Highest Eigenvector Centrality Research Articles

Analysing inhibitors to BIM implementation: a social network theoretical perspective

Purpose Building information modelling (BIM) implementation in the design, construction and operations (DCO) industry is increasingly becoming essential. While BIM has been adopted on a larger scale in many developed economies, its acceptance is still in the embryonic phases for developing nations in the DCO industry. This study aims to identify the inhibitors to BIM implementation through the social network theoretical lens, intending to understand the associations among the barriers in the Indian context. Subsequently, recommend strategies to mitigate the barriers from the academic practitioner’s perspective. Design/methodology/approach A mixed methods research was adopted, commencing with comprehensive literature reviews to recognise various inhibitors to BIM implementation. These identified barriers were further examined through the questionnaire survey (n = 71). BIM implementation barrier network (BIBN) was created using University of California at Irvine Network (UCINET) is a powerful social network analysis software that functions on the principle of social network theory. The experts’ opinions were captured through the BIBN network through interviews. Network properties such as eigen vector centrality, betweenness centrality, degree centrality, in-degree and out-degree and clustering coefficient were computed, and the metrics were analysed further. Findings Twenty-six BIM implementation barriers were initially identified. A questionnaire survey was conducted. The chain reaction can be minimised by prioritising and regulating these barriers. The issues were categorised into fourfold clusters (standardisation, policy and process, cultural and human resources, change management and operational) issues were generated from the exploratory factor analysis (EFA). The obstacles and barriers resulting from the other main barriers associated with it can be minimised by reducing the challenges with high eigenvector centrality but low betweenness importance. Practical implications This study proves to accelerate sustainable BIM implementation growth in developing nations; this research study assists BIM stakeholders in developing coping mechanisms to monitor and remove BIM implementation barriers. Originality/value Analysing the associativity of the BIM implementation barriers through sociograms for developing nations is a novel concept with this research.

Cascading failure resilience analysis and recovery of automotive manufacturing supply chain networks considering enterprise roles

With the rapid development of global trade, the member enterprises in the automotive manufacturing supply chain are widely spread around the world. As a result, the scale of the automotive manufacturing supply chain network has become increasingly large, and the structure has become more complex. At the same time, the connection between enterprises is becoming closer. On one hand, the close business relationships between enterprises and the vast network structure accelerate the operation efficiency of the automotive manufacturing supply chain network. On the other hand, it also exposes the entire network system to high risks of cascading failures when facing disruptions due to enterprise production interruptions. Therefore, this work aims to establish a realistic cascading failure model for the automotive manufacturing supply chain network to explore its cascading failure characteristics. Based on this model, a two-stage recovery strategy is proposed to enhance the network’s cascading failure resilience. Experimental results demonstrate the following findings: (1) underload nodes that emerge after load redistribution are the main cause of cascading failure resilience degradation in automotive manufacturing supply chain networks; (2) cascading failure resilience is negatively correlated with the load-exponential coefficient, the capacity lower-limit coefficient, and the attenuation-tolerance coefficient; (3) among four different types of attacks, cascading failures caused by the high eigenvector centrality attack strategy and the high degree attack strategy impose the most severe damage on the automotive manufacturing supply chain network; (4) the proposed two-stage recovery strategy can significantly improve the cascading failure resilience of the automotive manufacturing supply chain network.

Social Network, Food Patterns, Physical Activity and Associations with Overweight and Obesity in Adolescents from a School in Rural Brazil.

