Suburban Road Networks to Explore COVID-19 Vulnerability and Severity.

ABSTRACTPurposeThis study examined how social network strength (i.e., the intensity and frequency of interactions within one's personal network, assessed using centrality measures such as degree, closeness, betweenness, and eigenvector centrality) affects mental health.MethodsThe study was conducted with 108 first‐year university students. Data were collected using a structured questionnaire, and NetMiner 4.0 was employed to analyze degree centrality, closeness centrality, betweenness centrality, and eigenvector centrality as indicators of social network centrality. Additionally, Quadratic Assignment Procedure (QAP) correlation analysis and QAP regression analysis were conducted to assess the relationship between social network strength and mental health.ResultsThe study found that stronger social networks were linked to better mental health in late adolescence. Specifically, higher social network centrality was associated with lower levels of hopelessness and depression. QAP correlation analysis revealed that hopelessness had a significant negative correlation with in‐closeness centrality (which reflects how easily one can be reached by others within the network) (r = −0.252, p < 0.05), while depression was negatively correlated with in‐degree centrality (the number of direct incoming connections from peers) (r = −0.233, p < 0.05), in‐closeness centrality (r = −0.256, p < 0.05), and eigenvector centrality (a measure of how well a person is connected to popular or influential members of the network) (r = −0.291, p < 0.01). QAP regression analysis further confirmed that weaker social ties, indicated by higher out‐closeness centrality (how easily one can reach others, especially those at a distance), were associated with higher depressive symptoms (adj. R2 = 0.143, F = 2.423, p < 0.05). These results suggest that building stronger and more integrated social networks may help reduce psychological distress and support mental well‐being in late adolescence.ConclusionStronger social networks in late adolescence are associated with better mental health, highlighting the importance of social connections. While correlational findings suggest general links between centrality and emotional well‐being, the regression results underscore specific predictors with practical implications. In‐degree, in‐closeness, and eigenvector centrality were associated with reduced depressive symptoms, indicating that being well‐integrated within a social network may offer protective benefits. In contrast, high out‐closeness centrality predicted increased depression, suggesting that the effort required to maintain outward‐reaching connections may impose psychological burdens. These findings suggest that targeted interventions should focus on strengthening inward social integration while addressing the stress related to maintaining extensive outward connections, providing direction for more effective youth mental health strategies.

This paper is concerned with distributed computation of several commonly used centrality measures in complex networks. In particular, we propose deterministic algorithms, which converge in finite time, for the distributed computation of the degree, closeness and betweenness centrality measures in directed graphs. Regarding eigenvector centrality, we consider the PageRank problem as its typical variant, and design distributed randomized algorithms to compute PageRank for both fixed and time-varying graphs. A key feature of the proposed algorithms is that they do not require to know the network size, which can be simultaneously estimated at every node, and that they are clock-free. To address the PageRank problem of time-varying graphs, we introduce the novel concept of persistent graph, which eliminates the effect of spamming nodes. Moreover, we prove that these algorithms converge almost surely and in the sense of $L^p$. Finally, the effectiveness of the proposed algorithms is illustrated via extensive simulations using a classical benchmark.

Network is considered naturally as a wide range of different contexts, such as biological systems, social relationships as well as various technological scenarios. Investigation of the dynamic phenomena taking place in the network, determination of the structure of the network and community and description of the interactions between various elements of the network are the key issues in network analysis. One of the huge network structure challenges is the identification of the node(s) with an outstanding structural position within the network. The popular method for doing this is to calculate a measure of centrality. We examine node centrality measures such as degree, closeness, eigenvector, Katz and subgraph centrality for undirected networks. We show how the Katz centrality can be turned into degree and eigenvector centrality by considering limiting cases. Some existing centrality measures are linked to matrix functions. We extend this idea and examine the centrality measures based on general matrix functions and in particular, the logarithmic, cosine, sine, and hyperbolic functions. We also explore the concept of generalised Katz centrality. Various experiments are conducted for different networks generated by using random graph models. The results show that the logarithmic function in particular has potential as a centrality measure. Similar results were obtained for real-world networks.

