Network-level Measures Research Articles

Predicting treatment outcomes in major depressive disorder using brain magnetic resonance imaging: a meta-analysis.

Recent studies have provided promising evidence that neuroimaging data can predict treatment outcomes for patients with major depressive disorder (MDD). As most of these studies had small sample sizes, a meta-analysis is warranted to identify the most robust findings and imaging modalities, and to compare predictive outcomes obtained in magnetic resonance imaging (MRI) and studies using clinical and demographic features. We conducted a literature search from database inception to July 22, 2023, to identify studies using pretreatment clinical or brain MRI features to predict treatment outcomes in patients with MDD. Two meta-analyses were conducted on clinical and MRI studies, respectively. The meta-regression was employed to explore the effects of covariates and compare the predictive performance between clinical and MRI groups, as well as across MRI modalities and intervention subgroups. Meta-analysis of 13 clinical studies yielded an area under the curve (AUC) of 0.73, while in 44 MRI studies, the AUC was 0.89. MRI studies showed a higher sensitivity than clinical studies (0.78 vs. 0.62, Z = 3.42, P = 0.001). In MRI studies, resting-state functional MRI (rsfMRI) exhibited a higher specificity than task-based fMRI (tbfMRI) (0.79 vs. 0.69, Z = -2.86, P = 0.004). No significant differences in predictive performance were found between structural and functional MRI, nor between different interventions. Of note, predictive MRI features for treatment outcomes in studies using antidepressants were predominantly located in the limbic and default mode networks, while studies of electroconvulsive therapy (ECT) were restricted mainly to the limbic network. Our findings suggest a promise for pretreatment brain MRI features to predict MDD treatment outcomes, outperforming clinical features. While tasks in tbfMRI studies differed, those studies overall had less predictive utility than rsfMRI data. Overlapping but distinct network-level measures predicted antidepressants and ECT outcomes. Future studies are needed to predict outcomes using multiple MRI features, and to clarify whether imaging features predict outcomes generally or differ depending on treatments.

Spatial food webs in the Barents Sea: atlantification and the reorganization of the trophic structure.

Climate change affects ecosystems at several levels: by altering the spatial distribution of individual species, by locally rewiring interspecific interactions, and by reorganizing trophic networks at larger scales. The dynamics of marine food webs are becoming more and more sensitive to spatial processes and connections in the seascape. As a case study, we study the atlantification of the Barents Sea: we compare spatio-temporal subsystems at three levels: the identity of key organisms, critically important interactions and the entire food web. Network analysis offers quantitative measurements, including centrality indices, trophic similarity indices, a topological measure of interaction asymmetry and network-level measures. We found that atlantification alters the identity of key species (boreal demersals becoming hubs), results in strongly asymmetric interactions (dominated by haddock), changes the dominant regulation regime (from bottom-up to wasp-waist control) and makes the food web less modular. Since the results of food web analysis may be quite sensitive to network construction, the aggregation of food web data was explicitly studied to increase the robustness of food web analysis. We found that an alternative, mathematical aggregation algorithm better preserves some network properties (e.g. density) of the original, unaggregated network than the biologically inspired aggregation into functional groups. This article is part of the theme issue 'Connected interactions: enriching food web research by spatial and social interactions'.

Brain magnetic resonance imaging outperforms clinical severity ratings in the prediction of treatment outcomes in major depressive disorder

