Network Centrality Metrics Research Articles

A combined synchronization index for evaluating collective action social media

Social media has provided a citizen voice, giving rise to grassroots collective action, where users deploy a concerted effort to disseminate online narratives and even carry out offline protests. Sometimes these collective action are aided by inorganic synchronization, which arise from bot actors. It is thus important to identify the synchronicity of emerging discourse on social media and the indications of organic/inorganic activity within the conversations. This provides a way of profiling an event for possibility of offline protests and violence. In this study, we build on past definitions of synchronous activity on social media— simultaneous user action–and develop a Combined Synchronization Index (CSI) which adopts a hierarchical approach in measuring user synchronicity. We apply this index on six political and social activism events on Twitter and analyzed three action types: synchronicity by hashtag, URL and @mentions.The CSI provides an overall quantification of synchronization across all action types within an event, which allows ranking of a spectrum of synchronicity across the six events. Human users have higher synchronous scores than bot users in most events; and bots and humans exhibits the most synchronized activities across all events as compared to other pairs (i.e., bot-bot and human-human). We further rely on the harmony and dissonance of CSI-Network scores with network centrality metrics to observe the presence of organic/inorganic synchronization. We hope this work aids in investigating synchronized action within social media in a collective manner.

WDN-tailored Complex Network Centrality Metrics for water distribution networks domain analysis

Water Distribution Networks (WDNs) are spatially organized infrastructures, whose hydraulic behavior greatly depends on their connectivity properties, i.e., the topological domain. The Complex Network Theory (CNT) provides a wide range of useful functions and metrics that have been tailored for identifying WDNs behavior, even before using hydraulic models. This work exploits the recent studies on the WDN domain, which couple the CNT metrics of centrality tailored for WDN with the intrinsic relevance of the spatial elements of the networks. The study accounts for various combinations of metrics and intrinsic relevance functions aimed at supporting WDN analysis for operational and management tasks. A Digital Water Service (DWS) is used to enable technicians and water companies to replicate the same domain analysis in the ongoing context of digital transformation in WDN sector.

Using WDN-tailored complex network centrality metrics to assess isolation valve system reliability

Mechanical reliability Water Distribution Networks (WDNs) depends on the Isolation Valve System (IVS). In fact, in the event of accidental disruptions or planned maintenance works, segments of the WDNs need to be temporarily disconnected by closing isolation valves to allow pipes repair. This maneuver it modifies the WDN topology and relevant hydraulic behavior. In this paper a recent approach for IVS reliability based on the Complex Network Theory (CNT) has been applied. It couples the construction of the graph of segments and isolation valves generated by the IVS with the risk of segment interruptions, using several WDN-tailored centrality metrics. The study is demonstrated on a real WDN using a Digital Water Service developed ad hoc for enabling technicians and water utilities in targeting the most critical valves in case of failure and thereby defining a lineup for valves inspections and maintenance.

Burnout and network centrality as proxies for assessing the human cost of resilient performance

The extra effort of healthcare professionals to provide care is a manifestation of resilient performance (RP), usually going unnoticed due to successful outcomes. However, it is not clear how the human cost of RP can be assessed. This study addresses this gap by investigating the relationships between proxies of RP and its human cost. The proposed approach was tested in a 29-bed intensive care unit (ICU). The centrality of each professional in the advice-seeking social network was considered as the proxy of their contribution to system resilience. A resilience score was calculated for each professional as the product of three network centrality metrics (in-degree, closeness, and betweenness) and two non-network attributes, namely their availability and reliability. Professionals' burnout was the proxy of the human cost of RP, assessed through the Maslach Burnout Inventory, composed of 22 items divided into a triad of emotional exhaustion, depersonalization, and personal accomplishment. Both questionnaires, for social network analysis and burnout, included socio-demographic questions and were answered by 99.0% of the professionals. Results indicated a weak correlation between emotional exhaustion and the resilience score (p = 0.008). This score was also weakly correlated with working overtime (p = 0.005). Overall, findings provided initial evidence that RP as measured in our study matters to burnout, and that the two proxies are exemplars of applying a more general reasoning that might be valid for other proxies.