Event Abstract Back to Event The multiplex interplay between phonological and semantic networks impacts word production across different types of aphasia Nichol Castro1* and Massimo Stella2 1 Georgia Institute of Technology, United States 2 University of Southampton, United Kingdom The structure of mental lexicon networks, where similar words are connected, influences several language processes in healthy (e.g., De Deyne, Navarro, & Storms, 2013; Vitevitch, Goldstein, Siew, & Castro, 2015) and clinical (e.g., Castro, Pelczarski, & Vitevitch, 2017) populations, above and beyond traditional psycholinguistic measures. Specific to aphasia, phonological networks were found to influence picture naming performance by people with Broca’s and Wernicke’s aphasia (Vitevitch & Castro, 2015). However, these previous endeavors only examined one type of word-word similarity (e.g., phonological similarity; Vitevitch, 2008). The more recent approach of multiplex lexical networks (Stella, Beckage, & Brede, 2017; Stella, Beckage, Brede, & De Domenico, 2018) allows consideration of semantic and phonological relationships among words simultaneously. We extend the previous analysis of picture naming by people with aphasia to include the multiplex lexical network defined in Stella et al. (2018), where words are connected on four layers representing free associations, synonyms, generalizations, and phonological similarities. We focused on three network measures, namely degree, PageRank, and closeness centrality, for each word on each layer and over the entire multiplex. Degree is a local network measure equal to the number of immediate links of a node. PageRank highlights meso-scale network patterns by identifying the most important nodes in the network. Closeness centrality is a global network measure quantifying how fast information can flow from one node to all others. As multiplex generalizations of these measures we used multidegree (the sum of word degrees across layers), PageRank versatility (De Domenico, Solé-Ribalta, Omodei, Gómez, & Arenas, 2015), and multiplex closeness centrality, respectively. To assess picture naming performance, we conducted logistic regression models predicting the probability of correct picture naming from traditional psycholinguistic predictors (word length, frequency, and age of acquisition), type of aphasia (Anomic, Conduction, Wernicke, Broca, and healthy controls), and each of the network measures (layer-specific and multiplex variants) using archival data. We found that for local and meso-structural measures (i.e. degree and PageRank), the layer-specific variants were more predictive of picture naming performance than the multiplex variants. Conversely, for the global measure of closeness centrality, we found the multiplex variant being twice more predictive of picture naming performance than layer-specific variants. Furthermore, the difference in production likelihood between words with high and low multiplex closeness centrality is considerably greater for people with different types of aphasia than healthy controls, suggesting that this measure is particularly sensitive to picture naming performance in this clinical population (Fig. 1). These findings indicate that picture naming by people with aphasia crucially depends on the global interplay between phonology and semantics, which is captured by the whole multiplex structure. These findings may have particular relevance for the treatment of aphasia. Potentially, selecting words with high multiplex closeness centrality for treatment will lead to greater generalization of treatment to untrained words than when selecting words with low closeness centrality, given that words with high multiplex closeness centrality are 1) already better accessed by people with aphasia and 2) well-connected within the entire multiplex lexical network through semantic and phonological connections. Figure 1 Acknowledgements Portions of this work were made possible with a Ruth L. Kirschstein National Research Service Award (NRSA) Institutional Research Training Grant from the National Institutes of Health (National Institute on Aging) 5T32AG000175 to NC. MS acknowledges support from a Doctoral Training Centre grant (EP/G03690X/1). References Castro, N., Pelczarski, K. M., & Vitevitch, M. S. (2017). Using Network Science Measures to Predict the Lexical Decision Performance of Adults Who Stutter. Journal of Speech, Language, and Hearing Research, 60(7), 1911-1918. De Deyne, S., Navarro, D. J., & Storms, G. (2013). Better explanations of lexical and semantic cognition using networks derived from continued rather than single-word associations.Behavior research methods, 45(2), 480-498. De Domenico, M., Solé-Ribalta, A., Omodei, E., Gómez, S., & Arenas, A. (2015). Ranking in interconnected multilayer networks reveals versatile nodes. Nature communications, 6, 6868. Stella, M., Beckage, N. M., & Brede, M. (2017). Multiplex lexical networks reveal patterns in early word acquisition in children.Scientific Reports, 7, 46730. Stella, M., Beckage, N. M., Brede, M., & De Domenico, M. (2018). Multiplex model of mental lexicon reveals explosive learning in humans. Scientific reports, 8(1), 2259. Vitevitch, M. S. (2008). What can graph theory tell us about word learning and lexical retrieval?. Journal of Speech, Language, and Hearing Research, 51(2), 408-422. Vitevitch, M. S., & Castro, N. (2015). Using network science in the language sciences and clinic. International journal of speech-language pathology, 17(1), 13-25. Vitevitch, M. S., Goldstein, R., Siew, C. S., & Castro, N. (2014). Using complex networks to understand the mental lexicon. In Yearbook of the Poznan Linguistic Meeting, 1(1), 119-138. Keywords: Aphasia, multiplex lexical networks, Psycholinguistics, complex networks, picture naming, clinical applications Conference: Academy of Aphasia 56th Annual Meeting, Montreal, Canada, 21 Oct - 23 Oct, 2018. Presentation Type: poster presentation Topic: not eligible for a student prize Citation: Castro N and Stella M (2019). The multiplex interplay between phonological and semantic networks impacts word production across different types of aphasia. Conference Abstract: Academy of Aphasia 56th Annual Meeting. doi: 10.3389/conf.fnhum.2018.228.00035 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 16 Apr 2018; Published Online: 22 Jan 2019. * Correspondence: Dr. Nichol Castro, Georgia Institute of Technology, Atlanta, United States, nichol.castro@psych.gatech.edu Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Nichol Castro Massimo Stella Google Nichol Castro Massimo Stella Google Scholar Nichol Castro Massimo Stella PubMed Nichol Castro Massimo Stella Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.

