Betweenness Centrality Measures Research Articles

Betweenness Centrality Measures as Potential Predictors of Crash Frequency

Betweenness Centrality Measures as Potential Predictors of Crash Frequency

Conflict or coordination? An analysis of the Southern New England offshore wind - fisheries policy network

Offshore wind represents a growing part of coastal states’ energy portfolios. While it stands on the brink of rapid expansion in the United States, reaction from ocean users is mixed, with commercial fishers representing the most vocal opposition. Recent events suggest that fisheries conflict may represent a barrier to advancing the growth of U.S. offshore wind, yet little is known about the inner workings of this conflict nor how actors interact to try to influence policy outcomes. This research utilized policy networks and the advocacy coalition framework to assess the ways in which fishers, fisheries advocates, and others interacted with each other, government agencies, and wind developers within the context of offshore wind farm decision-making in southern New England, U.S. Data collected through a questionnaire were used to perform a social network analysis. Findings revealed one baseline communication network, and a similarly configured coordination network, rather than multiple advocacy coalitions. Degree and betweenness centrality measures revealed highly influential roles of one fisheries advocacy organization and two wind developers. Information/advice and trust networks were also analyzed; in-degree centrality measures revealed the influence of two federal fisheries offices, as well as the same fisheries organization, as credible and trustworthy. Overall, results reveal that while coordination was taking place, conflict was also fueled through the dominant influence of private industries and the lack of prominence of a neutral bridging organization. Recommendations for practice include integrating a bridging organization and facilitating a more influential role of respected government agencies to mitigate conflict.

Evolution of Quantum Computing: Theoretical and Innovation Management Implications for Emerging Quantum Industry

Quantum computing is a vital research field in science and technology. One of the fundamental questions hardly known is how quantum computing research is developing to support scientific advances and the evolution of path-breaking technologies for economic, industrial, and social change. This study confronts the question here by applying methods of computational scientometrics for publication analyses to explain the structure and evolution of quantum computing research and technologies over a 30-year period. Results reveal that the evolution of quantum computing from 1990 to 2020 has a considerable average increase of connectivity in the network (growth of degree centrality measure), a moderate increase of the average influence of nodes on the flow between nodes (little growth of betweenness centrality measure), and a little reduction of the easiest access of each node to all other nodes (closeness centrality measure). This evolutionary dynamics is due to the increase in size and complexity of the network in quantum computing research over time. This study also suggests that the network of quantum computing has a transition from hardware to software research that supports accelerated evolution of technological pathways in quantum image processing, quantum machine learning, and quantum sensors. Theoretical implications of this study show the morphological evolution of the network in quantum computing from a symmetric to an asymmetric shape driven by new inter-related research fields and emerging technological trajectories. Findings here suggest best practices of innovation management based on R&D investments in new technological directions of quantum computing having a high potential for growth and impact in science and markets.

Alleys: The future of walkability in suburbs

Alleys have the potential to become the future of walkability in suburban areas. This study strongly advocates for the incorporation of alleyways as fundamental components of circulation systems when designing walkable suburban neighborhoods. For this purpose, a dataset comprising 30 neighborhood samples have been meticulously selected from the cities of Abu Dhabi and Dubai. The Edge Betweenness centrality measure is used to assess how the inclusion of alleys affects the estimated trips along the network segments. This is achieved by comparing the analysis values for two network scenarios: one before and one after the inclusion of alleys. The objective of this analysis is to ascertain the circumstances under which alleys are considered as either a complementary or compensatory system to streets. The results reveal that the principal determinant of alleys' capacity to complement or compensate for street networks depends on their relative density in relation to streets. Moreover, other attributes of alleys, such as their continuity, distribution within the network, and direction, have been demonstrated to influence their utilization as central links in the network. The valuable insights derived from this analysis are instrumental in informing urban policies and design practices, contributing to the creation of more walkable areas in suburban areas.

