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The Central Plains Urban Agglomeration (CPUA) is a link of economic development to connect the eastern part and western part in China. Current research on urban agglomeration integration mainly uses urban properties’ data. This paper conducts an evaluation method for urban agglomeration integration based on multivariate big data. This method mainly applies eigenvector centrality to assess the integrated situation of the CPUA from four dimensions (internet connection, industrial economy, public service and coordinated governance). The data of this research mainly includes cell phone signaling data, Baidu index, industrial investment data and statistical data for planning. The main innovations and contributions of this research is that (i) on the theoretical aspect, this research proposed an index indicator evaluation system for integration of urban agglomerations based on Analytic Hierarchy Process (AHP) and expert rating. It contributes for the further development of regional integration and other related theories; (ii) on the practical aspect, this study, taking the CPUA as example, presents an assessment approach that uses multivariate big data to measure the current integrated situation of urban agglomeration. This method provides decision-making support for the development of urban agglomeration integration.

This study examines the internal migration patterns among Mexico's Indigenous population from 1990 to 2020. We begin by estimating the total interstate migration flows for Indigenous groups and employ an advanced interaction component model to identify migration flows that exceed expectations. This model allows us to detect significant deviations and patterns within the migration data. Additionally, we apply network analysis techniques to identify states that are central to these migration flows and categorize states into distinct communities based on their migration interactions. Our findings reveal that Indigenous migration exhibits higher‐than‐expected flows, particularly from the West Central and North regions of Mexico. By contrast, non‐Indigenous migration shows greater flows, predominantly in the southern and central states. Through network analysis, particularly the use of eigenvector centrality, we identify Nayarit and Durango as key hubs for Indigenous migration, whereas Estado de Mexico and Ciudad de Mexico emerge as central nodes in non‐Indigenous migration. Our study highlights the growing significance of Mexico's northern region, with Nuevo León playing a crucial role in Indigenous and non‐Indigenous migration flow networks. This study's findings contribute valuable insights regarding the spatial dynamics of internal migration and the evolving migration patterns of Indigenous populations in Mexico. [ABSTRACT FROM AUTHOR]

Exploring drought stress-responsive genes in rice is essential for breeding drought-resistant varieties. Rice drought resistance is controlled by multiple genes, and mining drought stress-responsive genes solely based on single omics data lacks stability and accuracy. Multi-omics correlation analysis and biological molecular network analysis provide robust solutions. This study proposed a random walk with a multi-restart probability (RWMRP) algorithm, based on the Restarted Random Walk (RWR) algorithm, to operate on rice MultiPlex biological networks. It explores the interactions between biological molecules across various levels and ranks potential genes. RWMRP uses eigenvector centrality to evaluate node importance in the network and adjusts the restart probabilities accordingly, diverging from the uniform restart probability employed in RWR. In the random walk process, it can be better to consider the global relationships in the network. Firstly, we constructed a MultiPlex biological network by integrating the rice protein–protein interaction, gene pathway, and gene co-expression network. Then, we employed RWMRP to predict the potential genes associated with rice tolerance to drought stress. Enrichment and correlation analyses resulted in the identification of 12 drought-related genes. We further conducted quantitative real-time polymerase chain reaction (qRT-PCR) analysis on these 12 genes, ultimately identifying 10 genes responsive to drought stress. [ABSTRACT FROM AUTHOR]

ObjectiveTo describe the current state of research and future research hotspots through a metrological analysis of the literature in the field of forensic anthropological remains identification research.MethodsThe data retrieved and extracted from the Web of Science Core Collection （WoSCC）， the core database of the Web of Science information service platform （hereinafter referred to as “WoS”）， was used to analyze the trends and topic changes in research on forensic identification of human remains from 1991 to 2022. Network visualisation of publication trends, countries （regions）, institutions, authors and topics related to the identification of remains in forensic anthropology was analysed using python 3.9.2 and Gephi 0.10.ResultsA total of 873 papers written in English in the field of forensic anthropological remains identification research were obtained. The journal with the largest number of publications was Forensic Science International （164 articles）. The country （region） with the largest number of published papers was China （90 articles）. Katholieke Univ Leuven （Netherlands， 21 articles） was the institution with the largest number of publications. Topic analysis revealed that the focus of forensic anthropological remains identification research was sex estimation and age estimation, and the most commonly studied remains were teeth.ConclusionThe volume of publications in the field of forensic anthropological remains identification research has a distinct phasing. However, the scope of both international and domestic collaborations remains limited. Traditionally, human remains identification has primarily relied on key areas such as the pelvis, skull, and teeth. Looking ahead, future research will likely focus on the more accurate and efficient identification of multiple skeletal remains through the use of machine learning and deep learning techniques.