We investigate the use of centrality measures to determine connected dominating sets (CDSs) for mobile ad hoc networks (MANETs) whose topology changes dynamically with time. A CDS is typically considered the graph theory equivalent for the backbone of a network and has been commonly used as the underlying topology for network-wide broadcasts with minimal retransmissions (accomplished by including fewer nodes in the CDS). In the case of MANETs, the degree centrality has been so far the most commonly used measure to determine a CDS with minimum node size (number of constituent nodes in the CDS). But, degree centrality-based CDS has been observed to be quite unstable in the presence of node mobility. In the rapidly emerging area of Network Science, centrality measures such as Eigenvector centrality, Betweenness centrality and Closeness centrality are used for complex network analysis. In this paper, we explore the use of these three centrality measures as the underlying criterion for inclusion of nodes in a CDS for MANETs and evaluate the lifetime and node size of such CDSs in comparison to that incurred for the degree centrality-based CDS and the maximum stable CDS determined using a benchmarking algorithm. We observe the Eigenvector centrality-based CDS to be the most stable (but the CDS node size is also the largest); the Betweenness centrality-based CDS is the least stable (but incurs the smallest CDS node size). The Betweenness centrality-based CDS incurs the lowest values for the CDS Node Size / CDS Lifetime tradeoff ratios when most of the nodes, if not all of them, are mobile.

Human Brain can be studied and analysed using neuroimages viz. MRI(Magnetic Resonance Imaging) / fMRI(Functional Magnetic Resonance Imaging) / PET(Positron Emission Tomography) / EEG(Electroencephalography) / MEG(Magnetoencephalography), which brings out the hidden information from it. Connectomes are graph like structure which represents complex brain network connectivity which are of two type's mainly structural connectivity and functional connectivity. Connectivity can be anatomical and functional properties of the brain. Nodes are voxels or Regions of Interest whereas edges are fibre bundles, temporal correlation between regions, in structural connectome and functional connectome respectively. This work focuses on functional connectivity analysis of brain network obtained through RS- fMRI images for identification of important regions in the human brain using image processing and graph theoretical approaches. The RS-fMRI images are obtained from 1000 Functional connectomes project and preprocessed using image processing techniques. Then the image is parcellated using AAL(Automated Anatomical Labeling) atlas and binary matrix is obtained. The graph is constructed from the derived matrix that exhibits functional connectivity between ROIs(Region of Interest). The various centrality measures (degree centrality, eigenvector centrality, betweenness centrality and closeness centrality) are used to identify the ROIs that act as provincial and/or connector hubs. The prominent provincial hubs are Rolandic Operculum, Thalamus, Insula, Hippocampus, Olfactory and connector hubs are Insula, Putamen, Occipital superior gyrus, Parietal Superior gyrus and Supramarginal gyrus. This work highlights the key regions in human brain which is involved in massive communication and information flow within the network.

