Identifying influential actors within social networks is pivotal for optimizing information flow and mitigating the spread of both rumors and diseases. Several methods have emerged to pinpoint these influential entities in networks that are represented as graphs. In these graphs, nodes corre-spond to individuals and edges indicate their connections. This study focuses on centrality measures, prized for their straightforwardness and effectiveness. We categorize structural central-ity into two: local, considering a node's immediate vicinity, and global, accounting for overarching path structures. Some techniques blend both centralities to highlight nodes influential at both mi-cro and macro levels. Our paper presents a novel centrality measure, accentuating node degree and incorporating the network's broader features, especially paths of different lengths. Through Spearman and Pearson's correlations tested on seven standard datasets, our method proves its merit against traditional centrality measures. Additionally, we employ the SIR model, portraying disease spread, to further validate our approach. The ultimate influential node is gauged by its capacity to infect the most nodes during the SIR model's progression. Our results indicate a nota-ble correlative efficacy across various real-world networks relative to other centrality metrics.

This article combines the traditional definition of portfolio risk with minimum spanning tree based “interconnectedness risk" to improve the equal risk contribution portfolio performance. We use betweenness centrality to measure an asset’s importance in a market graph (network). After filtering the complete correlation network to a minimum spanning tree, we calculate the centrality score and convert it to a centrality heuristic. We develop an adjusted variance-covariance matrix using the centrality heuristic, to bias the model to assign peripheral assets in the minimum spanning tree higher weights. We test this methodology using the constituents of the S&P 100 index. The results show that the centrality equal risk portfolio can improve upon the base equal risk portfolio returns, with a similar level of risk. We observe that during bear markets, the centrality-based portfolio can surpass the base equal risk portfolio risk.

In an urban context, the street network can be considered the most substantial element, as its role is to create the whole urban setting. The street network is a spatial pattern that can be recognized as a linkage bonding other components of the urban context, such as buildings and open spaces. Basically, the street is not only a collection of lines that transfer people, vehicles and goods but also denote a broad range of activities and influences people’s responses. In this respect, some studies address the street as a network that entails two key elements, link and node; the link can be defined by the type of mechanism that it connects, while the node can be identified according to the analysis technique. Defining the street’s elements is a crucial step in ascertaining the expression of whatever is a link or/and a node. Thus, some scholars use the link to refer to a street, and node for the intersection, while others employ the opposite meaning. Two different viewpoints deal with spatial network analysis; the first is a technique based on topological depth, such as Space Syntax SS, and the second is Multiple Centrality Assessment (MCA) that is employed for metric analysis. This paper compares Space Syntax (SS) and Multiple Centrality Assessment (MCA) by measuring the integration and centrality of the street, and highlights the points of similarity and difference between the two techniques.

Microbial biofilms cause several environmental and industrial issues, even on the human health. Although they have represented a threaten due to their resistance to antibiotics, there are currently no approved antibiofilm agents for clinical treatments. The multi-functionality of antimicrobial peptides (AMPs) including the antibiofilm activity and their potentialities to target multiple mi-crobes motivated the synthesis of AMP relatives for developing antibiofilm agents for clinical purposes. Antibiofilm peptides (ABFPs) have been organized in databases that allowed the building of prediction tools which have assisted in the discovery/design of new antibiofilm agents. However, the complex network approach has been explored yet as an assistant tool for this aim. Herein, a kind of similarity networks, called the Half-Space Proximal Network (HSPN) is applied to represent/analyse the chemical space of the ABFPs aimed to identify promising scaf-folds for the development of next generation antimicrobials, able to target both planktonic and biofilm microbial forms. Such analyses also considered the metadata associated to the ABFPs such as origin, other activities, targets, etc. in which the relationships were projected by multilayer networks called metadata networks (METNs). From the complex networks mining, a reduced but informative set of 66 ABFPs representing the original antibiofilm space was extracted. This subset retained from the most central to atypical ABFPs, having some of them, desired properties for developing next generation antimicrobials. So, this subset is advisable for assisting the search/design both new antibiofilm/antimicrobial agents. The provided ABFP motifs list, dis-covered within the HSPN communities, is also useful for the same purpose.

Pump Sizing is the process of dimensional matching an impeller and stator to provide a satisfactory performance test result and good service life during the operation of progressive cavity pumps. In this process, historical data analysis and dimensional monitoring are done manually, consuming a large amount of man-hours and requiring a deep knowledge of progressive cavity pump behavior. This work presents the use of complex networks in the construction of a prototype to recommend interference during the Pump Sizing process in a Progressive Cavity Pump. For this, data from different expert applications were used in addition to individual control Excel spreadsheets to build the database used in the prototype. From the pre-processed data, complex network techniques and the Betweenness Centrality metric were used to calculate the degree of importance of each order confirmation, as well as to calculate the dimensionality of the rotors. The model was evaluated using the mean squared error (MSE), obtaining a MSE of 0.28 for the cases where there were recommendations for order confirmations. Based on the results obtained, it was realized that there is a similarity with the dimensional defined by design engineers during the Pump Sizing process.

