A Grade Identification Method of Critical Node in Urban Road Network Based on Multi-Attribute Evaluation Correction

Accurately identifying the key nodes of the road network and focusing on its management and control is an important means to improve the robustness and invulnerability of the road network. In this paper, a classification and identification method of key nodes in urban road networks based on multi-attribute evaluation and modification was proposed. Firstly, the emergency function guarantee grade of road network nodes was divided by comprehensively considering the importance of road network nodes, the consequences of failure, and the degree of difficulty of recovery. The evaluation indexes were selected according to the local attributes, global attributes, and functional attributes of the road network topology. The spatial distribution patterns of the evaluation indexes of the nodes were analyzed. The dynamic classification method was used to cluster the attributes of the road network nodes, and the TOPSIS method was used to comprehensively evaluate the importance ranking of the road network nodes. Attribute clustering of road network nodes by dynamic classification method (DT) and the TOPSIS method was used to comprehensively evaluate the ranking of the importance of road network nodes. Then, combined with the modification of the comprehensive evaluation and ranking of the importance of the road network nodes, the emergency function support classification results of the road network nodes were obtained. Finally, the method was applied to the road network within the second Ring Road of Beijing. It was compared with the clustering method of self-organizing competitive neural networks. The results show that this method can identify the key nodes of the road network more accurately. The first-grade key nodes are all located at the more important intersections on expressways and trunk roads. The spatial distribution pattern shows a “center-edge” pattern, and the important traffic corridors of the road network show a “five vertical and five horizontal” pattern.

Analysis of Identification Methods of Key Nodes in Transportation Network

The identification of key nodes plays an important role in improving the robustness of the transportation network. For different types of transportation networks, the effect of the same identification method may be different. It is of practical significance to study the key nodes identification methods corresponding to various types of transportation networks. Based on the knowledge of complex networks, the metro networks and the bus networks are selected as the objects, and the key nodes are identified by the node degree identification method, the neighbor node degree identification method, the weighted k-shell degree neighborhood identification method (KSD), the degree k-shell identification method (DKS), and the degree k-shell neighborhood identification method (DKSN). Take the network efficiency and the largest connected subgraph as the effective indicators. The results show that the KSD identification method that comprehensively considers the elements has the best recognition effect and has certain practical significance.

3MCor: an integrative web server for metabolome-microbiome-metadata correlation analysis

Motivation The metabolome and microbiome disorders are highly associated with human health and there are great demands for dual-omics interaction analysis. Here, we designed and developed an integrative platform, 3MCor, for metabolome and microbiome correlation analysis under the instruction of phenotype and with the consideration of confounders. Results Many traditional and novel correlation analysis methods were integrated for intra- and inter-correlation analysis. Three inter-correlation pipelines are provided for global, hierarchical, and pairwise analysis. The incorporated network analysis function is conducive to rapid identification of network clusters and key nodes from a complicated correlation network. Complete numerical results (csv files) and rich figures (pdf files) will be generated in minutes. To our knowledge, 3MCor is the first platform developed specifically for the correlation analysis of metabolome and microbiome. Its functions were compared with corresponding modules of existing omics data analysis platforms. A real-world data set was used to demonstrate its simple and flexible operation, comprehensive outputs, and distinctive contribution to dual-omics studies. Availability 3MCor is available at http://3mcor.cn and the backend R script is available at https://github.com/chentianlu/3MCorServer. Supplementary information Supplementary data are available at Bioinformatics online.

Spread of Online Public Opinion of Animal Epidemic Emergency: A Case Study of the H7N9 Incident Based on Healthcare Data Analytics

Qualitative and quantitative analysis of online public opinion of the H7N9 animal epidemic incident in 2013 were conducted based on social network analysis (SNA) theories, SNA method, and life cycle theories. Trend and features of evolution of online public opinion of animal epidemic emergency were explored and structural characteristics of key nodes in these public opinion spreading networks were identified. The stages of spreading of public opinion of animal epidemics were investigated. This study provides references for the government to cope with online public opinion of animal epidemic emergencies in the future.

IKN-CF: An Approach to Identify Key Nodes in Inter-Domain Routing Systems Based on Cascading Failures

Inter-domain routing systems is an important complex network in the Internet. Research on the vulnerability of inter-domain routing network nodes is of great support to the stable operation of the Internet. For the problem of node vulnerability, we proposed a method for identifying key nodes in inter-domain routing systems based on cascading failures (IKN-CF). Firstly, we analyzed the topology of inter-domain routing network and proposed an optimal valid path discovery algorithm considering business relationships. Then, the reason and propagation mechanism of cascading failure in the inter-domain routing network were analyzed, and we proposed two cascading indicators, which can approximate the impact of node failure on the network. After that, we established a key node identification model based on improved entropy weight TOPSIS (EWT), and the key node sequence in the network can be obtained through EWT calculation. We compared the existing three methods in two real inter-domain routing networks. The results indicate that the ranking results of IKN-CF are high accuracy, strong stability, and wide applicability. The accuracy of the top 100 nodes of the ranking result can reach 83.6%, which is at least 12.8% higher than the average accuracy of the existing three methods.

