Network Analyzer Measurements Research Articles

Impacts of Brazilian Green Coffee Production and Its Logistical Corridors on the International Coffee Market

Background: The coffee industry is one of the most important world supply chains, with an estimated consumption of two billion cups daily, making it the most consumed beverage worldwide. Coffee beans are primarily grown in tropical countries, with Brazil accounting for almost 50% of the production. The objective of this study is to examine the Brazilian trade between 2018 and 2022, focusing on state producers, logistical corridors, and importer countries. Methods: The methodology approach revolves around a quantitative method using Social Network Analysis measures. Results: The results reveal a massive concentration in local production (99.5%—Minas Gerais), port movements (99.9%—Santos, Itaguai, and Rio de Janeiro), and country buyers (80.9%—the United States, United Kingdon, and Japan). Conclusions: The study concludes that the Brazilian green coffee supply chain relies on a fragile and overloaded logistical network. Due to that, this study indicates that the stakeholders and decision-makers involved must consider this high concentration of production in some areas and companies. They must also address the bottlenecks in logistical corridors and the fierce competition involved in acquiring and processing Brazilian coffee production because these factors can drastically affect the revenue of the companies operating in this sector.

Synthesis and Characterization of Hematite Fe2O3 Nanofiller for Enhanced Dielectric and Microwave-Absorbing Properties in PTFE Composites

This paper presents the synthesis of hematite Fe2O3 nanofiller from mill scales and its application in polytetrafluoroethylene (PTFE) composites for enhanced dielectric and microwave-absorbing properties. The nanofiller was obtained through 9 hours of high-energy ball milling, resulting in a particle size reduction 43.6 to 11.05 nm. The PTFE/Fe2O3 composites were fabricated by dispersing different concentration of Fe2O3 nanofillers using the dry powder processing technique. The structural and morphological characterization of the nanofiller and PTFE/Fe2O3 composites was carried out using X-ray diffraction (XRD) and field emission scanning electron microscopy (FESEM), respectively. The composites’ microwave absorption properties were analyzed utilizing vector network analyzer (VNA) measurements in the 8–12 GHz frequency range. Based on the findings from the results, as the percentage of filler increased from 5 to 15%wt, the composites' loss tangent and dielectric constant increased from 0.0272 to 0.0478 and 2.12 to 3.25, respectively, while their reduced signal transmission speed was between 2.21 and 2.07 x 108 m/s at 8 GHz and from 2.24 to 2.11 x 108 m/s at 12 GHz. These findings demonstrate that Fe2O3 nanoparticles are a suitable material for developing microwave-absorbing polymer composites within the 8–12 GHz frequency range.

Exploring the backward and forward linkages of production network in a developing country

This paper applies social network analysis (SNA) techniques to the input–output table in order to investigate the production network of Pakistan’s economy. Through different network measures, it identifies the sectors that exert significant direct and indirect impacts on other sectors. The findings reveal that, overall, sectors have weak forward linkages relative to backward linkages. The services sectors, particularly transportation and trade services, have the highest out-degree (a measure of forward linkages). In contrast, the manufacturing sectors demonstrate comparatively lower connectivity than the services sectors. The sectors of electricity, petroleum, and chemicals emerge as the most widely utilized inputs across various industries. Despite their relatively moderate immediate absorption in downstream industries, these sectors possess the potential to affect other industries indirectly. Considering the combined measures of network analysis, these sectors also emerge as the most crucial sectors in Pakistan’s production network. This suggests that enhancing efficiency in the energy, refining, and petrochemical industries will yield the highest economy-wide impact.

Phantom evaluation of electrical conductivity mapping by MRI: Comparison to vector network analyzer measurements and spatial resolution assessment.

To evaluate the performance of various MR electrical properties tomography (MR-EPT) methods at 3 T in terms of absolute quantification and spatial resolution limit for electrical conductivity. Absolute quantification as well as spatial resolution performance were evaluated on homogeneous phantoms and a phantom with holes of different sizes, respectively. Ground-truth conductivities were measured with an open-ended coaxial probe connected to a vector network analyzer (VNA). Four widely used MR-EPT reconstruction methods were investigated: phase-based Helmholtz (PB), phase-based convection-reaction (PB-cr), image-based (IB), and generalized-image-based (GIB). These methods were compared using the same complex images from a 1 mm-isotropic UTE sequence. Alternative transceive phase acquisition sequences were also compared in PB and PB-cr. In large homogeneous phantoms, all methods showed a strong correlation with ground truth conductivities (r > 0.99); however, GIB was the best in terms of accuracy, spatial uniformity, and robustness to boundary artifacts. In the resolution phantom, the normalized root-mean-squared error of all methods grew rapidly (>0.40) when the hole size was below 10 mm, with simplified methods (PB and IB), or below 5 mm, with generalized methods (PB-cr and GIB). VNA measurements are essential to assess the accuracy of MR-EPT. In this study, all tested MR-EPT methods correlated strongly with the VNA measurements. The UTE sequence is recommended for MR-EPT, with the GIB method providing good accuracy for structures down to 5 mm. Structures below 5 mm may still be detected in the conductivity maps, but with significantly lower accuracy.

