaders is an indispensable research direction in complex networks, which aims to discover nodes that play a crucial role in the structure and function of the network. Since influencers are essential for studying the security of the network and controlling the propagation process of the network, their assessment methods are of great significance and practical value to solve many problems. However, how to effectively combine global information with local information is still an open problem. To solve this problem, the generalized mechanics model is further improved in this paper. A generalized mechanics model based on information entropy is proposed to discover crucial spreaders in complex networks. The influence of each neighbor node on local information is quantified by information entropy, and the interaction between each node on global information is considered by calculating the shortest distance. Extensive tests on eleven real networks indicate the proposed approach is much faster and more precise than traditional ways and state-of-the-art benchmarks. At the same time, it is effective to use our approach to identify influencers in complex networks.

Identifying influential spreaders has theoretical and practical significance in complex networks. Traditional centrality methods can efficiently find a single spreader, but it could lead to influence redundancy and high initializing costs when used to identify a set of multiple spreaders. A cycle structure is one of the most crucial reasons for the complexity of a network and the cornerstone of the feedback effect. From this novel perspective, we propose a new method based on basic cycles in networks to identify multiple influential spreaders with superior spreading performance and low initializing costs. Experiments on six empirical networks show that the spreaders selected by the proposed method are more scattered in the network and yield the best spreading performance compared with those on seven well-known methods. Importantly, the proposed method is the most cost effective under the same spreading performance. The cycle-based method has the advantage of generating multiple solutions. Our work provides new insights into identifying multiple spreaders and hence can benefit wide applications in practical scenarios.

Influential nodes are the important nodes that most efficiently control the propagation process throughout the network. Among various structural-based methods, degree centrality, k-shell decomposition, or their combination identify influential nodes with relatively low computational complexity, making them suitable for large-scale network analysis. However, these methods do not necessarily explore nodes’ underlying structure and neighboring information, which poses a significant challenge for researchers in developing timely and efficient heuristics considering appropriate network characteristics. In this study, we propose a new method (IC-SNI) to measure the influential capability of the nodes. IC-SNI minimizes the loopholes of the local and global centrality and calculates the topological positional structure by considering the local and global contribution of the neighbors. Exploring the path structural information, we introduce two new measurements (connectivity strength and effective distance) to capture the structural properties among the neighboring nodes. Finally, the influential capability of a node is calculated by aggregating the structural and neighboring information of up to two-hop neighboring nodes. Evaluated on nine benchmark datasets, IC-SNI demonstrates superior performance with the highest average ranking correlation of 0.813 with the SIR simulator and a 34.1% improvement comparing state-of-the-art methods in identifying influential spreaders. The results show that IC-SNI efficiently identifies the influential spreaders in diverse real networks by accurately integrating structural and neighboring information.

Introduction The use of either an open or closed approach in rhinoplasty has been a topic of debate. Widening the narrow internal nasal valve area through internal nasal valve augmentation presents a strong case for an open approach. However, this study presents a direct method for fixing spreader grafts using a closed approach, which offers the benefits of precise placement and stability without an external scar. Materials and methods Nine patients with functional and/or cosmetic complaints that need spreader grafts insertion were enrolled in the study. Spreader grafts placed and fixed by stitches using a closed rhinoplasty approach with additional esthetic steps added as necessary. Results Valve collapse was secondary in 2 cases, and the spreader graft was inserted bilaterally in all but one patient. Their complaint was either functional (44%), cosmetic (22%), or both (33%). NOSE scores showed improvement in all patients with a mean operative time was (68.68 ± 12.45 SD) minutes and postoperative complications were acceptably low. Conclusion In this report, we describe fixation of spreader graft through a closed technique with visualization of the entire septal cartilage frame. This method combines the advantages of direct manipulation and fixation with all the benefits of a closed approach.

The study of influential spreaders has become a growing area of interest within network sciences due to its critical implications in understanding the robustness and vulnerability of complex networks. There is a significant degree of focus on the factors that dictate the decision-making process for identifying these influential spreaders in highly complex networks, given their crucial role in network performance and security. Previous research methodologies have offered a deep understanding of the importance of spreaders, also referred to as nodes. These methods, however, have primarily depended on either neighborhood or path information to identify these spreaders. They have often studied local network data, or adopted a more broad-based, global view of the network. Such an approach may not provide a comprehensive understanding of the overall network structure and the relationships between nodes. Addressing this limitation, our research introduces Neighborhood and Path Information-based Centrality (NPIC) algorithm. This innovative centrality algorithm combines both neighborhood and path information to identify influential spreaders in a complex network. By incorporating these two significant aspects, NPIC provides a more holistic analysis of network centrality, enabling a more accurate identification of influential spreaders. We have subjected NPIC to rigorous testing using numerous simulations on both real and artificially-created datasets. These simulations applied an epidemic model to calculate the spreading efficiency of each node within its given environment. Our simulations, conducted across a wide range of synthetic and real-world datasets, demonstrated that NPIC outperforms existing methodologies in identifying influential spreaders in corresponding networks.