IntroductionMajor depressive disorder (MDD) is a prevalent and disabling condition. Approximately 30-50% of patients do not respond to first-line medication or psychotherapy. Therefore, several studies have investigated the predictive potential of pretreatment severity rating or neuroimaging features to guide clinical approaches that can speed optimal treatment selection.ObjectivesTo evaluate the performance of 1) severity ratings (scores of Hamilton Depression/Anxiety Scale, illness duration, and sleep quality, etc.) and demographic characteristic and 2) brain magnetic resonance imaging (MRI) features in predicting treatment outcomes for MDD. Second, to assess performance variations among varied modalities and interventions in MRI studies.MethodsWe searched studies in PubMed, Embase, Web of Science, and Science Direct databases before March 22, 2023. We extracted a confusion matrix for prediction in each study. Separate meta-analyses were performed for clinical and MRI studies. The logarithm of diagnostic odds ratio [log(DOR)], sensitivity, and specificity were conducted using Reitsma’s random effect model. The area under curve (AUC) of summary receiver operating characteristic (SROC) curve was calculated.Subgroup analyses were conducted in MRI studies based on modalities: resting-state functional MRI (rsfMRI), task-based fMRI (tbfMRI), and structural MRI (sMRI), and interventions: antidepressant (including selective serotonin reuptake inhibitors [SSRI]) and electroconvulsive therapy (ECT). Meta-regression was conducted 1) between clinical and MRI studies and 2) among modality or intervention subgroups in MRI studies.ResultsWe included ten studies used clinical features covering 6494 patients, yielded a log(DOR) of 1.42, AUC of 0.71, sensitivity of 0.61, and specificity of 0.74. In terms of MRI, 44 studies with 2623 patients were included, revealing an overall log(DOR) of 2.53. The AUC, sensitivity, and specificity were 0.89, 0.78, and 0.75.Studies using MRI features had a higher sensitivity (0.89 vs. 0.61) in predicting treatment outcomes than clinical features (P < 0.001). RsfMRI had higher specificity (0.79 vs. 0.69) than tbfMRI subgroup (P = 0.01). No significant differences were found between sMRI and other modalities, nor between antidepressants (SSRIs and others) and ECT. Antidepressant studies primarily identified predictive imaging features in limbic and default mode networks, while ECT mainly focused on limbic network.ConclusionsOur findings suggest a robust promise for pretreatment brain MRI features in predicting treatment outcomes in MDD, offering higher accuracy than clinical studies. While tasks in tbfMRI studies differed, those studies overall had less predictive utility than rsfMRI data. For MRI studies, overlapping but distinct network level measures predicted outcomes for antidepressants and ECT.Disclosure of InterestNone Declared

Post global financial crisis multiplex financial network structure longevity: Does it predict re-globalization? Empirical investigation for 234 countries

Research background: International capital flows show decreasing trend after each financial crisis as countries become unwilling to invest in risky counterparties. Recent discussions over structural changes within the global financial network suggest that its structure may be still undergoing some changes. Uncovered weaknesses of over-connectedness with unstable un-ally countries during global financial crisis were further highlighted by recent Covid-19 pandemic. However, previous research suggest that important structural changes happened after global financial crisis. Thus, such situation raises a question whether post global financial crisis multiplex network structure is long-term and is it exhibiting re-globalization pattern as suggested by re-globalization megatrend. Purpose of the article: The main objective of the paper is to assess post global financial crisis multiplex financial network structure longevity and to identify whether it exhibits re-globalization pattern. Methods: Multiplex financial network mapping, network topological analysis and structure longevity methods were employed to evaluate network connectedness, topological structural properties of multiplex financial network and its’ structure longevity. 5-layer multiplex financial network was mapped for different types of capital flows for each layer: net direct and portfolio investments in debt, equity and net banking assets for each year in the post global financial crisis period 2009–2020. Each network layer employed bilateral data from up to 234 countries of the world. Next step of research employed a set of network-level measures defining topological features of each network in the period of 2009–2020 in order to capture the trend of structural changes and evidence of re-globalization. The final stage analyzes multiplex financial network structure by layer and country longevity. Findings & value added: Analysis reveals that stock (positions) multiplex financial network aggregate connectedness, i.e., density and value, increased during post- global financial crisis period until Covid-19 pandemic period, thus, suggesting that globalization in terms of stock is continuing. Topological structural properties of multiplex financial network changed as number of strongly and weakly connected countries decreased and more countries became included in network. Longevity analysis of layer and country structure within multiplex financial network reveal that banking and debt investments are increasing since 2018, while equity part is decreasing. Developed countries, i.e., the United Kingdom and Germany, decrease in multiplex financial network structure, while developing, especially from Asia region (i.e., Hong Kong SAR, China and Singapore) increase. Such empirical results support the re-globalization megatrend in terms of investment type and counterparty structure as highlighted by most recent scientific discussion showing that it started early after global financial crisis. Research also uncovers some regional trends of Asia region increasing in investment network structure, while Europe decreasing. As developing countries attract more and more investment and further develop, global cost saving and, thus, returns from global investment into these countries may be decreasing, resulting in lower gain from globalization. Our analysis may facilitate investment strategy decisions by suggesting that even though gains from globalization might be decreasing, instead of localization, countries could follow ally investment strategy to manage the risk of over-dependency from unstable counterparty while preserving globalization wealth.