We propose a new centrality measure based on a self-adaptive epidemic model characterized by an endogenous reinforcement mechanism in the transmission of information between nodes. We provide a strategy to assign to nodes a centrality score that depends, in an eigenvector centrality scheme, on that of all the elements of the network, nodes and edges, connected to it. We parameterize this score as a function of a reinforcement factor, which for the first time implements the intensity of the interaction between the network of nodes and that of the edges. In this proposal, a local centrality measure representing the steady state of a diffusion process incorporates the global information encoded in the whole network. This measure proves effective in identifying the most influential nodes in the propagation of rumors/shocks/behaviors in a social network. In the context of financial networks, it allows us to highlight strategic assets on correlation networks. The dependence on a coupling factor between graph and line graph also enables the different asset responses in terms of ranking, especially on scale-free networks obtained as minimum spanning trees from correlation networks. [ABSTRACT FROM AUTHOR]

Early cancer diagnosis is critical as it can help avoid the risks associated with long-term treatments and even prevent death. Identifying a defining trait of a cancer protein within its protein–protein interaction (PPI) network could lead to a significant breakthrough in accelerating early cancer detection. A systematic analysis of various topological properties of cancer proteins in the PPI network, focused on their fractal dimension, was conducted. It was observed that cancer proteins exhibit a high fractal dimension (with an average of 1.21). Those with the highest fractal dimension play a significant role in multiple mutation pathways. The observation that TP53 protein occupying high fractal dimension of 1.68, connected with 48 communities reaffirm the correctness of the approach. Further research in this area will provide valuable insights into the structural and functional complexity of cellular processes regulated by proteins, leading to the development of robust therapeutic approaches and enhancing our understanding of cancer biology. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Forensic Medicine / Fayixue Zazhi is the property of Journal of Forensic Medicine Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Abstract We present a novel approach for computing a variant of eigenvector centrality for multilayer networks with inter-layer constraints on node importance. Specifically, we consider a multilayer network defined by multiple edge-weighted, potentially directed, graphs over the same set of nodes with each graph representing one layer of the network and no inter-layer edges. As in the standard eigenvector centrality construction, the importance of each node in a given layer is based on the weighted sum of the importance of adjacent nodes in that same layer. Unlike standard eigenvector centrality, we assume that the adjacency relationship and the importance of adjacent nodes may be based on distinct layers. Importantly, this type of centrality constraint is only partially supported by existing frameworks for multilayer eigenvector centrality that use edges between nodes in different layers to capture inter-layer dependencies. For our model, constrained, layer-specific eigenvector centrality values are defined by a system of independent eigenvalue problems and dependent pseudo-eigenvalue problems, whose solution can be efficiently realized using an interleaved power iteration algorithm. We refer to this model, and the associated algorithm, as the Constrained Multilayer Centrality (CMLC) method. The characteristics of this approach, and of standard techniques based on inter-layer edges, are demonstrated on both a simple multilayer network and on a range of random graph models. An R package implementing the CMLC method along with example vignettes is available at https://hrfrost.host.dartmouth.edu/CMLC/ .

To investigate the spread of cascading failure on dual-layer networks, we propose a dual-layer network model based on eigenvector centrality and define network interactions through different connection strategies. Following the local priority reallocation rule of load and the nonlinear relationship between load and capacity, we conduct theoretical analysis of the proposed model and simulate it on the BA scale-free network. The result simulation shows that under different connection strategies, when the controllable coefficient α = 0. 6 of the initial load, the dual-layer network demonstrates better robustness regardless of attacking a node with a large initial load or a small initial load. And when α < 0. 6 , the robustness of the network for attacking a node with low initial load is enhanced as the node coupling rate of the dual-layer network increases. For α ≥ 0. 6 , the variation of network robustness does not vary essentially with the node coupling rate. [ABSTRACT FROM AUTHOR]