The objective of this study was to evaluate the social network, food patterns, physical activity, and their associations with overweight/obesity in adolescents from a school in rural Brazil. Students from a rural school in Northeast Brazil (n = 90) completed questionnaires on sociodemographic characteristics, food consumption, physical activity, and a name generator. Social networks were constructed using students' social proximity ties. Principal component analysis was performed to determine food patterns, and logistic models were used to investigate variables associated with overweight/obesity. Most participants were girls (62.9%), and the proportion of overweight/obesity was 30% among adolescents. Students cited 2070 people from their networks (family, friends at school, friends outside of school, and others). Among them, the family had the highest degree of influence (61%) in the network and had the most shared meals with adolescents (47%). Adolescents' perception of their family members' body size as obese, compared to normal or underweight, was prevalent (51%). Adolescents with unhealthy food patterns were 72% more likely to be categorized as overweight/obese, and eigenvector centrality was also associated with overweight/obesity (OR = 5.88, 95% CI = 1.08-32.03). Adolescents presented a social network with strong family influence, in which a high percentage of overweight/obesity was observed. Adolescents with high eigenvector centrality were more likely to be in the overweight/obesity category. Additionally, overweight/obesity was associated with unhealthy food patterns in the family network.

Linking FDI and trade network topology with the COVID-19 pandemic

Globalization has considerably increased the movement of people and goods around the world, which constitutes a key channel of viral infection. Increasingly close economic links between countries speeds up the transfer of goods and information, and the knock-on effect of economic crises, but also the transmission of diseases. Foreign direct investment (FDI) and trade establish clear ties between countries of origin and destination, and it is along these chains that contagious phenomena can unfold. In this paper, we investigate whether countries’ centrality in both global production and trade network corresponds to higher COVID-19 infection and mortality rates. Merging data on EU-27 greenfield FDI and international trade with data on COVID-19 infections and deaths, we find that countries mostly exposed to the COVID-19 outbreak are those characterized by a higher eigenvector centrality. This result is robust to the use of an alternative measure of network centrality and to the inclusion of other possible confounding factors.

Network modeling and stability improvement of the water-energy-fertilizer-food nexus flows based on global agricultural trade

Water, energy, fertilizer and food are vital for sustainable development, yet their nexuses within various agricultural trade patterns remain ambiguous. Therefore, we analyzed the nexus of these four elements in the global trade of both total and basic agricultural products, focusing on exporting countries from 2000 to 2019. Our approach involved coupling models, complex network analysis and mathematical programming. The results showed that: (1) In the total agricultural trade nexus flows, the export-import flow pairs with the highest average nexus of continents changed from Europe-Europe to Oceania-Asia. Netherlands-Germany maintained the export-import flow pairs with the highest nexus of countries. In the basic agricultural trade nexus flows, Oceania-Asia maintained the highest average nexus export-import flow pairs of continents. Denmark-Germany maintained the export-import flow pairs with the highest nexus of countries. (2) The basic agricultural trade nexus flows network exhibited the higher density and average clustering coefficient. Argentina, Brazil and Canada played pivotal roles in maintaining high closeness centrality. Congo, Dominican Republic and Bosnia and Herzegovina displayed considerable potential on the indirectness of the network with high eigenvector centrality. (3) To improve the stability of the nexus flows network, taking the total agricultural trade nexus of the US, Netherlands and China as examples, the main largest beneficiary countries were Vanuatu, Eswatini and Saint Lucia. Taking the basic agricultural trade nexus of Canada, Brazil and China as examples, the main largest beneficiary countries were Kiribati and Niue. This study utilizes coupling and network models to analyze agricultural trade nexus flows, offering a deeper understanding of the complex nexus relationships within various agricultural trade patterns. Furthermore, the findings serve as a reference point for the formulation of strategies promoting sustainable development.

The Korean Movie Minari: A Big Data Analysis

The ultimate goal of this paper is to analyze 42 articles of Google regarding the Korean movie Minari. As a research tool for our goal, we used the software package NetMiner. A major point to note is that one word has the highest frequency (1,247 tokens) and the highest proportion (57.8%). More specifically, it occurs 1,247 times in 42 articles of Google. A further point to note is that topic 11 is the most occurred one in 40 articles, followed by topic 3, topic 4, and topic 10, in that order. When it comes to the use of keywords in 42 articles, the word film is the most widely used one, followed by the word Minari, the word family, the name Jacob, and the name Monica, in that order. Talking about degree centrality, the word family has the highest degree centrality, followed by the name Monica, the word Minari, and the word film, in that order. It is worthwhile noting, on the other hand, that the word family has the highest closeness centrality since the distance among the word family and the other words is the closest. Finally, this paper argues that the name Monica has the highest Eigenvector centrality since it has the most neighbors. Thus, the name Monica counts as the most influential and important.