Determination of Critical Edges in Air Route Network Using Modified Weighted Sum Method and Grey Relational Analysis

The air transportation system has attracted due attention from researchers due to its fast expansion over the last decade. Past research has focused on air transportation networks (ATN), but this work considers the resilience of the air route network. This research work proposes a modified approach based on GRA-WSM, named MA (Modified Approach based on GRA-WSM) for the identification of critical edges that form the backbone of the Chinese air route network. MA is a two-step process: Initially, important nodes are identified using the proposed GRA-WSM, and second, a novel approach is used for the computation of critical edges. Previously, researchers have used edge betweenness centrality measure to identify vital edges. But it took into account the global information of a node. This research work considers different centrality measures as the multi-attribute of the network, to take advantage of each centrality measure. The proposed MA approach aims to minimize the robustness of the network after the removal of some edges and the result is the set of critical edges. The critical edges found by the proposed MA approach are different from the edges that are topologically more important. These findings provide new perspectives on how to better understand other real-world networks.

Influence of Urban Railway Network Centrality on Residential Property Values in Bangkok

In recent decades, Bangkok has experienced substantial investments in its urban railway network, resulting in a profound transformation of the city’s landscape. This study examines the relationship between railway development and property value uplift, particularly focusing on network centrality, which is closely linked to urban structure. Our findings are based on two primary analyses: network centrality and spatial hedonic models. The network centrality analysis reveals that closeness centrality underscores the city’s prevailing monocentric structure, while the betweenness centrality measure envisions the potential emergence of urban subcenters. In our hedonic analysis of condominiums near railway stations, we formulated various regression models with different specifications, incorporating spatial effects and network centrality. With Bangkok’s predominant monocentric structure in mind, we found that the spatial regression model, including a spatial error specification and closeness centrality, outperforms the others. This suggests that the impact of railways on property values extends beyond station proximity and encompasses network centrality, intricately linked with the city’s urban structure. We applied our developed model to estimate the expected increase in property values at major interchange stations with high network centralities. These numerical values indicate a considerable potential for their evolution into urban subcenters. These insights offer valuable policy recommendations for effectively harnessing transit-related premiums and shaping the future development of both the railway system and the city.

Optimal network-wide adjustments of initial airport slot allocations with connectivity and fairness objectives

Due to serious demand-supply imbalances, many airports around the world are highly congested. Access to these highly congested (Level 3, coordinated) airports is controlled through the use of the IATA World Airport Slot allocation Guidelines (WASG). At an individual airport, slots requested by each airline are allocated at the airport under consideration independently without taking into account the interactions between slots allocated at different airports. However, in order for the air-transport network to operate seamlessly, ensuring network-wide connectivity of flights, and the interdependencies existing between the slots allocated at individual airports need to be considered. Several models have been proposed in the literature to deal with the optimum allocation of slots at a single airport. However, the literature currently does not adequately address the network-wide slot allocation problem. In this paper, we are introducing a novel approach to address the network-wide slot allocation problem. Our approach considers as an input the individual airport schedules generated during the slot allocation process at individual airports and optimally adjusts them to ensure network-wide flight connectivity by taking into account the interdependencies existing between flights connecting pairs of airports.To this end, we propose bi-objective mathematical models, which consider schedule efficiency and inter-airline fairness objectives, and incorporate the importance that different airports have for the functioning of the air transport network, using the IATA connectivity indices and the betweenness centrality measures. We solve the proposed models using the ε − constraint method to investigate trade-offs between network-wide schedule efficiency and fairness, and we investigate the effect of these trade-offs on the airlines and the airports. Results from the application of the proposed models to a test network suggest that the consideration of the contribution of the airports to network connectivity affect the way that the total network-wide schedule displacement is distributed among the airports. Specifically, we found that the use of the IATA connectivity index tends to allocate less schedule displacement to airports with frequent flights to many destinations, while the use of the betweenness centrality measure allocates less schedule displacement to airports that are more critical in ensuring the connectivity of other airports in the network.