Copyright of Journal of International Business Studies is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Pro-environmental behavior does not diffuse sufficiently in society. Is there a way to enhance the degree of people's pro-environmental behavior? This study aims to develop a dynamic model of mutual learning in social networks to simulate the diffusion of pro-environmental behavior and to search for promising policies for promoting it. This study considers two policy measures: enhancing pro-environmental behavior of target people and changing the learning patterns of target people. The people targeted for intervention are determined by random selection, selection in descending order of degree centrality, and selection in descending order of eigenvector centrality. Centralities measure an influence of a node on other nodes through a network, based on the number of direct or indirect links. An interesting finding is that changing individual learning patterns is much more effective for enhancing the degree of pro-environmental behavior in social networks than trying to directly enhance its degree. In addition, selection of target people based on the centralities is more influential in encouraging environmentally friendly behavior than random selection, particularly in the policy of changing learning patterns. Multiplier effects are also measured: the ratio of the net increase in the number of people who enhance their degree of pro-environmental behavior at the end of a certain number of time steps beyond business as usual to the number of people intervened. Multiplier effects are always positive when learning patterns are changed. Six potential approaches to changing learning patterns are discussed: persuasion, reputation, competition, awareness of economic returns, information provisioning, and education.

In recent years, there has been widespread attention on hypergraphs as a research hotspot in network science.The unique structure of hypergraphs, which differs from traditional graphs, is characterized by hyperedges that can connect multiple nodes simultaneously, resulting in more complex and higher-order relationships.Effectively identifying important nodes and hyperedges in such network structures poses a key challenge.Eigenvector centrality, a common metric, has limitations in its application due to its locality when dealing with hub nodes with extremely high degree values in the network.To address this issue, the hypergraphs were transformed into their corresponding line graphs, and non-backtracking matrix centrality was employed as a method to measure the importance of hyperedges.This approach demonstrated better uniformity and differentiation in assessing the importance of hyperedges.Furthermore, the application of both eigenvector centrality and non-backtracking matrix centrality in assessing the importance of nodes in hypergraphs was explored.Comparative analysis revealed that non-backtracking matrix centrality effectively distinguished the importance of nodes.This research encompassed theoretical analysis, model construction, and empirical studies on real-world data.To validate the proposed method and conclusion, six real-world hypergraphs were selected as experimental subjects.The application of these methods to these hypergraphs confirmed the effectiveness of non-backtracking matrix centrality in identifying important nodes and hyperedges.The findings of this research offer a fresh perspective and approach for identifying key elements in hypergraphs, holding significant theoretical and practical implications for understanding and analyzing complex network systems.

The role of clustering in unsupervised fault diagnosis is significant, but different clustering techniques can yield varied results and cause inevitable uncertainty. Ensemble clustering methods have been introduced to tackle this challenge. This study presents a novel integrated technique in the field of fault diagnosis using spectral ensemble clustering. A new dimensionality reduction technique is proposed to intelligently identify faults, even in ambiguous scenarios, by exploiting the informative segment of the underlying bipartite graph. This is achieved by identifying and extracting the most informative sections of the bipartite graph based on the eigenvector centrality measure of nodes within the graph. The proposed method is applied to experimental current-voltage (I-V) curve data collected from a real photovoltaic (PV) platform. The obtained results remarkably improved the accuracy of aging fault detection to more than 83.50%, outperforming the existing state-of-the-art approaches. We also decided to separately analyze the ensemble clustering part of our FDD method, which indicated surpassing performance compared to similar methods by evaluating commonly used datasets like handwritten datasets. This proves that the proposed approach inherently holds promise for application in various real-world scenarios that are indicated by ambiguity and complexity.

Copyright of Chinese Journal of Network & Information Security is the property of Beijing Xintong Media Co., Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Road network studies attracted unprecedented and overwhelming interest in recent years due to the clear relationship between human existence and city evolution. Current studies cover many aspects of a road network, for example, road feature extraction from video/image data, road map generalisation, traffic simulation, optimisation of optimal route finding problems, and traffic state prediction. However, analysing road networks as a complex graph is a field to explore. This study presents comparative studies on the Porto, in Portugal, road network sections, mainly of Matosinhos, Paranhos, and Maia municipalities, regarding degree distributions, clustering coefficients, centrality measures, connected components, k-nearest neighbours, and shortest paths. Further insights into the networks took into account the community structures, page rank, and small-world analysis. The results show that the information exchange efficiency of Matosinhos is 0.8, which is 10 and 12.8% more significant than that of the Maia and Paranhos networks, respectively. Other findings stated are: (1) the studied road networks are very accessible and densely linked; (2) they are small-world in nature, with an average length of the shortest pathways between any two roads of 29.17 units, which as found in the scenario of the Maia road network; and (3) the most critical intersections of the studied network are 'Avenida da Boavista, 4100-119 Porto (latitude: 41.157944, longitude: − 8.629105)', and 'Autoestrada do Norte, Porto (latitude: 41.1687869, longitude: − 8.6400656)', based on the analysis of centrality measures. [ABSTRACT FROM AUTHOR]