Inferencing Bulk Tumor and Single-Cell Multi-Omics Regulatory Networks for Discovery of Biomarkers and Therapeutic Targets.

There are insufficient accurate biomarkers and effective therapeutic targets in current cancer treatment. Multi-omics regulatory networks in patient bulk tumors and single cells can shed light on molecular disease mechanisms. Integration of multi-omics data with large-scale patient electronic medical records (EMRs) can lead to the discovery of biomarkers and therapeutic targets. In this review, multi-omics data harmonization methods were introduced, and common approaches to molecular network inference were summarized. Our Prediction Logic Boolean Implication Networks (PLBINs) have advantages over other methods in constructing genome-scale multi-omics networks in bulk tumors and single cells in terms of computational efficiency, scalability, and accuracy. Based on the constructed multi-modal regulatory networks, graph theory network centrality metrics can be used in the prioritization of candidates for discovering biomarkers and therapeutic targets. Our approach to integrating multi-omics profiles in a patient cohort with large-scale patient EMRs such as the SEER-Medicare cancer registry combined with extensive external validation can identify potential biomarkers applicable in large patient populations. These methodologies form a conceptually innovative framework to analyze various available information from research laboratories and healthcare systems, accelerating the discovery of biomarkers and therapeutic targets to ultimately improve cancer patient survival outcomes.

A Network Analysis of the Relationship Between Mental and Physical Health in Unsheltered Homeless Persons in Los Angeles County.

Homelessness is a public health crisis both nationally, in the United States, and internationally. Nevertheless, due to the hidden vulnerabilities of persons who are without shelter, little is known about their experiences during periods of homelessness. The present research adopts a network approach that conceptualizes how the major risk factors of homelessness interact, namely substance abuse problems, poor mental health, disability, and exposure to physical or sexual violence by an intimate partner. Our analysis draws on a large demographic survey of over 5,000 unsheltered homeless persons conducted in 2017 by the Los Angeles Homeless Services Authority. We estimated a network structure for 12 survey items tapping individual risk using the graphical least absolute shrinkage and selection operator algorithm. We then examined network centrality metrics and implemented a community detection algorithm to detect communities in the network. Our results indicated that mental illness and intimate partner violence (IPV) are central measures that connect all other mental and physical health variables together and that post-traumatic stress disorder and IPV are both highly affected by changes in any part of the network and, in turn, affect changes in other parts of the network. A community detection analysis derived four communities characterized by disability, sexual victimization and health, substance use, and mental health issues. Finally, a directed acyclic graph revealed that drug abuse and physical disability were key drivers of the overall system. We conclude with a discussion of the major implications of our findings and suggest how our results might inform programs aimed at homelessness prevention and intervention.

A graph structure feature-based framework for the pattern recognition of the operational states of integrated energy systems

The recognition of operational state patterns is crucial for the safe, reliable and profitable operational management of integrated energy systems (IES). However, considering that the monitored operational data are often not labelled, supervised machine learning classification methods cannot be directly applied. In this study, based on the idea of graph theory, a framework of interpretable time series pattern recognition methods based on graph structural features is proposed. Firstly, the time series data is segmented into local subsequences by a shapelet-based time series classification method. Secondly, time-graphlets are constructed by means of a visibility graph algorithm and an original graph structure feature-based representation method is proposed. Then, pattern recognition of the different operational states of IES is performed in the form of multidimensional feature vectors. Thirdly, an association rule model is designed to analyze the supply and demand fluctuations, price fluctuations and couplings between multiple energy subsystems, based on which the pattern recognition results are annotated to match the operational characteristics of the IES. Fourthly, a shapelet-based state transfer network graph is constructed, and the complex network centrality metrics are used to analyze the operating characteristics and key operating states of the system over time. The new method is applied for pattern recognition of operational states in a Spanish IES. The results show that the proposed method can increase the interpretability of the recognition results and provide explanatory labels for the operational data. Furthermore, the vulnerable operational states of the system can be revealed, which allows managers to monitor the operational state of the system and take early defensive measures against the more threatening operational patterns. This suggests that the methodological framework presented in this paper is a promising approach to pattern recognition that can provide interpretable annotations for unsupervised operational data of IES and their safe management.

