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Purpose In recent years, centrality measures have been extensively used to analyze real-world complex networks. Water distribution networks (WDNs), as a good example of complex networks, exhibit properties not shared by other networks. This raises concerns about the effectiveness of applying the classical centrality measures to these networks. The purpose of this paper is to generate a new centrality measure in order to stick more closely to WDNs features. Design/methodology/approach This work refines the traditional betweenness centrality by adding a hydraulic-based weighting factor in order to improve its fit with the WDNs features. Rather than an exclusive focus on the network topology, as does the betweenness centrality, the new centrality measure reflects the importance of each node by taking into account its topological location, its demand value and the demand distribution of other nodes in the network. Findings Comparative analysis proves that the new centrality measure yields information that cannot be captured by closeness, betweenness and eigenvector centrality and is more accurate at ranking the importance of the nodes in WDNs. Practical implications The following practical implications emerge from the centrality analysis proposed in this work. First, the maintenance strategy driven by the new centrality analysis enables practitioners to prioritize the components in the network based on the priority ranking attributed to each node. This allows for least cost decisions to be made for implementing the preventive maintenance strategies. Second, the output of the centrality analysis proposed herein assists water utilities in identifying the effects of components failure on the network performance, which in turn can support the design and deployment of an effective risk management strategy. Originality/value The new centrality measure, proposed herein, is distinct from the conventional centrality measures. In contrast to the classical centrality metrics in which the importance of components is assessed based on a pure topological viewpoint, the proposed centrality measure integrates both topological and hydraulic attributes of WDNs and therefore is more accurate at ranking the importance of the nodes.

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.

Network analyses on psychopathological data focus on the network structure and its derivatives such as node centrality. One conclusion one can draw from centrality measures is that the node with the highest centrality is likely to be the node that is determined most by its neighboring nodes. However, centrality is a relative measure: knowing that a node is highly central gives no information about the extent to which it is determined by its neighbors. Here we provide an absolute measure of determination (or controllability) of a node - its predictability. We introduce predictability, estimate the predictability of all nodes in 18 prior empirical network papers on psychopathology, and statistically relate it to centrality. We carried out a literature review and collected 25 datasets from 18 published papers in the field (several mood and anxiety disorders, substance abuse, psychosis, autism, and transdiagnostic data). We fit state-of-the-art network models to all datasets, and computed the predictability of all nodes. Predictability was unrelated to sample size, moderately high in most symptom networks, and differed considerable both within and between datasets. Predictability was higher in community than clinical samples, highest for mood and anxiety disorders, and lowest for psychosis. Predictability is an important additional characterization of symptom networks because it gives an absolute measure of the controllability of each node. It allows conclusions about how self-determined a symptom network is, and may help to inform intervention strategies. Limitations of predictability along with future directions are discussed.

What affects who participates in politics? In most studies of political behaviour it is found that individuals with higher education participate to a larger extent in political activities than individuals with lower education. According to conventional wisdom, education is supposed to increases civic skills and political knowledge that functions as the causal mechanisms triggering participation. However, recently a number of studies have started dealing with the question of whether education is a direct cause for political participation or merely works as a proxy for other factors, such as pre-adult socialization or social network centrality. This review article provides an introduction and critical discussion of this debate.

Centrality is widely recognized as one of the most critical measures to provide insight into the structure and function of complex networks. While various centrality measures have been proposed for single-layer networks, a general framework for studying centrality in multilayer networks (i.e., multicentrality) is still lacking. In this study, a tensor-based framework is introduced to study eigenvector multicentrality, which enables the quantification of the impact of interlayer influence on multicentrality, providing a systematic way to describe how multicentrality propagates across different layers. This framework can leverage prior knowledge about the interplay among layers to better characterize multicentrality for varying scenarios. Two interesting cases are presented to illustrate how to model multilayer influence by choosing appropriate functions of interlayer influence and design algorithms to calculate eigenvector multicentrality. This framework is applied to analyze several empirical multilayer networks, and the results corroborate that it can quantify the influence among layers and multicentrality of nodes effectively.