Cybersecurity threats in the age of IoT: A review of protective measures

The Internet of Things (IoT) heralds a new era of connectivity, transforming the way devices interact and share information. With this transformative potential comes a concomitant rise in cybersecurity threats that pose significant challenges to the integrity of the IoT ecosystem. This paper undertakes a comprehensive examination of cybersecurity threats in the age of IoT, categorizing them into data breaches, malware attacks, physical manipulation, and more. Counteracting these threats necessitates a multifaceted approach encompassing device-level security, network-level measures, and effective management practices. Protective measures discussed include secure boot processes, encryption protocols, and the implementation of intrusion detection systems. However, persistent challenges, such as device diversity and resource constraints, underscore the need for ongoing research and development. Emerging technologies like blockchain, edge computing, and artificial intelligence offer promising avenues to bolster IoT security. In the pursuit of a secure IoT future, this paper emphasizes the critical importance of collaboration and collective efforts. The key findings underscore the centrality of robust security measures in the IoT landscape. The call to action resonates throughout the paper, urging stakeholders to unite in addressing challenges, embracing emerging technologies, and ensuring the responsible growth of IoT. Together, through shared knowledge and collaborative endeavors, we can forge a secure foundation for the continued expansion of IoT technologies

Analysis and mining of an election-based network using large-scale twitter data: a retrospective study.

The user-generated Twitter data are a rich source of study and research that reflects the various social, economic, political, and other issues affecting people across the world. Analysis of the social interactions among users, who express themselves online, reveals different internal dynamics and provides detailed insights into real-world phenomena. In this paper, the structure and dynamics of the state assembly election-based tweet-reply network have been studied, as generated by Twitter users across the country of India for a period of 6-weeks. We study the flow of Twitter activity pertaining to the West Bengal assembly elections, along with the identification of the hashtags used by the three main political contenders. This information is used to identify the cluster-level dominance in the Twitter network over the 6-weeks of study. It is observed that this cluster dominance information is representative of the actual outcome of the elections, and can be effectively used as a forecasting tool. The collected tweets are used for lexicon-based emotion detection and further analysis. This highlights the reaction of the social media users in response to the events related to the election. It is observed that fear is the dominant emotion, while happiness is scarce in the opinions expressed during the studied duration. Next, the study and analysis of the complete reply-based social networks during weeks 1, 4, and 6 are undertaken. Important political and media actors are identified with standard network-level measures toward determining the efforts put in by the different clusters and individual actors involved in the election to control the network dominance.

Stakeholder engagement variability across public, private and public-private partnership projects: A data-driven network-based analysis.

Stakeholder engagement is generally considered one of the most pertinent factors impacting project outcomes. However, there is lacking empirical evidence documenting patterns of stakeholder engagement and their potential differences between public, private and public-private partnership (PPP) projects. This study leverages social network research methods to capture and quantitively compare these engagement structures. Stakeholder network data were collected by an online questionnaire from 17 public, 30 private and 9 PPP projects. A series of network-based analyses were subsequently applied to the data at both the level of individual project stakeholders and entire project stakeholder ecologies. A statistically significant difference (p<0.05) exists among the network-level measures of network size, edge number, density and betweenness centralization across the three project types. Among these four network measures, the density varies significantly (p<0.05) between 'within budget' and cost overrun projects for the private and PPP projects. The top-5 stakeholder lists with respect to three node-level centrality values reveal distinctive differences across the three project types. To further interpret the data, exponential random graph models were also applied to determine the most statistically prevalent network motifs within each type of project. Again, statistically significant differences were found across these three project types. The findings consistently point to structural differences in patterns of stakeholder engagement across the public and private domain and illustrate the applicability of network data and analytical techniques to monitoring and managing complex webs of relationships among actors who affect and are affected by diverse types of projects.