Square Distance in Graphs

In this paper, we consider a simple connected graph   having no loops and multiple edges. The order and size of  are denoted by  and  respectively in graphs is a wide branch of graph theory having many scientific and real-life applications. There are various types of distances studied in the literature.The distance  is the length of the shortest path between the vertices  and  in  [2]. The detour distance  is the length of the longest path between the vertices  and  in  [3]. The concept of centrality widely applied in social network analysis. Degree centrality is the simplest centrality measure to compute centre. Betweenness centrality measures used for analyzed cancer network.

How do centrality measures help to predict similarity patterns in molecular chemical structural graphs?

The proposed work uses centrality measures based heuristic method to improve the efficiency of the solution for the similarity search problem in molecular chemical graphs by effectively identifying central candidate or representative candidate nodes, which simplify the complex processes involved while detecting a large-sized maximal common connected edge subgraph. After analyzing the structure of the two input molecular chemical graphs, a Tensor Product graph is created. This newly built graph is further analyzed to get the similarity pattern of the input graphs. It is an open problem to decide which centrality measure selects the best central candidate node in Tensor Product graphs to get a large maximal common connected edge graph. Since each centrality measure is analyses, the given graph is uniquely based on its own specific aspects. The proposed work offers directions on using various centrality measures to determine a big-sized maximal common connected subgraph for two molecular chemical input graphs. It also analyses seven centrality measures to select the best candidate node in the Tensor Product graph of two input chemical molecular graphs. Based on the obtained results, the betweenness centrality and degree centrality measures exclusively help to get large-sized similarity patterns.

A Network Analysis Approach toward Adaptive Overt Narcissism Network.

The relationship between narcissistic personality and art and beauty appreciation has recently become the focus of research investigations. Adaptive narcissists raise their sense of worth in order to shield themselves from harm caused by others. Because they aspire to be more attractive, healthier, and successful versions of themselves, they frequently have greater success in life than the majority of people. Grandiose and overtly narcissistic behavior are the main recognized characteristics of an overt narcissist, which is currently regarded as a personality disorder that puts mental health and wellbeing at peril. On a random sample collection of data from 1101 respondents to an online questionnaire, we conducted a network analysis of the Adaptive Overt Narcissism Scale (AONS) items. In this study, we used a network analysis approach to examine the network structure of adaptive overt narcissism, as well as its relationships with psychological functioning. The present study utilized network analysis to investigate the centrality measures of items in the Adaptive Overt Narcissism Scale (AONS) and their interrelationships. Results indicated that item Q6.8 ("I appreciate art and beauty") had low betweenness, closeness, and strength centrality measures, indicating that it was less influential in the network. However, it also had negative expected influence, suggesting that its absence would have a destabilizing effect on the network. These results highlight the importance of the appreciation of art and beauty in deactivating the adaptive overt narcissist network. Further research is needed to explore the mechanisms underlying this relationship and its implications for narcissism prevention and intervention.

Exploring high scientific productivity in international co-authorship of a small developing country based on collaboration patterns

The number of published scientific paper grows rapidly each year, totaling more than 2.9 million annually. New methodologies and systems have been developed to analyze scientific production and performance indicators from large quantities of data available from the scientific databases, such as Web of Science or Scopus. In this paper, we analyzed the international scientific production and co-authorship patterns for the most productive authors from Serbia based on the obtained Web of Science dataset in the period 2006–2013. We performed bibliometric and scientometric analyses together with statistical and collaboration network analysis, to reveal the causes of extraordinary publishing performance of some authors. For such authors, we found significant inequality in distribution of papers over journals and countries of co-authors, using Gini coefficient and Lorenz curves. Most of the papers belong to multidisciplinary, interdisciplinary, and the field of applied sciences. We have discovered three specific collaboration patterns that lead to high productivity in international collaboration. First pattern corresponds to mega-authorship papers with hundreds of co-authors gathered in specific research groups. The other two collaboration patterns were found in mathematics and multidisciplinary science, mainly application of graph theory and computational methods in physical chemistry. The former pattern results in a star-shaped collaboration network with mostly individual collaborators. The latter pattern includes multiple actors with high betweenness centrality measure and identified brokerage roles. The results are compared with the later period 2014–2023, where high scientific production has been observed in some other fields, such as biology and food science and technology.