외국인 유학생 관련 보도 동향의 네트워크 분석

In this study, we explored how Korean society looked at foreign students even before the implementation of the Study Korea Project policy through a big data analysis of news trends. We collected 14,121 news articles related to foreign students from January 1, 1990 to November 25 2022, and words with high degree centrality and eigenvector centrality were labeled as keywords, and words with highest TF-IDF score were considered as topics. As a result of co-occurrence word network analysis, we examined the process of agenda formation centering on the Gyeongnam region prior to the implementation of the Study Korea Project. In addition, although news articles related to foreign students increased due to the implementation of the Study Korea Project, COVID-19 had a decisive effect on the increase in the issue salience of foreign students. As a result of analyzing the topics by three different periods, we identified that 'the perspective of international trade' was the dominant viewpoint in Korean society, rather than 'the perspective of global education'. Based on these results, an urgent call for the internationalization of higher education based on an intercultural perspective was requested to respect and welcome the diversity of foreign students.

Abstract 15030: Hidden Sources of Inequity in Heart Failure Care Transitions: A Mixed-Method Network Analysis of Clinical Notes

Introduction: Fragmentation in heart failure (HF) care transitions occur disproportionately among those adversely affected by social drivers of health. Social network analysis (SNA) may provide new insights into barriers to equitable care. Purpose: To assess the nature and structure of clinician networks across health system settings of care during care transitions. Methods: An explanatory sequential mixed-methods design was used. We stratified a purposeful sample (n=11) from a cohort of adults (n=1269) first hospitalized for HF between 2016 and 2018 by race, Medicaid use, and Area Deprivation Index, adjusting for risk (3M Clinical Risk Groups Severity of Illness Score and Charlson Comorbidity Index). EHR clinical notes were used to construct patients' clinician networks 1-year before, during, and after the index hospitalization using patient-sharing (2-mode) SNA. Patients' clinician positional and structural network measures were integrated with qualitative analyses of clinical notes. Results: Socioeconomically advantaged patients used fewer acute care services and lived longer. They tended to have higher network density and clinicians more centrally located in the health system network earlier and across settings and frequent telephone notes between visits that indicated reciprocal communication patterns among patients and clinicians shown in contents. Close care relationships and early involvement of influential providers measured by high Eigenvector centrality may be vital for smooth care transitions. Conclusions: Barriers to care coordination may result from variability in clinician networks. Well-connected clinician teams and consistent and reciprocal communication between patients and outpatient care teams are associated with more effective care coordination. Patients with clinicians in central and bridge positions within a health system network may receive higher quality care due to greater social capital and influence.

Metabolome-wide association study suggests antagonistic responses to cadmium and selenium in diseased human lungs