Organizations strive to achieve technological competence in the current era of inevitable technological progress. One way to measure the adaptability of firms to huge technological shifts is through various parameters, including patenting activities. This study presents a method for identifying the significance of firms in an innovation network using patent citation analysis and centrality measures. Specifically, the study employs k-means clustering to classify firms into similar clusters based on network-based centrality measures such as betweenness, closeness, and eigenvector centrality. The study then develops a cluster relational network by establishing a cluster adjacency network and identifying firm positions within and between clusters. By examining the relationship between clusters, the cluster network identifies the significance of firms. The study identifies four positions, namely, leader, follower, knowledge inertia, and significantly emerging, that align with the status of firms in patenting innovation capability. The method is implemented using blockchain technology as a case study. The novelty of the study lies in the structured approach to identifying firm significance by adding another layer of adjacency network to existing patent citation analysis techniques.

Essential proteins are indispensable for cellular life. It is of great significance to identify essential proteins that can help us understand the minimal requirements for cellular life and is also very important for drug design. However, identification of essential proteins based on experimental approaches are always time-consuming and expensive. With the development of high-throughput technology in the post-genomic era, more and more protein-protein interaction data can be obtained, which make us study essential proteins from the network level become possible. There have been a series of computational approaches proposed for predicting essential proteins based on network topologies. Most of these topology based essential protein discovery methods were to use network centrality. In this paper, we investigate the essential proteins' topological characters from a completely new perspective. To our knowledge it is the first time that topology potential is used to identify essential proteins from protein-protein interaction network. The basic idea is that each protein in the network can be viewed as a material particle which creates a potential field around itself and the interaction of all proteins forms a topological field over the network. By defining and computing the value of each protein's topology potential, we can obtain a more precise ranking which reflects the importance of proteins from the protein-protein interaction network. The experiment results show that topology potential outperforms traditional topology measures: Degree Centrality (DC), Betweenness Centrality (BC), Closeness Centrality (CC), Subgraph Centrality(SC), Eigenvector Centrality(EC), Information Centrality(IC), and Sum of ECC (NC) for predicting essential proteins. In addition, these centrality measures are improved on their performance for identifying essential proteins in biological network when controlled by topology potential.

Calculating centrality measures has been used to identify Cyber-physical System critical component for some time. However, accurate identification of critical elements within the interconnected system that are vulnerable to system failures and intentional attacks requires not only a reliable model but robust index to dynamic system operating environment. These measures include 1) Node Degree, 2) Node Importance, 3) Betweenness Centrality, 4) Closeness Centrality and 5) Eigenvector Centrality. System component criticalities have been evaluated based on these measures in this paper and their performances and rankings under different network topologies and operating conditions have been compared using 1) Pearson Correlation Coefficient and 2) Spearman Correlation Coefficient. This has enabled the quantification of the optimality of using different centrality measures in different network scenarios. The results suggest that Betweenness and Degree centralities are the most suitable indices for identifying critical electrical power system bus and cyber system node individually. Moreover, the degree of correlation between directed measures has suggested also the necessity of using directional model in Cyber-physical System studies.