This paper aims to investigate the factors influencing the international beef market's trade flows by applying the gravity model. We focus on the effects of trade networks, non-tariff measures, and natural disasters on the beef trade. This is the first study to incorporate network analysis eigenvector scores into the gravity approach with a view to examining the impact of trade network improvements on trade. Eigenvector centrality scores reveal the prominent role played by the European and Southern Common Market countries and show a well-connected beef network. The results of our gravity model show that beef trade increases more when an importer improves its position in the trade network, shedding light on the importance of strategic network engagement. We also show that Sanitary and Phytosanitary measures are more trade-restrictive than tariffs, a finding which emphasises the need for trade policy to pay attention to such measures. Meteorological (hydrological) catastrophes hurt beef imports (exports); this highlights the importance of implementing safeguarding measures on beef farms. [ABSTRACT FROM AUTHOR]

We propose a new centrality measure based on a self-adaptive epidemic model characterized by an endogenous reinforcement mechanism in the transmission of information between nodes. We provide a strategy to assign to nodes a centrality score that depends, in an eigenvector centrality scheme, on that of all the elements of the network, nodes and edges, connected to it. We parameterize this score as a function of a reinforcement factor, which for the first time implements the intensity of the interaction between the network of nodes and that of the edges. In this proposal, a local centrality measure representing the steady state of a diffusion process incorporates the global information encoded in the whole network. This measure proves effective in identifying the most influential nodes in the propagation of rumors/shocks/behaviors in a social network. In the context of financial networks, it allows us to highlight strategic assets on correlation networks. The dependence on a coupling factor between graph and line graph also enables the different asset responses in terms of ranking, especially on scale-free networks obtained as minimum spanning trees from correlation networks.

Advanced age is the most important risk factor for Alzheimer’s disease (AD), and carrier-status of the Apolipoprotein E4 (APOE4) allele is the strongest known genetic risk factor. Many studies have consistently shown a link between APOE4 and synaptic dysfunction, possibly reflecting pathologically accelerated biological aging in persons at risk for AD.To test the hypothesis that distinct functional connectivity patterns characterize APOE4 carriers across the clinical spectrum of AD, we investigated 128 resting state functional Magnetic Resonance Imaging (fMRI) datasets from the Alzheimer’s Disease Neuroimaging Initiative database (ADNI), representing all disease stages from cognitive normal to clinical dementia. Brain region centralities within functional networks, computed as eigenvector centrality, were tested for multivariate associations with chronological age, APOE4 carrier status and clinical stage (as well as their interactions) by partial least square analysis (PLSC).By PLSC analysis two distinct brain activity patterns could be identified, which reflected interactive effects of age, APOE4 and clinical disease stage. A first component including sensorimotor regions and parietal regions correlated with age and AD clinical stage (p

Kangaroos are an iconic group of Australian fauna. Despite considerable research on kangaroo behaviour, key gaps remain in our understanding of their social organization in the wild. In particular, it remains largely unknown whether kangaroos form long-term social bonds and what factors might prompt individuals to associate or dissociate from one another. Over 6 years, we monitored the social affiliations of individually identified eastern grey kangaroos, Macropus giganteus , in a large wild population. We investigated the short-term and long-term relationships of kangaroos and the extent those relationships varied with age, sex and reproductive state. We found evidence that long-term relationships among eastern grey kangaroos are possible, especially between adult females. Those individuals that were more sociable within years were also more likely to establish affiliations across years. Contrary to previous studies, we observed females actively associating with other mothers in the years in which they had young. These data suggest that the fission–fusion dynamics of eastern grey kangaroo social behaviour allow females to modulate their social position with conspecifics according to their current reproductive state. We highlight the adaptive implications of the formation of long-term bonds and the changes in social behaviour observed in females. • We found evidence for possible long-term relationships in eastern grey kangaroos. • Social individuals were more likely to establish potential long-term relationships. • Female kangaroos had more associations in years when they had young. [ABSTRACT FROM AUTHOR]