Background and Aim: Cadmium (Cd) is a toxic environmental metal that binds selenium (Se) and contributes to many lung diseases. Humans have widespread exposures to Cd through diet and cigarette smoking, and studies in rodent models show that Se can protect against Cd toxicities. We performed metabolome-wide association study (MWAS) of Cd and Se in diseased and non-diseased human lungs to test for possible Cd-Se interactions. Methods: We analyzed tissue from 31 human lung explants, including 25 from patients with end-stage lung diseases, including idiopathic pulmonary fibrosis, cystic fibrosis, and chronic obstructive lung disease (COPD)/emphysema. We used ICP-MS (Inductively Coupled Plasma Mass Spectrometry) to measure Cd and Se, and liquid chromatography-high-resolution mass spectrometry (LC-HRMS) to conduct metabolomics analysis. Results: Metabolic pathway associations with Cd were similar to those previously observed in experimental models, including amino acid, lipid and energy-related pathways, many of which were also associated with Se. Two-way hierarchical cluster analysis (HCA) of metabolites associated with Cd revealed separation of individuals according to disease, with COPD/emphysema in the highest Cd cluster, and non-diseased individuals in the lowest Cd cluster. HCA of Se-associated metabolites revealed that COPD/emphysema also clustered by lower Se compared to others. More pathway associations occurred for the ratio of Cd:Se than either Cd or Se alone, suggesting an antagonistic relationship, and network analysis showed that most metabolites were associated with either Cd or Se. A relatively small number of polyunsaturated fatty acids and metabolites associated with inflammatory signaling had high Eigenvector centrality scores, suggesting a central mechanism of antagonistic interaction. Conclusions: Overall, the data show that metabolic pathway responses in human lung vary with Cd:Se ratio in a pattern suggesting that Se is antagonistic to Cd toxicity. Keyword: Human lung disease, environmental metals, metabolic disruption, high-resolution metabolomics.

Profiling the most highly cited scholars from China: Who they are. To what extent they are interdisciplinary

Since the beginning of the 21st century, an increasing number of Chinese researchers have joined the ranks of the world’s top scientists. Some international organizations have observed this phenomenon and ranked the world’s top Chinese researchers. However, investigation of highly cited interdisciplinary research (IDR) scholars is insufficient, although IDR tends to have a greater social impact. Looking at the top 2% of the world’s Chinese scholars, this study analyzes the structural attributes of IDR by those top scholars in detail using network analysis, cluster analysis, block modeling, and quadratic assignment procedure analysis. The results show that the proportion of highly cited scholars in technical categories is higher than in social categories. The fields of artificial intelligence and image processing, oncology and carcinogenesis, plus neurology and neurosurgery serve as bridges across disciplines, with materials, energy, and artificial intelligence and image processing having higher eigenvector centrality. The field of social sciences has the widest range of IDR activities, but cooperation within this field is low. Forty-two of the world’s first-class universities are in China, and of the world’s top 2% scholars who come from China, 46.3% work for these institutions. The research themes of highly cited academics from World First-Class universities in China are most similar to the themes of scholars from universities in China with first-class academic disciplines. There are differences between non-university and university scholars in terms of research topics. It is suggested that the government can promote a triple-helix effect (public institute, industrial enterprise, and research school) so that organizations of different natures can produce synergistic effects.

Study on the Hierarchical Structure of the “Belt and Road” Aviation Network Based on K-Core Analysis

Since the “Belt and Road” Initiative, air transport has become the second largest mode of transport after ocean shipping and has contributed more and more to international trade between the countries. To explore the hierarchical characteristics of the aviation network of the “Belt and Road,” analyze the relationship between levels, and identify the core layer of the network, k-core analysis based on the “degree” value was designed and performed; the data of airports and air routes were collected to construct the “Belt and Road” aviation network model; then, k-core decomposition was conducted to reduce the size of nodes layer by layer from the outside to the inside, and a network structure model with 19 levels was obtained; the relationship between the coreness and centrality of nodes in the network was investigated, and the changes in the attribute of networks at different levels and the connection between networks were explored; according to the structural characteristics of the network, the “Belt and Road” aviation network was divided into three categories: the core layer, the middle layer, and the detail layer. The results showed that the highest-level network obtained by k-core decomposition was used as the core layer of the “Belt and Road” aviation network, including 53 airports in 37 countries, all of which have high degree centrality and eigenvector centrality; the core layer network exhibits strong “small world” characteristics; there is little difference between the degree values of nodes, and the network has high stability. The status of China’s airports in the core layer network is not prominent; the four regional sectors such as East Asia, Southeast Asia, Central Asia, and the Middle East are closely connected regarding air transport. Europe is relatively weakly connected with three sectors: East Asia, Southeast Asia, and Central Asia; geographical factors are still the dominant factors determining the status of hub airports along the “Belt and Road.”