Stress of a graph and its computation

Stress is a centrality measure determined by the shortest paths passing through the given vertex. Noting that adjacency matrix playing an important role in finding the distance and the number of shortest paths between given pair of vertices, an interesting expression and also an algorithm are presented to find stress using adjacency matrix. The results and algorithm are suitably adopted to obtain betweenness centrality measure. Further results are extended to the cases of Cartesian product of graphs, corona graph and their special cases.

Abstract 4313: A novel gene regulatory network model identifies master regulators in cancer

Abstract Small-scale regulatory networks can model known biological processes; while large-scale genome-wide datasets can identify novel mechanisms. We developed a biophysical modeling framework that combines the accuracy of small-scale networks with the power of large-scale datasets. As a proof of principle, we implemented this framework on a cohort of 844 multiple myeloma (MM) patients’ (and 1092 TCGA breast cancer patients) z-normalized RNAseq data using t-Distributed Stochastic Neighbor Embedding to construct a disease-specific transcriptomic map, where genes closer to each other co-express within the cohort. Fuzzy c-means clustering is carried out to identify clusters of genes that are likely regulated by a common transcription factor (TF). We construct a gene regulatory network (GRN) for each cluster of co-expressing genes on a disease-specific transcriptomic map by identifying upstream TFs for each cluster using publicly available databases ENCODE and ChEA, and kinases that phosphorylate these TFs using PhosphoSitePlus and PhosphoPoint. An exhaustive list of TFs and kinases are reduced to a few key predictor variables using regression tree modeling for each gene in that cluster. This leads to a cascading network of kinases that phosphorylate TFs, which regulate expression of genes in a cluster. We derived a mechanistic model from first-principles to define functional relationships governing the GRN; where transcription, translation, and post-translational modifications are modeled using first-order reversible reaction kinetic equations. The patient-specific rate constants of the model are parametrized by single sample gene set enrichment analysis scores of key KEGG pathways like ribosome, protein synthesis, RNA degradation, etc. The system of differential equations, under steady-state, reduce to an algebraic equation that can predict the expression of every gene in a cluster from the expression of its upstream TFs and kinases alone, which is fitted to RNAseq data of 422 MM patients to estimate undetermined parameters. The remaining patients’ data is used to estimate the accuracy of the model using Pearson’s correlation (model predicted vs actual) coefficient, r. Out of 16,738 genes, 7,936 were predicted accurately (r>0.5), while the remaining genes were shown to have a significant overlap (hypergeometric test; p-value<1e-48 and representation factor = 7.27) with genes that have high variability in chromatin accessibility across patients. A reduced GRN with only accurately predicted genes is obtained for each cluster, followed by linking GRNs to each other through TFs and kinases that are featured in other GRNs; where betweenness centrality measures of the resulting directed graph identifies disease-specific master regulators. MYC, STAT3, CREB1, POLR2A, PLK1, and TP53 are found to be key hubs in MM network; similar analyses are being conducted for other cancers featured in TCGA. Citation Format: Praneeth Reddy Sudalagunta, Rafael Renatino Canevarolo, Mark Meads, Maria Coelho Silva, Xiaohong Zhao, Raghunandan Reddy Alugubelli, Joon-hyun Song, Erez Persi, Mehdi Damaghi, Kenneth H. Shain, Ariosto Silva. A novel gene regulatory network model identifies master regulators in cancer. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 4313.