Effects of an Oral Contraceptive on Dynamic Brain States and Network Modularity in a Serial Single-Subject Study.

Hormonal contraceptive drugs are used by adolescent and adult women worldwide. Increasing evidence from human neuroimaging research indicates that oral contraceptives can alter regional functional brain connectivity and brain chemistry. However, questions remain regarding static whole-brain and dynamic network-wise functional connectivity changes. A healthy woman (23 years old) was scanned every day over 30 consecutive days during a naturally occurring menstrual cycle and again a year later while using a combined hormonal contraceptive. Here we calculated graph theory-derived, whole-brain, network-level measures (modularity and system segregation) and global brain connectivity (characteristic path length) as well as dynamic functional brain connectivity using Leading Eigenvector Dynamic Analysis and diametrical clustering. These metrics were calculated for each scan session during the serial sampling periods to compare metrics between the subject’s natural and contraceptive cycles. Modularity, system segregation, and characteristic path length were statistically significantly higher across the natural compared to contraceptive cycle scans. We also observed a shift in the prevalence of two discrete brain states when using the contraceptive. Our results suggest a more network-structured brain connectivity architecture during the natural cycle, whereas oral contraceptive use is associated with a generally increased connectivity structure evidenced by lower characteristic path length. The results of this repeated, single-subject analysis allude to the possible effects of oral contraceptives on brain-wide connectivity, which should be evaluated in a cohort to resolve the extent to which these effects generalize across the population and the possible impact of a year-long period between conditions.

Network analysis of a community-wide teenage pregnancy prevention initiative.

Although US teenage birth rates substantially decreased over the past two decades, it still remains the highest in the developed world. More innovative, community-wide initiatives are needed to combat the issue. In Oklahoma County, Oklahoma, the Central Oklahoma Teen Pregnancy Prevention Collaboration is utilizing the collective impact model to convene multiple organizations with the goal of making systems-level changes related to teenage pregnancy within the community. This study used an interorganizational network analysis to evaluate the current strength of relationships between organizations in the Collaboration. An interorganizational network survey assessed collaborative relationships (e.g. information sharing and joint planning) within the network. Using R software, network diagrams were developed to depict partner relationships, and network measures, including node-, group- and network-level measures, were calculated. The network depiction (n = 23) revealed that the network core was composed of organizations from multiple sectors, and the backbone organization served as the most central organization for most centrality measures. This study provides insight into how organizational relationships can be assessed in order to increase community capacity to address teenage pregnancy. Continuous monitoring of the strength of relationships is important to ensure success in achieving goals as well as collective impact.

The structural architecture of international industry networks in the global economy.

Globalisation continuously produces novel economic relationships mediated by flows of goods, services, capital, and information between countries. The activity of multinational corporations (MNCs) has become a primary driver of globalisation, shaping these relationships through vast networks of firms and their subsidiaries. Extensive empirical research has suggested that globalisation is not a singular process, and that variation in the intensity of international economic interactions can be captured by ‘multiple globalisations’, however how this differs across industry sectors has remained unclear. This paper analyses how sectoral variation in the ‘structural architecture’ of international economic relations can be understood using a combination of social network analysis (SNA) measures based on firm-subsidiary ownership linkages. Applying an approach that combines network-level measures (Density, Clustering, Degree, Assortativity) in ways yet to be explored in the spatial networks literature, a typology of four idealised international network structures is presented to allow for comparison between sectors. All sectoral networks were found to be disassortative, indicating that international networks based on intraorganisational ties are characterised by a core-periphery structure, with professional services sectors such as Banks and Insurance being the most hierarchically differentiated. Retail sector networks, including Food & Staples Retailing, are the least clustered while the two most clustered networks—Materials and Capital Goods—have also the highest average degree, evidence of their extensive globalisations. Our findings suggest that the multiple globalisations characterising international economic interactions can be better understood through the ‘structural architecture’ of sectoral variation, which result from the advantages conferred by cross-border activity within each.