Eigenvector PageRank difference as a measure to reveal topological characteristics of the brain connectome for neurosurgery.

Applying graph theory to the human brain has the potential to help prognosticate the impacts of intracerebral surgery. Eigenvector (EC) and PageRank (PR) centrality are two related, but uniquely different measures of nodal centrality which may be utilized together to reveal varying neuroanatomical characteristics of the brain connectome. We obtained diffusion neuroimaging data from a healthy cohort (UCLA consortium for neuropsychiatric phenomics) and applied a personalized parcellation scheme to them. We ranked parcels based on weighted EC and PR, and then calculated the difference (EP difference) and correlation between the two metrics. We also compared the difference between the two metrics to the clustering coefficient. While EC and PR were consistent for top and bottom ranking parcels, they differed for mid-ranking parcels. Parcels with a high EC centrality but low PR tended to be in the medial temporal and temporooccipital regions, whereas PR conferred greater importance to multi-modal association areas in the frontal, parietal and insular cortices. The EP difference showed a weak correlation with clustering coefficient, though there was significant individual variation. The relationship between PageRank and eigenvector centrality can identify distinct topological characteristics of the brain connectome such as the presence of unimodal or multimodal association cortices. These results highlight how different graph theory metrics can be used alone or in combination to reveal unique neuroanatomical features for further clinical study.

Social Network Analysis: #BlackLivesMatter Distribution at Actor Level and System Level

#BlackLivesMatter accompanies several cases of discrimination against the black community. The hashtag was spread by actors who have great influences on Twitter users. The actors create communication network which connected to each other to form opinions about the Black Lives Matter movement. Researchers conducted a study to determine the distribution of #BlackLivesMatter at the actor level for the period 20-27 April 2021 in Twitter. The study used quantitative methods and a positivistic paradigm with a Social Network Analysis (SNA) approach. The results show that the actor with the highest degree of centrality is @jeanmessiha with 238 interactions, the actor with the highest betweenness centrality is @helloagain0611 with a value of 0.000049, the actor with the highest eigenvector centrality is @jeanmessiha with a value of 1 and there are 1,416 actors who have closeness centrality. # BlackLivesMatter has a low diameter value so that it spreads quickly but not too widely, not much reciprocity occurs, not concentrated in one dominant cluster but spread widely in several clusters. The actors play a role in spreading diverse opinions regarding Black Lives Matter, thus creating free discussion in several clusters on Twitter. Opinion widely spread on Twitter creates public opinion regarding the Black Lives Matter movement.

Compensatory functional connectome changes in a rat model of traumatic brain injury.

Penetrating cortical impact injuries alter neuronal communication beyond the injury epicentre, across regions involved in affective, sensorimotor and cognitive processing. Understanding how traumatic brain injury reorganizes local and brain wide nodal interactions may provide valuable quantitative parameters for monitoring pathological progression and recovery. To this end, we investigated spontaneous fluctuations in the functional MRI signal obtained at 11.1 T in rats sustaining controlled cortical impact and imaged at 2- and 30-days post-injury. Graph theory-based calculations were applied to weighted undirected matrices constructed from 12 879 pairwise correlations between functional MRI signals from 162 regions. Our data indicate that on Days 2 and 30 post-controlled cortical impact there is a significant increase in connectivity strength in nodes located in contralesional cortical, thalamic and basal forebrain areas. Rats imaged on Day 2 post-injury had significantly greater network modularity than controls, with influential nodes (with high eigenvector centrality) contained within the contralesional module and participating less in cross-modular interactions. By Day 30, modularity and cross-modular interactions recover, although a cluster of nodes with low strength and low eigenvector centrality remain in the ipsilateral cortex. Our results suggest that changes in node strength, modularity, eigenvector centrality and participation coefficient track early and late traumatic brain injury effects on brain functional connectivity. We propose that the observed compensatory functional connectivity reorganization in response to controlled cortical impact may be unfavourable to brain wide communication in the early post-injury period.