Network Brokerage and Self-Rated Popularity Among Lao School-Based Children: Findings From Primary Survey Data

This study examines whether and how an individual’s subjective, or self-rated, popularity is related to one’s structural position in the peer network, as measured by betweenness centrality and structural hole measure. Data were drawn from the original fieldwork conducted in Laos ( N = 1,490; boys = 40%; Mage = 13), a low-income country in Southeast Asia. Using friendship nominations, we constructed a global network matrix based on which sociometric variables were calculated. Findings from hierarchical linear models showed that net of sociometric and other controls, network brokers—those who span more structural holes or bridge across more disconnected dyads pairs—indeed perceive themselves as being “more popular.” Subjective, net of objective, measure of popularity should be incorporated as a critical component in the lives of young people.

Bet-GAT: An Efficient Centrality-Based Graph Attention Model for Semi-Supervised Node Classification

Graph Neural Networks (GNNs) have witnessed great advancement in the field of neural networks for processing graph datasets. Graph Convolutional Networks (GCNs) have outperformed current models/algorithms in accomplishing tasks such as semi-supervised node classification, link prediction, and graph classification. GCNs perform well even with a very small training dataset. The GCN framework has evolved to Graph Attention Model (GAT), GraphSAGE, and other hybrid frameworks. In this paper, we effectively usd the network centrality approach to select nodes from the training set (instead of a traditional random selection), which is fed into GCN (and GAT) to perform semi-supervised node classification tasks. This allows us to take advantage of the best positional nodes in the network. Based on empirical analysis, we choose the betweenness centrality measure for selecting the training nodes. We also mathematically justify why our proposed technique offers better training. This novel training technique is used to analyze the performance of GCN and GAT models on five benchmark networks—Cora, Citeseer, PubMed, Wiki-CS, and Amazon Computers. In GAT implementations, we obtain improved classification accuracy compared to the other state-of-the-art GCN-based methods. Moreover, to the best of our knowledge, the results obtained for Citeseer, Wiki- CS, and Amazon Computer datasets are the best compared to all the existing node classification methods.

Distributed Rendezvous placement for producer mobility support in NDN-IoT

Named Data Networking is a new networking architecture proposed as a potential design for the next generation of networks. However, supporting the mobility of producers in NDN-based IoT systems remains an open challenge for researchers to address. To tackle this problem, the NDN research group introduced a solution called KITE [1]. Since this solution is an initial design, some issues to be resolved remain, including the rendezvous placement selection. The location of the rendezvous servers can significantly reduce the path stretch efficiently. This paper focuses on improving the KITE solution by addressing two specific issues. First, we aim to determine the optimal number of rendezvous servers to use in the network. Second, we seek to identify the best locations for the rendezvous servers to minimize the path stretch efficiently. We propose an evaluation metric called average hop count based on network centrality measures to select the number and the location for the rendezvous. We evaluate the Average hop count under different network centrality measures using random scale-free network topology. Our results show that the betweenness centrality measure performs better than other measures, and we, therefore, suggest using it as a metric for selecting the number of rendezvous servers and their locations.

Resistance-distance betweenness: Definition, properties, and application in complex linguistic networks

Betweenness centrality measures how a node lies on the paths between other nodes. We propose the so-called resistance-distance betweenness (RDB) which takes a new viewpoint on scoring paths and contribution of each node in the paths. This new betweenness is developed based on the two facts: (1) the resistance distance characterizes the aggregate distance between two nodes in the context of electrical networks; (2) the resistance distance has an edge-wise decomposition form. Thus, the RDB of a node is defined as the sum of resistance distance components associated with the node’s incident edges, which measures how a node lies on the “aggregated path” between other nodes. The basic properties of RDB and its relation and difference from current-flow betweenness (CFB) are investigated. Then, we apply the RDB to the study of an open question in linguistic networks, i.e., to check whether the Yuan poems written by Han and non-Han poets are similar. We also compare the performance of RDB with CFB. The betweenness analysis of the character co-occurrence networks of Han’s and non-Han’s poems provides a fresh and quantitative perspective to support that Han’s and non-Han’s poems are similar in general.