Performance comparison of some centrality measures used in detecting anomalies in directed social networks

Recently, researchers have shown an increased interest in combining Statistical Process Control/Monitoring and Social Network Analysis. One approach to detect anomalies in social networks is to monitor some summary statistics of the network structure using control charts. We aim in this study to conduct a performance comparison among some network centrality measures (betweenness, closeness, and degree) in terms of their ability in detecting anomalies using control charts. Although directed networks include more information on the nodes communication than the undirected ones, they are rarely considered and evaluated in literature. Hence, the performance comparison is conducted assuming weighted and unweighted directed networks. Two network-level measures are used; the average and standard deviation for each metric. Our simulation results revealed that the degree centrality measure in most cases is recommended to be used for both weighted and unweighted networks. Also, it can generally be concluded that the average of a centrality measure performs better than the standard deviation in detecting anomalies. However, if the structural change is major in weighted networks specifically, we recommend the standard deviation of the centrality measure to be used.

Performance analysis of high technology collaborative networks: A case of medical device manufacturing

Technological advancements, potential cost savings and quest for better ways of treating the medical problems have given rise to a new kind of manufacturing environment. This environment is characterized by enhanced competencies involving user experience based creations, co-innovation, sharing resources and use of advanced technologies in a networking mode. The paper presents a cluster of enterprises evolved as a High Technology Collaborative Network (HTCN) to envisage affordable manufacturing of a medical device. The paper makes several contributions (i) quantifies the tie strength among the enterprises of the HTCN (ii) determines agent and network level measures for studying the pattern of collaborative relationships (iii) demonstrates performance analysis of a real life case of HTCN (iv) performs structural investigations of the HTCN and states various implications at operational and strategic level. We identify various hard and soft factors to quantify the collaborative relationships and construct the social network. The proposed methodology is helpful in analysing the performance and effective management of the network.

Comparing Shared Patient Networks Across Payers.

Measuring care coordination in administrative data facilitates important research to improve care quality. To compare shared patient networks constructed from administrative claims data across multiple payers. Social network analysis of pooled cross sections of physicians treating prevalent colorectal cancer patients between 2003 and 2013. Surgeons, medical oncologists, and radiation oncologists identified from North Carolina Central Cancer Registry data linked to Medicare claims (N = 1735) and private insurance claims (N = 1321). Provider-level measures included the number of patients treated, the number of providers with whom they share patients (by specialty), the extent of patient sharing with each specialty, and network centrality. Network-level measures included the number of providers and shared patients, the density of shared-patient relationships among providers, and the size and composition of clusters of providers with a high level of patient sharing. For 24.5% of providers, total patient volume rank differed by at least one quintile group between payers. Medicare claims missed 14.6% of all shared patient relationships between providers, but captured a greater number of patient-sharing relationships per provider compared with the private insurance database, even after controlling for the total number of patients (27.242 vs 26.044, p < 0.001). Providers in the private network shared a higher fraction of patients with other providers (0.226 vs 0.127, p < 0.001) compared to the Medicare network. Clustering coefficients for providers, weighted betweenness, and eigenvector centrality varied greatly across payers. Network differences led to some clusters of providers that existed in the combined network not being detected in Medicare alone. Many features of shared patient networks constructed from a single-payer database differed from similar networks constructed from other payers' data. Depending on a study's goals, shortcomings of single-payer networks should be considered when using claims data to draw conclusions about provider behavior.

An examination of the temporal nature of shared athlete leadership: A longitudinal case study of a competitive youth male ice hockey team

Athlete leadership has been shown to be a shared practice. That is, numerous team members provide leadership through formal and informal leadership positions. Researchers have typically examined athlete leadership using cross-sectional designs, while largely ignoring the nature of this construct over time. Taking a longitudinal approach, the current study used social network analysis (SNA) to examine shared athlete leadership within a competitive, highly ranked youth male ice hockey team. Members of the team (N = 20) completed roster-based surveys related to task and social athlete leadership at five time points during the season. Density (i.e. the overall amount of leadership) and degree centralization (i.e. the distribution of leadership) were the two network level measures used to examine the temporal nature of shared athlete leadership over the five time points. The findings demonstrated significant increases in the amount of task athlete leadership over the course of the season; however, task athlete leadership did not become more shared with time. Further, there were significant increases in both the amount of social athlete leadership within the team as well as the degree to which social leadership was shared among team members as the season progressed. The results of the current study shed light on the dynamic nature of athlete leadership and highlight the value of using longitudinal designs and SNA to examine shared athlete leadership.