Analysis of simultaneous visual and complex neural dynamics during cognitive learning to diagnose ASD.

The interactions between gaze processing and neural activities mediate cognition. The present paper aims to identify the involvement of visual and neural dynamics in shaping the cognitive behavior in Autism Spectrum Disorder (ASD). Electroencephalogram (EEG) and Eye-tracker signals of ASD and Typically Developing (TD) are recorded while performing two difficulty levels of a maze-based experimental task. During task, the performance metrics, complex neural measures extracted from EEG data using Visibility Graph (VG) algorithm and visual measures extracted from eye-tracker data are analyzed and compared. For both task levels, the cognition processing is examined via performance metrics (reaction-time and poor accuracy), gaze measures (saccade, fixation duration and blinkrate) and VG-based metrics (average weighted degree, clustering coefficient, path length, global efficiency, mutual information). An engagement in cognitive processing in ASD is revealed statistically by high reaction time, poor accuracy, increased fixation duration, raised saccadic amplitude, higher blink rate, reduced average weighted degree, global efficiency, mutual information as well as higher eigenvector centrality and path length. Over the course of repetitive trials, the cognitive improvement is although poor in ASD compared to TDs, the reconfigurations of visual and neural network dynamics revealed activation of Cognitive Learning (CL) in ASD. Furthermore, the correlation of gaze-EEG measures reveal that independent brain region functioning is not impaired but declined mutual interaction of brain regions causes cognitive deficit in ASD. And correlation of EEG-gaze measures with clinical severity measured by Autism Diagnostic Observation Schedule(ADOS) suggest that visual-neural activities reveals social behavior/cognition in ASD. Thus, visual and neural dynamics together support the revelation of the cognitive behavior in ASD.

How Centrality of Driver Nodes Affects Controllability of Complex Networks

Recently, the controllability of complex networks has become a hot topic in the field of network science, where the driver nodes play a key and central role. Therefore, studying their structural characteristics is of great significance to understand the underlying mechanism of network controllability. In this paper, we systematically investigate the nodal centrality of driver nodes in controlling complex networks, we find that the driver nodes tend to be low in-degree but high out-degree nodes, and most of driver nodes tend to have low betweenness centrality but relatively high closeness centrality. We also find that the tendencies of driver nodes towards eigenvector centrality and Katz centrality show very similar behaviors, both high eigenvector centrality and high Katz centrality are avoided by driver nodes. Finally, we find that the driver nodes towards PageRank centrality demonstrate a polarized distribution, i.e., the vast majority of driver nodes tend to be low PageRank nodes whereas only few driver nodes tend to be high PageRank nodes.

Eigenvector centrality based algorithm for finding a maximal common connected vertex induced molecular substructure of two chemical graphs

The physical and biological properties of a chemical molecule entity are related to its structure. One of the basic widely accepted principles in chemistry is that compounds with similar structures frequently share similar physicochemical properties and biological activities. The process of finding structural similarities between chemical structures of molecules helps to identify the common behavior of these molecules. A familiar approach to capture the structural similarity between two chemical compounds is to detect a maximal Common Connected vertex induced Subgraph (CCS) in their molecular chemical graphs. The proposed algorithm detects a maximal CCS by checking the induced property of the vertices which are collected by performing a DFS search on the tensor product graph of two input molecular chemical graphs. The DFS search will start from the node which has the highest eigenvector centrality in the tensor product graph. The significance of the proposed work is that it uses eigenvector centrality to predict the root node of the DFS search tree, so that the resulting sugraph gets more number of nodes (i.e. large size maximal CCS). The experimental results on synthetic and real chemical database, further ensure the competence of the proposed algorithm when compared with the existing works.