Advancing drug repurposing research: Trends, collaborative networks, innovation and knowledge leaders

The advancement of drug repurposing (DR) relies on scientific leadership and innovative institutions with access to knowledge. We performed a bibliometric and social network analysis (covering the period 2011-2020) to map the DR research trends and to identify innovation and knowledge leaders (IKLs). The indexing rate of DR publications has steadily increased. Major research areas included emerging viruses, cancer, methodological approaches, preclinical research, and infectious diseases. Government and academia accounted for nearly 65% of funding. Using a combination of network centrality metrics, 41 IKLs affiliated to central and pericentral institutions in the global research network were identified. These IKLs can facilitate the flow of knowledge and are best positioned to promote interactions within the network, with the potential to contribute significantly to advancing the DR field.

A Novel Approach to Analyze the Isolation Valve System Based on the Complex Network Theory

AbstractComplex Network Theory (CNT) studies network models and centrality metrics to deal with reliability/vulnerability assessment in networked systems. Classic CNT analyses evaluate the impact of failure of single vertices/edge elements on system connectivity assuming that all vertices/nodes have the same relevance. In Water Distribution Networks (WDNs), the Isolation Valve System (IVS) plays a crucial role for system reliability because a pipe failure asks for valves shutdown to isolate that pipe, usually requiring the isolation of a larger network segment, having its intrinsic relevance. This circumstance makes classic connectivity analyses and metrics of CNT misleading in reliability assessment of WDN. This work proposes a novel approach to analyze IVS, which is based on the construction of the graph of segments and isolation valves generated by the IVS. The application of the recently proposed relevance‐based (WDN‐tailored) betweenness centrality to such graph is used to rank the importance of segments and isolation valves. Two WDNs of different sizes are used to demonstrate and discuss the strategy also from a practical perspective.

A Multiscale Land Use Regression Approach for Estimating Intraurban Spatial Variability of PM2.5 Concentration by Integrating Multisource Datasets.

Poor air quality has been a major urban environmental issue in large high-density cities all over the world, and particularly in Asia, where the multiscale complex of pollution dispersal creates a high-level spatial variability of exposure level. Investigating such multiscale complexity and fine-scale spatial variability is challenging. In this study, we aim to tackle the challenge by focusing on PM2.5 (particulate matter with an aerodynamic diameter less than 2.5 µm,) which is one of the most concerning air pollutants. We use the widely adopted land use regression (LUR) modeling technique as the fundamental method to integrate air quality data, satellite data, meteorological data, and spatial data from multiple sources. Unlike most LUR and Aerosol Optical Depth (AOD)-PM2.5 studies, the modeling process was conducted independently at city and neighborhood scales. Correspondingly, predictor variables at the two scales were treated separately. At the city scale, the model developed in the present study obtains better prediction performance in the AOD-PM2.5 relationship when compared with previous studies ( from 0.72 to 0.80). At the neighborhood scale, point-based building morphological indices and road network centrality metrics were found to be fit-for-purpose indicators of PM2.5 spatial estimation. The resultant PM2.5 map was produced by combining the models from the two scales, which offers a geospatial estimation of small-scale intraurban variability.

Simulation of traffic light disruptions in street networks

Intelligent transportation systems (ITS) use information technology and control devices like traffic lights and sensors to help commuters in achieving flow coordination, improving safety and smoothness in mobility and providing valuable information to public managers. But ITS devices are not free of vulnerabilities against cyber-attacks and failures. The impacts of traffic light disruption will be different for different street network structures. Complex network analysis can be used to evaluate critical infrastructures like street networks, revealing patterns of flow distribution, path redundancy, efficiency and robustness. But, at the present moment, the correlations between street network topology and the impact of traffic light disruptions were not sufficiently addressed in the literature. This work aims to evaluate the role of topological configuration and resilience of street networks when subjected to traffic light disruption, 50000 trips were simulated using three attack strategies that prioritize traffic lights based on complex network centrality metrics. Complex street, network analysis is used to interpret the results of the trip simulation.