Social network analysis of cell networks improves deep learning for prediction of molecular pathways and key mutations in colorectal cancer

Colorectal cancer (CRC) is a primary global health concern, and identifying the molecular pathways, genetic subtypes, and mutations associated with CRC is crucial for precision medicine. However, traditional measurement techniques such as gene sequencing are costly and time-consuming, while most deep learning methods proposed for this task lack interpretability. This study offers a new approach to enhance the state-of-the-art deep learning methods for molecular pathways and key mutation prediction by incorporating cell network information. We build cell graphs with nuclei as nodes and nuclei connections as edges of the network and leverage Social Network Analysis (SNA) measures to extract abstract, perceivable, and interpretable features that explicitly describe the cell network characteristics in an image. Our approach does not rely on precise nuclei segmentation or feature extraction, is computationally efficient, and is easily scalable. In this study, we utilize the TCGA-CRC-DX dataset, comprising 499 patients and 502 diagnostic slides from primary colorectal tumours, sourced from 36 distinct medical centres in the United States. By incorporating the SNA features alongside deep features in two multiple instance learning frameworks, we demonstrate improved performance for chromosomal instability (CIN), hypermutated tumour (HM), TP53 gene, BRAF gene, and Microsatellite instability (MSI) status prediction tasks (2.4%–4% and 7–8.8% improvement in AUROC and AUPRC on average). Additionally, our method achieves outstanding performance on MSI prediction in an external PAIP dataset (99% AUROC and 98% AUPRC), demonstrating its generalizability. Our findings highlight the discrimination power of SNA features and how they can be beneficial to deep learning models’ performance and provide insights into the correlation of cell network profiles with molecular pathways and key mutations.

Design, Simulation, and Fabrication of a 500 kV Ultrawideband Coaxial Matched Load and Its Connectors for Fast Transient Pulse Measurement Systems

In the past few decades, Pulsed Power (PP) has been one of the fastest growing technologies, with more and more systems frequently emerging in domains such as civil, medical and military. These systems are based on high-voltage pulses, up to several hundreds of kilovolts, with temporal parameters ranging from microsecond levels to sub-nanosecond levels. One of the biggest challenges in this technology is the accurate and precise measurement of the generated PP. The PP measurement systems must possess high-voltage and wideband properties simultaneously, which is often conflicting. The central elements of a PP measurement system are a voltage divider and a termination load. The work presented in this article is dedicated to the second element of the PP measurement system. This paper describes the development of a 50 Ω coaxial termination load and its connectors for a high power ultrawideband (UWB) pulse measurement systems. The principle roles of these devices are to serve as a dummy matched load for the former and to facilitate the connections between different components of the pulse measurement system for the latter. These devices are designed to withstand pulse voltage amplitudes at least up to 500 kV with temporal parameters, such as rise time and pulse duration, varying from nanosecond to sub-nanosecond ranges. The main challenge in the development of a high-voltage UWB termination load is the tradeoff between the high-voltage and wideband characteristics, both of them requiring opposite dimensional aspects for the load device. This challenge is overcame by the special exponential geometry of the load device. The design employs a 30 cm long low-inductance tubular ceramic 50 Ω resistor, enclosed in a critically dimensioned shielding conductor of an exponential inner profile. This shrinking coaxial structure makes it possible to maintain a good level of matching all along the 50 Ω load. The results obtained through 3D electromagnetic modeling and vector network analyzer measurements show good agreement and confirm the reflection coefficient below −27 dB up to at least 2.5 GHz for the load device. Moreover, calculations demonstrate that the load device is very well adapted for nanosecond and sub-nanosecond pulses with voltage peaks as high as 500 kV. These results demonstrate the high-voltage and UWB properties of the developed load device and prove the utilization of this device in the measurement systems for the accurate and precise measurements of the PP.

Mathematical optimization and machine learning to support PCB topology identification

Abstract. In this paper, we study an identification problem for schematics with different concurring topologies. A framework is proposed, that is both supported by mathematical optimization and machine learning algorithms. Through the use of Python libraries, such as scikit-rf, which allows for the emulation of network analyzer measurements, and a physical microstrip line simulation on PCBs, data for training and testing the framework are provided. In addition to an individual treatment of the concurring topologies and subsequent comparison, a method is introduced to tackle the identification of the optimum topology directly via a standard optimization or machine learning setup: An encoder-decoder sequence is trained with schematics of different topologies, to generate a flattened representation of the rated graph representation of the considered schematics. Still containing the relevant topology information in encoded (i.e., flattened) form, the so obtained latent space representations of schematics can be used for standard optimization of machine learning processes. Using now the encoder to map schematics on latent variables or the decoder to reconstruct schematics from their latent space representation, various machine learning and optimization setups can be applied to treat the given identification task. The proposed framework is presented and validated for a small model problem comprising different circuit topologies.