Social networks are absolutely a useful and important place for connecting people within the world. A basic issue in a social network is to identify the key persons within it. This is why different centrality measures have been found over the years. In this survey paper, we present past and present research works on measures of centrality in social network. For this plan, we discuss mathematical definitions and different developed centrality measures. We also present some applications of centrality measures in biology, research, security, traffic, transportation, drug, class room. At last, our future research work on centrality measure is given.

Using data from 138 independent samples, we meta-analytically examined three research questions concerning the roles of personality and network position in organizations. First, how do different personality characteristics—self-monitoring and the Big Five personality traits—relate to indegree centrality and brokerage, the two most studied structurally advantageous positions in organizational networks? Second, how do indegree centrality and brokerage compare in explaining job performance and career success? Third, how do these personality variables and network positions relate to work outcomes? Our results show that self-monitoring predicted indegree centrality (across expressive and instrumental networks) and brokerage (in expressive networks) after controlling for the Big Five traits. Self-monitoring, therefore, was especially relevant for understanding why people differ in their acquisition of advantageous positions in social networks. But the total variance explained by personality ranged between 3% and 5%. Surprisingly, we found that indegree centrality was more strongly related to job performance and career success than brokerage. We also found that personality predicted job performance and career success above and beyond network position and that network position partially mediated the effects of certain personality variables on work outcomes. This paper provides an integrated view of how an individual’s personality and network position combine to influence job performance and career success.

Exploration in complex networks has surged. Centrality measures play a pivotal role in pinpointing essential components within these networks. Previous work focus on nodes with the highest Betweenness centrality through extensive simulations. This paper analyzes the attack and/or defense strategy using one more centrality metric, bridging centrality and Bridging-Betweenness Fusion Attack (combination of both betweenness and bridging centrality). Our two-fold contribution is (1) Using high centrality removal as an attacking strategy and inspired by the dynamic node removal process, recalculated node method after each node removal is proposed. (2) In our defense techniques, new nodes are added to existing lower centrality nodes. They are added after attacks to restore the graph’s connectivity according to proposed defense strategies. Note that some attacks and defense techniques were already introduced while others are presented first time, e.g., the combination of two centrality measures for attack and a bridging-based defense strategy. This innovative approach presents a promising advancement in enhancing the resilience and fortification of complex networks against potential attacks, signifying a notable advantage of this work.

Physical climate risks are reshaping economic geography and pose a direct threat to the collaborative networks of green innovation that underpin mitigation and adaptation. This paper examines how climate risk differentially affects three core structural roles that cities occupy in green innovation collaboration networks: hubs, which aggregate knowledge and are measured by degree centrality; channels, which transmit information and are captured by closeness centrality; and bridges, which link resources and are reflected in betweenness centrality. Using a panel of Chinese cities over the past decade and two way fixed effects models, we estimate the impacts of climate risk on cities’ network roles. The results show that climate risk significantly reduces all three roles, but the negative effects on channels and hubs are substantially larger than the effect on bridges. This pattern is consistent with a defensive structural reconfiguration of the network that emphasizes resilience at the expense of efficiency. The specific pathways and magnitudes of change depend on local financial conditions, regulatory responses, a city’s position in the urban hierarchy, and the type of climate risk encountered. These findings incorporate exogenous environmental pressure into theories of network evolution and provide empirical support for shifting regional innovation policy from an efficiency first orientation toward a resilience oriented innovation ecosystem.

Identifying changes in the spatial structure of cities is a prerequisite for the development and validation of adequate planning strategies. Nevertheless, current methods of measurement are becoming ever more challenged by the highly diverse and intertwined ways of how people actually make use of urban space. Here, we propose a new quantitative measure for the centrality of locations, taking into account not only the numbers of people attracted to different locations, but also the diversity of the activities they are engaged in. This 'centrality index' allows for the identification of functional urban centres and for a systematic tracking of their relative importance over time, thus contributing to our understanding of polycentricity. We demonstrate the proposed index using travel survey data in Singapore for different years between 1997 and 2012. It is shown that, on the one hand, the city-state has been developing rapidly towards a polycentric urban form that compares rather closely with the official urban development plan. On the other hand, however, the downtown core has strongly gained in its importance, and this can be partly attributed to the recent extension of the public transit system.