INFERRING FINANCIAL STOCK RETURNS CORRELATION FROM COMPLEX NETWORK ANALYSIS

In Social Media the directed links formed between the users, are used for the transfer of information. Based on previous research, the rate of information transfer in a social network depends on the strength of connections of the user in the network, which is measured by the centrality value. In this paper, based on data collected from Twitter, we perform an analysis of eigenvector centrality approach of finding the influential users. We investigate the variation in indegree and eigenvector centrality of users participating in a hashtag in Twitter, with respect to change in the amount of interactions. Here interactions are: tweets, mentions and replies. We also investigate the relationship between indegree and eigenvector centrality in a given hashtag. We make the following interesting observations. First, in Twitter, users with high eigenvector centrality need not be influential users. Second, in a given hashtag, there is an increase in users with both high indegree and eigenvector centrality when there are more user interactions. Here interactions are: tweets, mentions and replies, indicating both indegree and eigenvector centrality should be considered when finding influential users. Third, there is a positive correlation between indegree and eigenvector centrality.

Background: People who inject drugs (PWID) are a well-identified risk population for HIV infection. The risk networks of PWID have been implicated as possible modulators of both HIV risk and educational interventions among this population. In order to further understand the nature of risk networks, we examined how individual characteristics were associated with influential network position based on high closeness, betweenness, or eigenvector network centrality. These centrality measures assess an individual’s importance or potential to influence others based on their connections, closeness is based on proximity to others, betweenness on acting as an intermediary between others, and eigenvector on connection to highly connected peers. Methods: Using data from Athens, Greece collected as part of the Transmission Reduction Intervention Project (TRIP), we constructed a risk network and identified individuals in the top quartile of the distribution for each centrality measure. Using logistic regression, we identified associations between being in the top quartile of each centrality measure and individual characteristics such as demographics, risk behaviors, and altruistic behaviors. We also performed a series of sensitivity analyses to evaluate robustness of the results to the definition of high centrality (e.g., the top 50%, 20%, and 10% of the distribution of the centrality measure). Results: The TRIP study contained a total 356 individuals after restriction to the largest connected component and censoring of individuals with missing covariate information a sample of 231 PWID was extracted from the TRIP study population. Individuals who injected at least once per day were more likely to have high closeness (odds ratio (OR) = 3.36; 95% confidence interval (CI) = 1.57, 8.42), betweenness (OR = 2.22 95% CI = 1.06, 4.67), or eigenvector centrality (OR = 4.50 95% CI = 1.89, 10.68). Individuals who engaged in sex without a condom were less likely to have high closeness centrality (OR = 0.18 95% CI =0.07, 0.45) or high eigenvector centrality (OR = 0.19 95% CI =0.07, 0.49). Individuals who reported higher numbers of sex partners were more likely to have high betweenness centrality (OR = 1.04 95% CI =1.00, 1.08). Years living in the project recruitment area was also associated with high eigenvector centrality (OR = 1.04 95% CI = 1.00, 1.09). Conclusions: Injection frequency was consistently related with network position and likely indicates that individuals who inject more frequently have more interactions with other PWID. Unprotected sex was also related to network centrality and may reflect that less central

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

Abstract. This study aims to analyze the characteristics of global virtual water trade (GVWT), such as the connectivity of each trader, vulnerable importers, and influential countries, using degree and eigenvector centrality during the period 2006–2010. The degree centrality was used to measure the connectivity, and eigenvector centrality was used to measure the influence on the entire GVWT network. Mexico, Egypt, China, the Republic of Korea, and Japan were classified as vulnerable importers, because they imported large quantities of virtual water with low connectivity. In particular, Egypt had a 15.3 Gm3 year−1 blue water saving effect through GVWT: the vulnerable structure could cause a water shortage problem for the importer. The entire GVWT network could be changed by a few countries, termed "influential traders". We used eigenvector centrality to identify those influential traders. In GVWT for food crops, the USA, Russian Federation, Thailand, and Canada had high eigenvector centrality with large volumes of green water trade. In the case of blue water trade, western Asia, Pakistan, and India had high eigenvector centrality. For feed crops, the green water trade in the USA, Brazil, and Argentina was the most influential. However, Argentina and Pakistan used high proportions of internal water resources for virtual water export (32.9 and 25.1 %); thus other traders should carefully consider water resource management in these exporters.

Based on the quarterly data of mutual funds in China from the fourth quarter of 2004 to the fourth quarter of 2019, this paper constructs a series of complex bipartite networks based on the overlapped portfolios of mutual funds and then explores the influences of fund network position on mutual fund’s investment behavior and performance. This paper finds that a mutual fund with shorter information transmission path to other entities in the fund network (i.e., having higher closeness centrality) or with stronger ties with those entities in important information positions (i.e., having higher eigenvector centrality) will achieve better investment performance. However, a stronger mediating role over the potential information flow of the fund network (i.e., having higher betweenness centrality) cannot help a mutual fund increase performance. The empirical results also indicate that a mutual fund holding stock portfolios with high valuation difficulties caused by the market or fundamental information uncertainty will achieve better investment performance, while holding hard-to-value portfolios caused by limited public information will reduce the performance of the fund. Furthermore, high closeness centrality or eigenvector centrality can help mutual funds deal with the disclose problems of public information, thus reducing the likelihood of a mutual fund holding hard-to-value portfolios caused by limited public information to achieve worse performance. Eigenvector centrality brings information advantages about company fundamentals, so it is easier for a mutual fund with high eigenvector centrality to profit from holding hard-to-value portfolios caused by the fundamental information uncertainty. The conclusions of this paper can enhance our understanding of the fund network and its information mechanism and shed new light on mutual fund’s information advantages and related asset allocation strategies.