The real‐time performance is a major concern of complex real‐time systems during information acquisition, transmission, processing, and application. The interactions among information within these systems constitute complicated information exchange networks. Network topology plays an important role in the behaviors of information interactions and interferes, which severely affects the real‐time performance of the whole systems. Methods such as synchronization‐based method or traffic balancing strategy are the main ideas that guide the construction of network topology. However, these methods could not consider the real‐time performance in the process of topology construction. This article proposes an automatic topology generation algorithm with real‐time guarantee. Specifically speaking, the nodes in the network are clustered to generate the network topology by introducing the concepts of node degree and eigenvector centrality to ensure the real‐time performance of the network. The position of the nodes in the network is determined by comparing the node degree and the communication factors. Analytic method and simulation method are used to verify the real‐time performance of the proposed algorithm. Results show that the real‐time performance of more than half of the total information exchanges is improved compared with the traffic balancing method and topology grouping method for an industrial‐scale networking case. [ABSTRACT FROM AUTHOR]

Scientific analysis of public transport systems at the urban, regional, and national levels is vital in this contemporary, highly connected world. Quantifying the accessibility of nodes (locations) in a transport network is considered a holistic measure of transportation and land use and an important research area. In recent years, complex networks have been employed for modeling and analyzing the topology of transport systems and services networks. However, the design of network hierarchy-based accessibility measures has not been fully explored in transport research. Thus, we propose a set of three novel accessibility metrics based on the k-core decomposition of the transport network. Core-based accessibility metrics leverage the network topology by eliciting the hierarchy while accommodating variations like travel cost, travel time, distance, and frequency of service as edge weights. The proposed metrics quantify the accessibility of nodes at different geographical scales, ranging from local to global. We use these metrics to compute the accessibility of geographical locations connected by air transport services in India. Finally, we show that the measures are responsive to changes in the topology of the transport network by analyzing the changes in accessibility for the domestic air services network for both pre-covid and post-covid times.

The social structure of gregarious animals can be influenced by phenotypic differences, including signals of quality or of social dominance such as colour ornamentation. Familiarity among individuals can also influence social behaviour. When individuals are already familiar with each other, it is unclear whether ornamentation remains important in shaping social behaviour. Here, we tested how long-term associations among individuals are affected by two types of perturbations. First, we tested whether experimentally manipulating ornamental plumage affected social dominance and network centrality among familiar individuals. In a second experiment, we tested whether temporary absence of individuals affected their long-term associations. In our first experiment, we reduced the size of the red breast plumage of wild-caught common waxbills, Estrilda astrild , that had been living together for over a year in a large mesocosm and monitored their social network and aggressive interactions before and up to 2 months after the manipulation. We found that waxbills with experimentally reduced red breast plumage did not change in social network centrality or social dominance, compared to controls. In our second experiment, we temporarily removed a subset of waxbills from the mesocosm and found that their social network centrality was not affected upon their return to the group 3 months later. The strength of associations between separated birds remained stable, not having changed more than those between control birds that remained together in the mesocosm. In contrast to these temporarily absent birds, waxbills entering the mesocosm for the first time occupied more central network positions. Results indicate that long-term associations among familiar waxbills are resilient to changes in ornamentation and to prolonged absence. We propose that resuming long-term associations with familiar individuals stabilizes social structure, which may increase the benefits of social living for gregarious species like the waxbill, which in nature exhibits fission–fusion and vagrant movements. - Colour ornamentation and familiarity can influence social behaviour - It is unclear whether colour ornamentation shapes sociality of familiar individuals - Using waxbills, we reduced the size of red colour or temporarily removed individuals - Neither treatment affected positions in the social network or dominance hierarchy - A resilient social structure may be adaptive in species with fission-fusion dynamics [ABSTRACT FROM AUTHOR]

Many patients with glioma, primary brain tumors, suffer from poorly understood executive functioning deficits before and/or after tumor resection. We aimed to test whether frontoparietal network centrality of multilayer networks, allowing for integration across multiple frequencies, relates to and predicts executive functioning in glioma. Patients with glioma (n = 37) underwent resting-state magnetoencephalography and neuropsychological tests assessing word fluency, inhibition, and set shifting before (T1) and one year after tumor resection (T2). We constructed binary multilayer networks comprising six layers, with each layer representing frequency-specific functional connectivity between source-localized time series of 78 cortical regions. Average frontoparietal network multilayer eigenvector centrality, a measure for network integration, was calculated at both time points. Regression analyses were used to investigate associations with executive functioning. At T1, lower multilayer integration (p = 0.017) and epilepsy (p = 0.006) associated with poorer set shifting (adj. R2 = 0.269). Decreasing multilayer integration (p = 0.022) and not undergoing chemotherapy at T2 (p = 0.004) related to deteriorating set shifting over time (adj. R2 = 0.283). No significant associations were found for word fluency or inhibition, nor did T1 multilayer integration predict changes in executive functioning. As expected, our results establish multilayer integration of the frontoparietal network as a cross-sectional and longitudinal correlate of executive functioning in glioma patients. However, multilayer integration did not predict postoperative changes in executive functioning, which together with the fact that this correlate is also found in health and other diseases, limits its specific clinical relevance in glioma. [ABSTRACT FROM AUTHOR]