Description of collective team behaviours and team performance analysis of elite rugby competition via cooperative network analysis

ABSTRACT This study investigated cooperative passing interactions in elite rugby match play. Associations between team network metrics and match outcomes were also investigated. A cross-sectional approach was adopted, using data from four Australian Super Rugby teams, across five seasons. 44,178 passing actions were included across 321 team-fixture observations. Network metrics were calculated for each positional group within each match, and two statistical models were developed; First: a mixed-effects multinomial regression to identify differences between positional groups; and second: a mixed-effects binomial logistic regression to determine the association between team-level network metrics and match outcomes. Differences were identified between positional groups e.g. Halves had the highest out-degree centrality and betweenness, while Centres had higher eigenvector centrality than all other positions. Within the Forwards pack, the Back Row had greater in-degree, out-degree, and betweenness than the Tight Five. Regarding match outcomes, the model explained only 6.9% of variance, although greater in-degree centralisation (OR = 1.847 [1.241–2.749], p = 0.002) and lower eigenvector centralisation (OR = 0.655 [0.440–0.975]; p = 0.037) were associated with successful outcomes. Cooperative passing networks in rugby union may provide useful information to describe how various positions interact, and some behaviours may contribute towards successful team performance.

Modelling cyclists’ route choice using Strava and OSMnx: A case study of the City of Glasgow

Previous research has demonstrated the influence of street layout on travel behaviour; however, little research has been undertaken to explore these connections using detailed and robust street network analysis or cycling data. In this study, we harness state-of-the-art datasets to model cyclists’ route choice based on a case study of the City of Glasgow, Scotland. First, the social fitness network Strava was used to obtain datasets containing the number of cycling trips on each street intersection for the years 2017 and 2018. Second, we employed a Python toolkit to acquire and analyse the street networks. OSMnx was subsequently employed to quantify several commonly used centrality indices (degree, eigenvector, betweenness and closeness) to measure street layout. Due to the presence of spatial dependence, a spatial error model was used to model route choices. Model results demonstrate that: (1) cyclists’ movement models were consistent for the years 2017 and 2018; (2) the presence of a spillover effect suggests that cyclists tend to cycle in proximity to each other; and (3) cyclists avoid streets with high degree centrality values and prefer streets with high eigenvector centrality, betweenness centrality and closeness centrality. These findings reveal cyclists’ desired street layouts and can be taken into consideration for future interventions.

The consistency of individual centrality across time and networks in wild vervet monkeys.

Previous primate social network studies largely limited their focus to grooming and/or aggression networks, particularly among adult females. In addition, the consistency of individuals' network centrality across time and/or different networks has received little attention, despite this being critical for a global understanding of dynamic social structure. Here, we analyzed the grooming, aggression, and play social networks of a group of 26-28 wild vervet monkeys (Chlorocebus pygerythrus), including adults and juveniles, over two periods of 6 months. We collected data on grooming, play, and aggression using focal animal sampling with instantaneous recording and ad libitum sampling. We examined whether individuals' network centralities were consistent over the two periods and across networks, as well as the effect of age, sex, and dominance rank on three individual centrality metrics in each network and within each study period. We found that individuals were quite consistent in their network position from 1 year to the next despite changes in group composition. However, their network centralities were not correlated across networks, except for Strength and weighted Eigenvector centrality between grooming and aggression networks. We also found that in the aggression network, high-rankers showed the highest centrality in most network metrics (e.g., Degree, Strength, and Eigenvector centrality) and compared to males, females were most central in 2017 but not in 2018. In the grooming network, high-ranking females had the highest Eigenvector centrality, whereas in the play network, juvenile males had the highest Eigenvector centrality. Our findings corroborate previous findings on vervet monkeys. In addition, they show that individuals' network centralities may vary among networks and over time; thus highlighting the effect of sociodemographics and behaviors' functions on the group level dynamics of social behavior.