Identification of key gene signatures and their characterization by expression correlation with drug sensitivity in smoking-associated oral squamous cell carcinoma.

Oral squamous cell carcinoma (OSCC), a most frequent type of head-and-neck cancer, is becoming more common and posing a substantial health risk. Using a network biology strategy, this study intended to find and investigate critical genes associated with OSCC. The extended protein-protein interaction networks for differentially expressed genes related to smoking and nonsmoking conditions of OSCC were constructed and visualized using Cytoscape software. The hub genes/proteins were determined based on degree and betweenness centrality measures and then evaluated and validated for expression using the Gene Expression Profiling Interactive Analysis 2 (GEPIA2), and their relationship to the sensitivity of small molecules was discovered utilizing the Gene Set Cancer Analysis (GSCA) web server. A total of 596 differentially expressed genes were screened, and four genes, interleukin (IL)-6, JUN, tumor necrosis factor (TNF), and vascular endothelial growth factor A (VEGFA), were identified as hub proteins, and their expression and overall survival in head-and-neck cancers were further investigated using GEPIA2. TNF and VEGFA gene expressions were considerably greater in cancers when compared to normal samples, while JUN and IL-6 gene expressions were not statistically significant. Further, these hub proteins are found to have a substantial favorable correlation with overall survival of head-and-neck cancer patients. Finally, GSCA was used to predict gene-specific potential drugs that act on these molecules by combining mRNA expression and drug sensitivity data from the Genomics of Drug Sensitivity in Cancer and the Cancer Therapeutics Response Portal. The hub genes/proteins identified in this study could help researchers better understand the molecular processes involved in the progression and metastasis of oral cancer in smokers.

Incorporating auxiliary information in betweenness measure for input–output networks

The betweenness centrality plays an important role in input–output analysis. Existing betweenness measures are mostly defined based on the intermediate flow network, without accounting for node-level information which can be of great value in some applications. Here we propose a novel betweenness centrality measure that incorporates available node-specific auxiliary information for weighted, directed networks with a specific focus on input–output network analysis. The proposed measure is defined upon strongest paths reflecting the pull effects of sectors in an economy, which distinguishes itself from many other classical versions. The search of strongest paths is done by the Dijkstra’s algorithm. The proposed betweenness integrates the structural information of a network and auxiliary information which may come from sources beyond the network. Through two simulation experiments and applications to the 2018 national input–output network of China, we demonstrate the importance and effectiveness of incorporating auxiliary information relevant to the research objectives for betweenness computation and node centrality assessment. The implementation is publicly available in an R package ionet.

SocialMedia2Traffic: Derivation of Traffic Information from Social Media Data

Traffic prediction is a topic of increasing importance for research and applications in the domain of routing and navigation. Unfortunately, open data are rarely available for this purpose. To overcome this, the authors explored the possibility of using geo-tagged social media data (Twitter), land-use and land-cover point of interest data (from OpenStreetMap) and an adapted betweenness centrality measure as feature spaces to predict the traffic congestion of eleven world cities. The presented framework and workflow are termed as SocialMedia2Traffic. Traffic congestion was predicted at four tile spatial resolutions and compared with Uber Movement data. The overall precision of the forecast for highly traffic-congested regions was approximately 81%. Different data processing steps including ways to aggregate data points, different proxies and machine learning approaches were compared. The lack of a universal definition on a global scale to classify road segments by speed bins into different traffic congestion classes has been identified to be a major limitation of the transferability of the framework. Overall, SocialMedia2Traffic further improves the usability of the tested feature space for traffic prediction. A further benefit is the agnostic nature of the social media platform’s approach.