The old adage "it is not what you know, but who you know" suggests that in connection(s) lies the key(s) to success. But what does success mean, and for how long will it last? What does the choice of partner, and network connections say about the performance implications of contracting, particularly in the case of a public–private partnership? With countries such as the United States accounting for the world's largest buyer (of any and everything), several suppliers eagerly await their opportunity to contract with large government entities, but is it always a wise decision? Such questions remain largely unexplored and require answers. This research provides answers to these questions by integrating congruence, and network theory to investigate how government contracting impacts private suppliers' financial performance and how suppliers' supply chain network connections moderate this relationship. Results using panel data over several years suggest that while contracting with government bolsters' short‐term financial performance (ROA), it negatively affects long‐term supplier performance (Tobin's Q). In addition, the prominence of a firm's connections (i.e., who they know) and the composition of these connections enhances the performance gains, highlighting the critical role of a firm's network structure on their performance within the contracting relationship. We therefore find that the power of supply chain network connections enhances the short‐term positive effects and mitigates the long‐term adverse effects when contracting with government buyers, as the relationship is not always universally positive. [ABSTRACT FROM AUTHOR]

The use of statistical tools for predicting the winner in tennis matches has enjoyed an increase in popularity over the last two decades and, currently, a variety of methods are available. In particular, paired comparison approaches make use of latent ability estimates or rating calculations to determine the probability that a player will win a match. In this paper, we extend this latter class of models by using network indicators for the predictions. We propose a measure based on eigenvector centrality. Unlike what happens for the standard paired comparisons class (where the rates or latent abilities only change at time t for those players involved in the matches at time t), the use of a centrality measure allows the ratings of the whole set of players to vary every time there is a new match. The resulting ratings are then used as a covariate in a simple logit model. Evaluating the proposed approach with respect to some popular competing specifications, we find that the centrality-based approach largely and consistently outperforms all the alternative models considered in terms of the prediction accuracy. Finally, the proposed method also achieves positive betting results. [ABSTRACT FROM AUTHOR]

Delay Tolerant Networks (DTNs) are marked by the extended delay in message delivery and unavailability of a dedicated path from the source to the destination. To this end, determining the appropriate relay nodes for effective message delivery becomes crucial. In this work, we have used Eigenvector centrality to measure the importance of a node for relay node prediction. The nodes that encounter frequently usually contribute to the initial improvement of message delivery probability. However, in some cases, the nodes encountered infrequently are exceptionally important in the message transfer process. For relay node selection, we have also taken into consideration a time associated matrix which is updated periodically. This matrix is filled based on contact frequency and contact duration to improve the overheads. A series of simulations has been carried out and experimental results show the efficacy of the proposed eigenvector centrality (EVC) based approach. It is observed that EVC routing reduced more overheads as compared to other existing protocols. We also analyzed the proposed protocol for delivery probability and average latency. [ABSTRACT FROM AUTHOR]

Scientific analysis of public transport systems at the urban, regional, and national levels is vital in this contemporary, highly connected world. Quantifying the accessibility of nodes (locations) in a transport network is considered a holistic measure of transportation and land use and an important research area. In recent years, complex networks have been employed for modeling and analyzing the topology of transport systems and services networks. However, the design of network hierarchy-based accessibility measures has not been fully explored in transport research. Thus, we propose a set of three novel accessibility metrics based on the k-core decomposition of the transport network. Core-based accessibility metrics leverage the network topology by eliciting the hierarchy while accommodating variations like travel cost, travel time, distance, and frequency of service as edge weights. The proposed metrics quantify the accessibility of nodes at different geographical scales, ranging from local to global. We use these metrics to compute the accessibility of geographical locations connected by air transport services in India. Finally, we show that the measures are responsive to changes in the topology of the transport network by analyzing the changes in accessibility for the domestic air services network for both pre-covid and post-covid times. [ABSTRACT FROM AUTHOR]