Investigating the network response to node removal and the efficacy of the node removal strategies are related and fundamental questions in network science. Research studies proposed many node centralities based on the network structure ranking node to remove. The random walk (RW) on networks describes a stochastic process in which a walker travels among nodes. RW can be a model of transport, diffusion, and search on networks, and an essential tool for studying the importance of network nodes. In this manuscript, we propose four new measures of node centrality based on RW. Then, we compare the efficacy of the new RW node removal strategies to network dismantle with effective node removal strategies from the literature, such as betweenness and closeness node removal over synthetic and real-world networks. We evaluate the network dismantle along node removal using the size of the largest connected component (LCC). We find that, hence the betweenness nodes attack is the best strategy overall, the new node removal strategies based on RW show the highest efficacy in peculiar network topology. Specifically, RW strategies based on covering times emerge as the most effective strategy on a synthetic lattice network and two real-world road networks. Our results may be useful in selecting the best node attack strategies in a specific class of networks and in building more robust network structures.

Drought is one of the most serious abiotic stressors in the environment, restricting agricultural production by reducing plant growth, development, and productivity. To investigate such a complex and multifaceted stressor and its effects on the plants, a systems biology-based approach is necessitated, entailing the generation of co-expression networks, identification of highly-priority transcription factors (TFs) dynamic mathematical modeling, and computational simulations. Here, we studied a high-resolution drought transcriptome of Arabidopsis. We revealed distinct temporal transcriptional signatures and demonstrated the involvement of specific biological pathways. Generation of a large-scale co-expression network followed by network centrality analyses identified 117 TFs that possess critical properties of hubs, bottlenecks, and high clustering coefficients nodes. Dynamic transcriptional regulatory modeling on integrated TF-target and transcriptome datasets uncovered major transcriptional events during the course of drought stress. Mathematical transcriptional simulations allowed us to ascertain the activation status of major TFs as well as the transcriptional intensity and amplitude of their target genes. Finally, we validated our predictions by providing experimental evidence of gene expression under drought stress for a set of four TFs and their major target genes using qRT-PCR. Taken together, we provided a systems-level perspective on the dynamic transcriptional regulation during drought stress in Arabidopsis and uncovered numerous novel TFs that can potentially be used in future genetic crop engineering programs.

Transverse momentum spectra of +, p, Λ, Ξ or ¯Ξ+, Ω or ¯Ω+ and deuteron (d) in different centrality intervals in Gold-Gold (Au-Au) and Lead-Lead (Pb-Pb) collisions at 62.4 GeV and 2.76 TeV respectively, are analyzed by the blast wave model with Boltzmann Gibbs statistics. We extracted Kinetic freeze out temperature,transverse flow velocity and kinetic freezeout volume from the transverse momentum spectra of the particles. It is observed that the non-strange and strange (multi-strange) particles freezeout separately due to different reaction cross-sections. While the freezeout volume and transverse flow velocity are mass dependent which decrease with the rest mass of the particles. The present work reveals the scenario of double kinetic freezeout in collisions at STAR and ALICE Collaborations. Furthermore, the kinetic freezeout temperature and freezeout volume are larger in central collisions than the peripheral collisions and they decrease from central to periphery. However the transverse flow velocity remains almost unchanged from central to peripheral collisions.

Peptide-based drugs are promising anticancer candidates due to their biocompatibility, and low toxicity. Particularly, tumor homing peptides (THPs) have the ability to bind specifically to can-cer cells receptors and tumor vasculature. Despite their potential to develop antitumor drugs, there are few available prediction tools to assist the discovery of new THPs. Two webservers based on machine learning models are currently active, the TumorHPD (https://webs.iiitd.edu.in/raghava/tumorhpd) and the THPep (http://codes.bio/thpep), and more recently the SCMTHP (SCMTHP (pmlabstack.pythonanywhere.com), based on scoring card method. Herein, a novel method based on network science and similarity searching implemented in the starPep toolbox (http://mobiosd-hub.com/starpep/) is presented for THPs discovery. The approach leverages from exploring the structural space of THPs with Chemical Space Networks (CSNs) and from applying centrality measures to identify the most relevant and non-redundant THPs sequences within the CSN. Such THPs were considered as queries (Qs) for multi-query similarity searches that applies a group fusion (MAX-SIM rule) model. The resulting multi-query similarity searching models (SSMs) were validated with three benchmarking datasets of THPs/non-THPs. Predictions achieved accuracies ranged from 92.64 to 99.18% and Matthews Correlation Coefficients between 0.894-0.98, outperforming state-of-the-art predictors. The best model was applied to repurpose AMPs from the starPep database as THPs, which were subse-quently optimized for the TH activity. Finally, 54 promising THP leads were discovered, and their sequences were analyzed to encounter novel motifs. These results demonstrate the potential of CSNs and multi-query similarity searching for a rapid and accurate identification of THPs.