In social network analysis, for determining the relevance or significance of a node in the network, several node centrality measures are often used such as degree centrality, betwenness centrality, closeness centrality, eigenvector, subgraph and page rank centrality. In this paper we apply a principal components analysis over the traditional centrality measures for obtaining an overall single metric that combines the best attributes of the traditional centrality measures and permits to detect relevant nodes in the network. Concretely, a detailed study of the Spanish stocks market will be used for demonstrating the advantages of this approach.

p-means centrality

Centrality, which quantifies the importance of individual nodes, is among the most essential concepts in modern network theory. As there are many ways in which a node can be important, many different centrality measures are in use. Here, we concentrate on versions of the common betweenness and closeness centralities. The former measures the fraction of paths between pairs of nodes that go through a given node, while the latter measures an average inverse distance between a particular node and all other nodes. Both centralities only consider shortest paths (i.e., geodesics) between pairs of nodes. Here we develop a method, based on absorbing Markov chains, that enables us to continuously interpolate both of these centrality measures away from the geodesic limit and toward a limit where no restriction is placed on the length of the paths the walkers can explore. At this second limit, the interpolated betweenness and closeness centralities reduce, respectively, to the well-known current-betweenness and resistance-closeness (information) centralities. The method is tested numerically on four real networks, revealing complex changes in node centrality rankings with respect to the value of the interpolation parameter. Nonmonotonic betweenness behaviors are found to characterize nodes that lie close to intercommunity boundaries in the studied networks.

Liver disease diagnosis remains challenging due to asymptomatic early stages and limitations of traditional diagnostic methods. This paper presents a novel graph-theoretic approach combined with advanced data balancing for improved liver disease classification. We develop a patient similarity graph using cosine similarity of biochemical features, extracting five centrality measures (degree, clustering coefficient, betweenness, closeness, and eigenvector centrality) to capture relational patterns overlooked by conventional methods. The Indian Liver Patient Dataset is enhanced from 10 to 15 features through graph-based feature engineering. Class imbalance is addressed using SMOTEENN technique. An ensemble voting classifier incorporating multiple algorithms (XGBoost, LightGBM, Random Forest, etc.) is evaluated via 10-fold cross-validation. Our approach achieves superior performance with 95.74% accuracy, 94.25% precision, 99.31% recall, 96.67% F1-score, and 98.53% ROC AUC, significantly outperforming existing methods. Results demonstrate that leveraging patient relationships through graph-based features substantially enhances diagnosis accuracy.

Gene regulatory network inference is a key approach for elucidating molecular mechanisms underlying complex diseases, but accurately inferring them from high-dimensional data, especially when sample sizes are imbalanced, remains a significant challenge. Although the L1-type regularization methods have been used for gene network inference, the existing methods often fail under conditions involving high dimensionality, noise, and unequal sample sizes across phenotypes. To overcome these limitations, this study developed netRL, a novel computational framework that integrates the Random Lasso with prior network biological knowledge. The proposed method leveraged a bootstrap-based strategy to stabilize the selection of key regulatory genes and incorporates network-informed penalization using centrality measures (i.e., hubness and betweenness centrality). This study also introduced a statistical strategy using a hypergeometric test to assess the significance of the inferred edges, thereby enhancing the reliability of the network. Through extensive simulation studies, this study demonstrated that netRL outperforms conventional methods in both network estimation and gene selection. Applying netRL to whole-blood RNA-seq profiles from the Japan COVID-19 Task Force, this study successfully identified distinct phenotype-specific molecular interplays between asymptomatic and critical cases despite pronounced sample imbalance. The findings reveal that asymptomatic networks were dense and enriched for ribosomal proteins, whereas critical networks were sparse, centralized, and characterized by hub genes such as NFKBIA, B2M, CXCL8, and FOS. Pathway enrichment further revealed phenotype-specific biological processes, highlighting molecular signatures of disease progression. The results of this study suggest that enhancing the activity of asymptomatic condition-specific markers (e.g., ribosomal proteins) may provide important insights into the molecular mechanisms underlying COVID-19 severity. Collectively, these results demonstrate that netRL enables biologically interpretable and statistically robust network inference, offering new insights into the molecular basis of COVID-19 severity and broader applications in systems biology.

오피니언 마이닝과 네트워크 분석을 활용한 상품 커뮤니티 분석: 영화 흥행성과 예측 사례