Embeddedness, Prosociality, and Social Influence: Evidence from Online Crowdfunding

This paper examines how (1) a crowdfunding campaign’s prosociality (the production of a public versus private good), (2) the social network structure (embeddedness) among individuals advocating for the campaign on social media, and (3) the volume of social media activity around a campaign jointly determine fundraising from the crowd. Integrating the emerging literature on social media and crowdfunding with the literature on social networks and public goods, we theorize that prosocially, public-oriented crowdfunding campaigns will benefit disproportionately from social media activity when advocates’ social media networks exhibit greater levels of embeddedness. Drawing on a panel dataset that combines campaign fundraising activity associated with more than 1,000 campaigns on Kickstarter with campaign-related social media activity on Twitter, we construct network-level measures of embeddedness between and amongst individuals initiating the latter, in terms of transitivity and topological overlap. We demonstrate that Twitter activity drives a disproportionate increase in fundraising for prosocially oriented crowdfunding campaigns when posting users’ networks exhibit greater embeddedness. We discuss the theoretical implications of our findings, highlighting how our work extends prior research on the role of embeddedness in peer influence by demonstrating the joint roles of message features and network structure in the peer influence process. Our work suggests that when a transmitter’s message is prosocial or cause-oriented, embeddedness will play a stronger role in determining influence. We also discuss the broader theoretical implications for the literatures on social media, crowdfunding, crowdsourcing, and private contributions to public goods. Finally, we highlight the practical implications for marketers, campaign organizers, and crowdfunding platform operators.

Increased inflammation and brain glutamate define a subtype of depression with decreased regional homogeneity, impaired network integrity, and anhedonia

Combined increases in peripheral inflammation and brain glutamate may identify a subtype of depression with distinct neuroimaging signatures. Two contrasting subgroups of depressed subjects—with and without combined elevations in plasma C-reactive protein (CRP) and basal ganglia glutamate (high and low CRP-Glu, respectively) were identified by hierarchical clustering using plasma CRP (indexing peripheral inflammation) and magnetic resonance spectroscopy (MRS)-based measurement of left basal ganglia glutamate. High CRP-Glu group status was associated with greater severity of anhedonia and cognitive and motor slowing. Local- and network-level measures of functional integrity were determined using brain oxygen level-dependent (BOLD)-oscillatory activity and graph theory. Greater decreases in concordance of oscillatory activity between neighboring voxels (Regional Homogeneity ‘ReHo’, p < 0.01) within the MRS volume-of-interest was associated with the High CRP-Glu subgroup. Using brain-wide, CRP-Glu ReHo contrast maps, a covariance network of 41 regions-of-interest (ROIs) with similar ReHo decreases was identified in the High CRP-Glu group and was located to brain structures previously implicated in depression. The 41-ROI network was further decomposed into four subnetworks. ReHo decreases within Subnetwork4—comprised of reward processing regions —was associated with anhedonia. Subnetwork4 ReHo also predicted decreased network integrity, which mediated the link between local ReHo and anhedonia in the Low but not High CRP-Glu group. These findings suggest that decreased ReHo and related disruptions in network integrity may reflect toxic effects of inflammation-induced increases in extrasynaptic glutamate signaling. Moreover, local BOLD oscillatory activity as reflected in ReHo might be a useful measure of target-engagement in the brain for treatment of inflammation-induced behaviors.

Heritability Estimation of Reliable Connectomic Features.