Migration efficiency sustains connectivity across agroecological networks supporting sandhill crane migration

AbstractPreserving avian flyway connectivity has long been challenged by our capacity to meaningfully quantify continental habitat dynamics and bird movements at temporal and spatial scales underlying long‐distance migrations. Waterbirds migrating hundreds or thousands of kilometers depend on networks of wetland stopover sites to rest and refuel. Entire populations may rely on discrete wetland habitats, particularly in arid landscapes where the loss of limited stopover options can have disproportionately high impacts on migratory cost. Here, we examine flyway connectivity in water‐limited ecosystems of western North America using 108 GPS tagged greater sandhill cranes. Bird movements were used to reconstruct wetland stopover networks across three geographically unique sub‐populations spanning 12 U.S.–Mexican states and Canadian provinces. Networks were monitored with remote sensing to identify long‐term (1988–2019) trends in wetland and agricultural resources supporting migration and evaluated using network theory and centrality metrics as a measure of stopover site importance to flyway connectivity. Sandhill crane space use was analyzed in stopover locations to identify important ownership and landscape factors structuring bird distributions. Migratory efficiency was the primary mechanism underpinning network function. A small number of key stopover sites important to minimizing movement cost between summering and wintering locations were essential to preserving flyway connectivity. Localized efficiencies were apparent in stopover landscapes given prioritization of space use by birds where the proximity of agricultural food resources and flooded wetlands minimized daily movements. Model depictions showing wetland declines from 16% to 18% likely reflect a new normal in landscape drying that could decouple agriculture–waterbird relationships as water scarcity intensifies. Sustaining network resilience will require conservation strategies to balance water allocations preserving agricultural and wetlands on private lands that accounted for 67–96% of habitat use. Study outcomes provide new perspectives of agroecological relationships supporting continental waterbird migration needed to prioritize conservation of landscapes vital to maintaining flyway connectivity.

How are network centrality metrics related to interest rates in the Mexican secured and unsecured interbank markets?

In financial stability, it is essential to know the determinants of interest rates in interbank markets because they are important vehicles for liquidity allocation among banks and are relevant for monetary policy transmission. Recent research indicates that banks with excess liquidity exercise their market power by rationing liquidity during periods of financial stress. This confirms the value of knowing the banks connections and identifying liquidity spreaders in such markets to manage contagion risk, liquidity hoarding and to preserve financial stability. In addition to well studied bank features such as size, liquidity and credit risk, we study which network metrics relate to interest rates during different periods. Using transaction level data on unsecured and secured lending, we apply an approach that employs network theory, econometric models and machine learning to analyze the structural properties of the secured and unsecured interbank markets in Mexico. Our findings support the “too-interconnected-to-fail” hypothesis. In the secured interbank market, PageRank shows a relationship with interest rates, while metrics associated with the notion of influence and systemic risk (Katz and DebtRank) are relevant in the unsecured interbank market. In general, a bank with high centrality lends at higher rates and gets funding at lower rates.

DeepNetBim: deep learning model for predicting HLA-epitope interactions based on network analysis by harnessing binding and immunogenicity information

BackgroundEpitope prediction is a useful approach in cancer immunology and immunotherapy. Many computational methods, including machine learning and network analysis, have been developed quickly for such purposes. However, regarding clinical applications, the existing tools are insufficient because few of the predicted binding molecules are immunogenic. Hence, to develop more potent and effective vaccines, it is important to understand binding and immunogenic potential. Here, we observed that the interactive association constituted by human leukocyte antigen (HLA)-peptide pairs can be regarded as a network in which each HLA and peptide is taken as a node. We speculated whether this network could detect the essential interactive propensities embedded in HLA-peptide pairs. Thus, we developed a network-based deep learning method called DeepNetBim by harnessing binding and immunogenic information to predict HLA-peptide interactions.ResultsQuantitative class I HLA-peptide binding data and qualitative immunogenic data (including data generated from T cell activation assays, major histocompatibility complex (MHC) binding assays and MHC ligand elution assays) were retrieved from the Immune Epitope Database database. The weighted HLA-peptide binding network and immunogenic network were integrated into a network-based deep learning algorithm constituted by a convolutional neural network and an attention mechanism. The results showed that the integration of network centrality metrics increased the power of both binding and immunogenicity predictions, while the new model significantly outperformed those that did not include network features and those with shuffled networks. Applied on benchmark and independent datasets, DeepNetBim achieved an AUC score of 93.74% in HLA-peptide binding prediction, outperforming 11 state-of-the-art relevant models. Furthermore, the performance enhancement of the combined model, which filtered out negative immunogenic predictions, was confirmed on neoantigen identification by an increase in both positive predictive value (PPV) and the proportion of neoantigen recognition.ConclusionsWe developed a network-based deep learning method called DeepNetBim as a pan-specific epitope prediction tool. It extracted the attributes of the network as new features from HLA-peptide binding and immunogenic models. We observed that not only did DeepNetBim binding model outperform other updated methods but the combination of our two models showed better performance. This indicates further applications in clinical practice.