BackgroundLiving systems are associated with Social networks — networks made up of nodes, some of which may be more important in various aspects as compared to others. While different quantitative measures labeled as “centralities” have previously been used in the network analysis community to find out influential nodes in a network, it is debatable how valid the centrality measures actually are. In other words, the research question that remains unanswered is: how exactly do these measures perform in the real world? So, as an example, if a centrality of a particular node identifies it to be important, is the node actually important?PurposeThe goal of this paper is not just to perform a traditional social network analysis but rather to evaluate different centrality measures by conducting an empirical study analyzing exactly how do network centralities correlate with data from published multidisciplinary network data sets.MethodWe take standard published network data sets while using a random network to establish a baseline. These data sets included the Zachary's Karate Club network, dolphin social network and a neural network of nematode Caenorhabditis elegans. Each of the data sets was analyzed in terms of different centrality measures and compared with existing knowledge from associated published articles to review the role of each centrality measure in the determination of influential nodes.ResultsOur empirical analysis demonstrates that in the chosen network data sets, nodes which had a high Closeness Centrality also had a high Eccentricity Centrality. Likewise high Degree Centrality also correlated closely with a high Eigenvector Centrality. Whereas Betweenness Centrality varied according to network topology and did not demonstrate any noticeable pattern. In terms of identification of key nodes, we discovered that as compared with other centrality measures, Eigenvector and Eccentricity Centralities were better able to identify important nodes.

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.

We propose a novel algorithm, FURS (Fast and Unique Representative Subset selection) to deterministically select a set of nodes from a given graph which retains the underlying community structure. FURS greedily selects nodes with high-degree centrality from most or all the communities in the network. The nodes with high-degree centrality for each community are usually located at the center rather than the periphery and can better capture the community structure. The nodes are selected such that they are not isolated but can form disconnected components. The FURS is evaluated by quality measures, such as coverage, clustering coefficients, degree distributions and variation of information. Empirically, we observe that the nodes are selected such that most or all of the communities in the original network are retained. We compare our proposed technique with state-of-the-art methods like SlashBurn, Forest-Fire, Metropolis and Snowball Expansion sampling techniques. We evaluate FURS on several synthetic and real-world networks of varying size to demonstrate the high quality of our subset while preserving the community structure. The subset generated by the FURS method can be effectively utilized by model-based approaches with out-of-sample extension properties for inferring community affiliation of the large-scale networks. A consequence of FURS is that the selected subset is also a good candidate set for simple diffusion model. We compare the spread of information over time using FURS for several real-world networks with random node selection, hubs selection, spokes selection, high eigenvector centrality, high Pagerank, high betweenness centrality and low betweenness centrality-based representative subset selection.

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.

Part of the excitation energy transfer (EET) characteristics of the photosystem II (PSII) comes from the interconnection between pigments. To understand the correlation between the EET and the pigments’ interaction structure, we construct a network from the EET rates which are related to both the distance between the pigments (chlorophylls and pheophytins) and their spatial orientations. Especially, we investigate how well the PS II core complex’s EET functionality can be explained by using only the network topology in Thermosynechococcus vulcanus 1.9 °A. Starting from the Förster theory, we construct a network of EET pathways. For an analysis of the network structure, we calculate common network-structural measures like betweenness centrality, eigenvector centrality and weighted clustering. These measures can reflect the role of individual pigments in the EET network. In our work, we found that some well-known properties were reproduced by the network analysis of the simplified network, which means that the topology of the network encodes functionally relevant information. For example, from the network structural analysis, we can infer that most of the chlorophyll molecules (clorophylls) in the pigment-protein complex CP47 have heightened probability to transfer energy compared with other chlorophylls. We also see that the active branch chlorophylls in the reaction center are characterized by a high eigenvector centrality, a high betweenness centrality and a low weighted clustering coefficient. This is indicative of functionally important vertices.

BackgroundUsing opinion leaders to accelerate the dissemination of evidence-based public health practices is a promising strategy for closing the gap between evidence and practice. Network interventions (using social network data to accelerate behavior change or improve organizational performance) are a promising but under-explored strategy. We aimed to use mobile phone technology to rapidly and inexpensively map a social network and identify opinion leaders among community health workers in a large HIV program in western Kenya.MethodsWe administered a five-item socio-metric survey to community health workers using a mobile phone short message service (SMS)-based questionnaire. We used the survey results to construct and characterize a social network of opinion leaders among respondents. We calculated the extent to which a particular respondent was a popular point of reference (“degree centrality”) and the influence of a respondent within the network (“eigenvector centrality”).ResultsSurveys were returned by 38/39 (97%) of peer health workers contacted; 52% were female. The median survey response time was 13.75 min (inter-quartile range, 8.8–38.7). The total cost of relaying survey questions through a secure cloud-based SMS aggregator was $8.46. The most connected individuals (high degree centrality) were also the most influential (high eigenvector centrality). The distribution of influence (eigenvector centrality) was highly skewed in favor of a single influential individual at each site.ConclusionsLeveraging increasing access to SMS technology, we mapped the network of influence among community health workers associated with a HIV treatment program in Kenya. Survey uptake was high, response rates were rapid, and the survey identified clear opinion leaders. In sum, we offer proof of concept that a “mobile health” (mHealth) approach can be used in resource-limited settings to efficiently map opinion leadership among health care workers and thus open the door to reproducible, feasible, and efficient empirically based network interventions that seek to spread novel practices and behaviors among health care workers.

Supplier dependence and R&D intensity: The moderating role of network centrality and interconnectedness

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.

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.

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.