Key Node Discovery Algorithm Based on Multiple Relationships and Multiple Features in Social Networks

The key nodes play important roles in the processes of information propagation and opinion evolution in social networks. Previous work rarely considered multiple relationships and features into key node discovery algorithms at the same time. Based on the relational networks including the forwarding network, replying network, and mentioning network in a social network, this paper first proposes an algorithm of the overlapping user relational network to extract different relational networks with same nodes. Integrated with these relational networks, a multirelationship network is established. Subsequently, a key node discovery (KND) algorithm is presented on the basis of the shortest path, degree centrality, and random walk features in the multirelationship network. The advantages of the proposed KND algorithm are proved by the SIR propagation model and the normalized discounted cumulative gain on the multirelationship networks and single-relation networks. The experiment’s results show that the proposed KND method for finding the key nodes is superior to other baseline methods on different networks.

Distribution Network for the Last Mile of Cross-Border E-business in a Smart City at Emerging Market in Response to COVID-19: A Key Node Analysis Based on a Vision of Fourth Party Logistics

This research studies the development of distribution networks for the last mile distribution for cross-border E-business based on a vision of fourth party logistics (4PL) in smart cities in emerging markets in response to COVID-19. This research analyzes the distribution centers of distribution companies in Beijing city using fuzzy cluster analysis as a case study of smart cities. The location decision for distribution centers to serve cross-border E-business is further analyzed by considering the local conditions of the distribution centers. The solutions to the location decisions for distribution centers in different cases are further visualized by 2-mode networks. The key nodes in the distribution network of the last mile for cross-border E-business are further studied based on fourth-party logistics by a immune algorithm. Cross-border E-business value creation based on the development of distribution networks using fourth-party logistics is further discussed. The location distribution of key nodes can spread from the downtown district to suburban areas as the coverage of the distribution network is expanded. This research can help managers and decision makers address the last mile distribution for cross-border E-business in smart cities in emerging markets based on a vision of fourth-party logistics in response to COVID-19.

Encoding and retrieval of associative recognition memory engage different sub-networks within a hippocampal-thalamo-cortical memory circuit

Recognition of previously encountered stimuli and their associated spatial and temporal information depends on neural activity within a brain-wide network in which the CA1 region of the hippocampus, nucleus reuniens of the thalamus (NRe) and medial prefrontal cortex (mPFC) are key nodes. However, the pathways crucial for coordinating activity during memory encoding and/or retrieval phases have been little explored. Here we opto- or chemo associative recognition memory. We discovered that encoding, but not retrieval depended on the CA1 to mPFC and NRe to mPFC projections. In contrast, retrieval depended on the mPFC to NRe projection. Interestingly the NRe to CA1 pathway was required for both memory phases. Our findings therefore reveal that encoding and retrieval engage dissociable sub-networks within a hippocampal-thalamo-cortical recognition memory circuit in order to enable binding of recent and related information, whilst ensuring a separation of processing.

Hierarchy and control of ageing-related methylation networks

DNA methylation provides one of the most widely studied biomarkers of ageing. Since the methylation of CpG dinucleotides function as switches in cellular mechanisms, it is plausible to assume that by proper adjustment of these switches age may be tuned. Though, adjusting hundreds of CpG methylation levels coherently may never be feasible and changing just a few positions may lead to biologically unstable state. A prominent example of methylation-based age estimators is provided by Horvath’s clock, based on 353 CpG dinucleotides, showing a high correlation (not necessarily causation) with chronological age across multiple tissue types. On this small subset of CpG dinucleotides we demonstrate how the adjustment of one methylation level leads to a cascade of changes at other sites. Among the studied subset, we locate the most important CpGs (and related genes) that may have a large influence on the rest of the sub-system. According to our analysis, the structure of this network is way more hierarchical compared to what one would expect based on ensembles of uncorrelated connections. Therefore, only a handful of CpGs is enough to modify the system towards a desired state. When propagation of the change over the network is taken into account, the resulting modification in the predicted age can be significantly larger compared to the effect of isolated CpG perturbations. By adjusting the most influential single CpG site and following the propagation of methylation level changes we can reach up to 5.74 years in virtual age reduction, significantly larger than without taking into account of the network control. Extending our approach to the whole methylation network may identify key nodes that have controller role in the ageing process.