Compound Target Identification in Tissue-Specific Interaction Networks

Characterizing all possible side effects of compounds is not a trivial task due to unknown off-target proteins that might eventually lead lethal reactions. There is still a tremendous need of computational methods to identify protein targets of a new compound. We have performed a comprehensive analysis for identification of protein targets by integrating tissue-specific protein-protein interaction (PPI) networks and compound induced transcriptome data. Several network centrality metrics are computed to suggest the most probable off-targets of a given compound. The effects of interaction types between proteins and tissue-specific PPI networks are evaluated from multiple perspectives. Usage of network centrality metrics on a tissue-specific PPI network enhances the correct prediction rate of known targets of a given compound. The detailed analysis of successfully identified known targets indicated that degree and local radiality metrics are more practical for determining different types of target protein families, such as GPCR, chemokines, proteasome, and protein kinase families. Therefore, the proposed computational pipeline is applicable while investigating proteins from these families that can be especially targeted for treatment of complex diseases. To the best of our knowledge, this study for the first time presents how tissue-specific transcriptome changes alter the topological structure of PPI networks and the relative effects of tissue-specific networks in target identification. Overall, the proposed computational methods are practical tools for choosing more accurate protein targets for later computational analysis and wet-lab experiments.

Graph visualization of dark web hyperlinks and their feature analysis

Content regarding various illegal activities, such as weapon and drug trafficking, is shared on the dark web. Most of the illegal content is distributed on anonymous networks that cannot be directly accessed from the World Wide Web. A number of studies have been conducted on the network structure of the World Wide Web since its advent. Similar to the World Wide Web, the dark web is connected by hypertext transfer protocol (http); this makes it possible to use the methods developed for the web in the dark web. Many studies have investigated the dark web and its network structure. However, few studies have focused on the visualization of the dark web network structure, and there have been no studies investigating the temporal changes in the network structure. In this study, to understand the hypertext markup language (html) network structure of the dark web, we created and visualized a graph of the html hyperlink relations of the Tor network, which is popular on the dark web. We then compared the insights gained from graph centrality metrics with those gained from visualizations. The analyzed dataset comprised 25,270,157 pages of html text files crawled from the Tor network by breadth-first search from June 1, 2018, to January 30, 2021. Subsequently, we acquired half-yearly snapshots from the collected data and investigated the change in the dark web network over time using a time-series graph. Then, we derived the centrality metrics from the created graph data and confirmed the differences between the centrality metrics and visualizations. The results obtained in this study provided new insights into the dark web. First, we found that the dark web fluctuated significantly; the structure of the dark web network was more strongly interconnected. Second, most of the nodes that had increased in the past two years may have disappeared rapidly after May 2020. Third, analysis of each snapshot revealed that the proportion of highly volatile domains increased from 40% to 75% during the observation period. Fourth, after calculating the network centrality metrics from each snapshot and comparing the transition of hub nodes in chronological order, we observed that the importance of link-collection sites as the main information retrieval method used in the dark web decreased. Finally, we estimated the size of the dark web based on our observed dark web measurements using the mark-recapture method. To the best of our knowledge, this is the first study to use the mark-recapture method to estimate the size of the dark web network.