Brain imaging genetics is an emerging research field to explore the underlying genetic architecture of brain structure and function measured by different imaging modalities. However, not all the changes in the brain are a consequential result of genetic effect and it is usually unknown which imaging phenotypes are promising for genetic analyses. In this paper, we focus on identifying highly heritable measures of structural brain networks derived from diffusion weighted imaging data. Using the twin data from the Human Connectome Project (HCP), we evaluated the reliability of fractional anisotropy measure, fiber length and fiber number of each edge in the structural connectome and seven network level measures using intraclass correlation coefficients. We then estimated the heritability of those reliable network measures using SOLAR-Eclipse software. Across all 64,620 network edges between 360 brain regions in the Glasser parcellation, we observed ~5% of them with significantly high heritability in fractional anisotropy, fiber length or fiber number. All the tested network level measures, capturing the network integrality, segregation or resilience, are highly heritable, with variance explained by the additive genetic effect ranging from 59% to 77%.

Network nestedness as generalized core-periphery structures.

The concept of nestedness, in particular for ecological and economical networks, has been introduced as a structural characteristic of real interacting systems. We suggest that the nestedness is in fact another way to express a mesoscale network property called the core-periphery structure. With real ecological mutualistic networks and synthetic model networks, we reveal the strong correlation between the nestedness and core-periphery-ness (likeness to the core-periphery structure), by defining the network-level measures for nestedness and core-periphery-ness in the case of weighted and bipartite networks. However, at the same time, via more sophisticated null-model analysis, we also discover that the degree (the number of connected neighbors of a node) distribution poses quite severe restrictions on the possible nestedness and core-periphery parameter space. Therefore, there must exist structurally interwoven properties in more fundamental levels of network formation, behind this seemingly obvious relation between nestedness and core-periphery structures.

Using Social Network Analysis to Assess Mentorship and Collaboration in a Public Health Network.

IntroductionAddressing chronic disease burden requires the creation of collaborative networks to promote systemic changes and engage stakeholders. Although many such networks exist, they are rarely assessed with tools that account for their complexity. This study examined the structure of mentorship and collaboration relationships among members of the Healthy Aging Research Network (HAN) using social network analysis (SNA).MethodsWe invited 97 HAN members and partners to complete an online social network survey that included closed-ended questions about HAN-specific mentorship and collaboration during the previous 12 months. Collaboration was measured by examining the activity of the network on 6 types of products: published articles, in-progress manuscripts, grant applications, tools, research projects, and presentations. We computed network-level measures such as density, number of components, and centralization to assess the cohesiveness of the network.ResultsSixty-three respondents completed the survey (response rate, 65%). Responses, which included information about collaboration with nonrespondents, suggested that 74% of HAN members were connected through mentorship ties and that all 97 members were connected through at least one form of collaboration. Mentorship and collaboration ties were present both within and across boundaries of HAN member organizations.ConclusionSNA of public health collaborative networks provides understanding about the structure of relationships that are formed as a result of participation in network activities. This approach may offer members and funders a way to assess the impact of such networks that goes beyond simply measuring products and participation at the individual level.

Mobile Phone Assessment in Egocentric Networks: A Pilot Study on Gay Men and Their Peers.

Mobile phone-based data collection encompasses the richness of social network research. Both individual-level and network-level measures can be recorded. For example, health-related behaviors can be reported via mobile assessment. Social interactions can be assessed by phone-log data. Yet the potential of mobile phone data collection has largely been untapped. This is especially true of egocentric studies in public health settings where mobile phones can enhance both data collection and intervention delivery, e.g. mobile users can video chat with counselors. This is due in part to privacy issues and other barriers that are more difficult to address outside of academic settings where most mobile research to date has taken place. In this article, we aim to inform a broader discussion on mobile research. In particular, benefits and challenges to mobile phone-based data collection are highlighted through our mobile phone-based pilot study that was conducted on egocentric networks of 12 gay men (n = 44 total participants). HIV-transmission and general health behaviors were reported through a mobile phone-based daily assessment that was administered through study participants' own mobile phones. Phone log information was collected from gay men with Android phones. Benefits and challenges to mobile implementation are discussed, along with the application of multi-level models to the type of longitudinal egocentric data that we collected.