Social Network Metric-Based Interventions? Experiments with an Agent-Based Model of the COVID-19 Pandemic in a Metropolitan Region

We present and use an agent-based model to study interventions for suppression, mitigation, and vaccination in coping with the COVID-19 pandemic. Unlike metapopulation models, our agent-based model permits experimenting with micro-level interventions in social interactions at individual sites. We compare common macro-level interventions applicable to everyone (e.g., keep distance, close all schools) to targeted interventions in the social network spanned by households based on specific (potential) transmission rates (e.g., prohibit visiting spreading hubs or bridging ties). We show that, in the simulation environment, micro-level measures of 'locking' of a number of households and 'blocking' access to a number of sites (e.g., workplaces, schools, recreation areas) using social network centrality metrics permits refined control on the positioning on the immunity-mortality curve. In simulation results, social network metric-based vaccination of households offers refined control and reduces the spread saliently better than random vaccination.

River cruise holiday packages: A network analysis combined with a geographic information system framework

This research analyzes the physical network of river cruise holiday packages as a relational system. A comprehensive network analysis is undertaken based on data from the Douro River (Portugal). Adjacency matrices are built to derive network centrality, constraint, and modularity metrics, further integrated into a Geographic Information System. Centrality and constraint measures are regressed on river-cruise holiday packages' attraction and logistical factors. Results reveal a major impact of logistical attributes rather than attraction on most centrality and constraint metrics. River cruising's destination network proves to be a multimodal transit network structure in a hub-and-spoke network framework, exhibiting several modules linked by connector hubs, which is consistent with a power-law distribution common to real-world networks. The results confirm river cruising's complex physical network of destinations, geographically diffused, which raises systemic and scale concerns that call for an integrated approach that articulates destination planning comprehensively.

A Graph-Theoretic Equilibrium Analysis of Attacker-Defender Game on Consensus Dynamics Under $\mathcal {H}_2$ Performance Metric

In this paper, we propose a game-theoretic framework for improving the resilience of the consensus algorithm, under the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathcal {H}_2$</tex-math></inline-formula> performance metric, in the presence of a strategic attacker. In this game, an attacker selects a subset of nodes in the network to inject attack signals. Its objective is to maximize the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathcal {H}_2$</tex-math></inline-formula> norm of the system from the attack signal to the output of the system. The defender improves the resilience of the system by adding self-feedback loops to certain nodes of the network to minimize the system's norm. We investigate the interplay between the equilibrium strategies of the game and the underlying connectivity graph, using the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathcal {H}_2$</tex-math></inline-formula> performance metric as the game pay-off. The equilibrium of the (zero-sum) attacker-defender game determines the optimal location of the defense nodes in the network. The existence of a Nash equilibrium for consensus dynamics is studied under undirected and directed network topologies. For the cases where the attacker-defender game does not admit a Nash equilibrium, the Stackelberg equilibrium of the game is studied with the defender as the game leader. Our results indicate that the equilibrium strategies of the game are characterized by graph-theoretic notions such as network centrality metrics. In particular, we show that the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">effective center</i> of the graph, a new network centrality measure, captures the optimal location of defense nodes in undirected networks. In directed networks, however, the optimal locations of defenders are those nodes with small in-degrees. The theoretical results are applied to the design of a resilient formation of vehicle platoons.

Automatic network restructuring and risk mitigation through business process asset dependency analysis

In the 4th industrial revolution era, security of multiple interconnected devices has become a critical issue. A rapidly increasing number of cybersecurity incidents emerge due to complex interconnected sensors, devices, and systems used in the Internet of Things. In this paper, we tackle the need for automation in security risk analysis and restructuring of such networks. The presented framework models the connections of assets and devices so as to depict their interdependencies on a company's business processes and effectively reduces their overall risk against cybersecurity threats. It achieves this by (1) identifying critical components and dependency structural risks, (2) prioritizing assets based on their influence on business processes and (3) proposing network restructures and asset clusters. To do that, the proposed algorithm utilizes (i) dependency risk graphs for modeling and analyzing networks dependencies, (ii) graph minimum spanning trees, and (iii) network centrality metrics. We test the implementation on a real-world company and demonstrate its effectiveness. Results show that the framework can automatically identify critical components and dependency structural risks and propose different network topologies by creating the optimum number of asset subnets, while retaining business operations. Tests show that the closeness centrality metric combined with the midpoint on extreme values calculation type works best for network asset grouping and subnetting.