Bringing physics into the coarse‐grid selection: Approximate diffusion distance/effective resistance measures for network analysis and algebraic multigrid for graph Laplacians and systems of elliptic partial differential equations

AbstractIn a recent paper, the author examined a correlation affinity measure for selecting the coarse degrees of freedom (CDOFs) or coarse nodes (C nodes) in systems of elliptic partial differential equations (PDEs). This measure was applied to a set of relaxed vectors, which exposed the near‐nullspace components of the PDE operator. Selecting the CDOFs using this affinity measure and constructing the interpolation operators using a least‐squares procedure, an algebraic multigrid (AMG) method was developed. However, there are several noted issues with this AMG solver. First, to capture strong anisotropies, a large number of test vectors may be needed; and second, the solver's performance can be sensitive to the initial set of random test vectors. Both issues reflect the sensitive statistical nature of the measure. In this article, we derive several other statistical measures that ameliorate these issues and lead to better AMG performance. These measures are related to a Markov process, which the PDE itself may model. Specifically, the measures are based on the diffusion distance/effective resistance for such process, and hence, these measures incorporate physics into the CDOF selection. Moreover, because the diffusion distance/effective resistance can be used to analyze graph networks, these measures also provide a very economical scheme for analyzing large‐scale networks. In this article, the derivations of these measures are given, and numerical experiments for analyzing networks and for AMG performance on weighted‐graph Laplacians and systems of elliptic boundary‐value problems are presented.

Unveiling the cooperation dynamics in the photovoltaic technologies’ development

Cooperation studies have supported the understanding of possible technological development strategies adopted in the Research and Development (R&D) departments. This article introduces an approach associating Social Network Analysis and Salton measure to expand the analysis of inter-organizational cooperation networks. This approach was used in 37,122 patent families of photovoltaic (PV) technologies applied between 1998 and 2017. The results showed that patent assignees prioritize their developments exclusively in their internal R&D areas. It has been observed that closed innovation is more prevalent than open innovation, given the lower proportion of technologies developed in collaboration. When there is cooperation, they prioritize cooperation relations between organizations of the same nationality. Small companies have greater cooperation intensity than large ones because they are more dependent on the innovation ecosystem and seek growth and support for their technological development by partnerships. The intensity of this cooperation is classified as incipient, but smaller organizations have a unilateral dependence on the relationship, which is a clear definition of dominance in cooperative relations. The results regarding the intensity of cooperation and the mapping of these networks did not indicate different actors who assumed more significant roles, whether in terms of constancy, centrality or intermediations of these relations. The findings can support strategic decisions on R&D, prioritization of investments, identification of potential partnerships (or competitors) for technological development. Additionally, they can contribute to the definition of public policies based on promoting the development and use of PV technologies.

Analysis of Acid Diffusion Effects on Physical Properties of Polymer Composites: A Combined Study of Mechanical and Electrical Characterization

In this study, we examined the impact of carbon nanotube (CNT) concentration on the mechanical properties of epoxy/CNT composites under acid exposure. Samples with varying CNT concentrations (0% to 5%) were fabricated and characterized using dynamic mechanical analysis (DMA) and nanoindentation. Beyond the percolation threshold, the composites experienced decreased bulk mechanical properties due to CNT agglomeration. Acid exposure for one week and one month revealed a gradient of properties from the sample’s skin to its core. Overall, the composites exhibited modified physical properties, with degradation influenced by the CNT concentration. Higher concentrations acted as barriers but also created pathways for acid diffusion through pores surrounding CNT agglomerates. The agreement between nanoindentation and vector network analyzer (VNA) measurements further supported our findings. This convergence of mechanical and electromagnetic characterization techniques holds promise for wireless structural health monitoring (SHM) applications. Our study enhances the understanding of epoxy/CNT composites for SHM applications. The relationship between CNT concentration, acid exposure, and mechanical properties guides material selection and the development of real-time damage-detection techniques. Integrating multiple measurement techniques, as demonstrated by the agreement between nanoindentation and VNA data, provides a comprehensive understanding of structural behavior, improving SHM practices.

Spatio-Temporal Patterns of the SARS-CoV-2 Epidemic in Germany.

Results from an explorative study revealing spatio-temporal patterns of the SARS-CoV-2/ COVID-19 epidemic in Germany are presented. We dispense with contestable model assumptions and show the intrinsic spatio-temporal patterns of the epidemic dynamics. The analysis is based on COVID-19 incidence data, which are age-stratified and spatially resolved at the county level, provided by the Federal Government's Public Health Institute of Germany (RKI) for public use. Although the 400 county-related incidence time series shows enormous heterogeneity, both with respect to temporal features as well as spatial distributions, the counties' incidence curves organise into well-distinguished clusters that coincide with East and West Germany. The analysis is based on dimensionality reduction, multidimensional scaling, network analysis, and diversity measures. Dynamical changes are captured by means of difference-in-difference methods, which are related to fold changes of the effective reproduction numbers. The age-related dynamical patterns suggest a considerably stronger impact of children, adolescents and seniors on the epidemic activity than previously expected. Besides these concrete interpretations, the work mainly aims at providing an atlas for spatio-temporal patterns of the epidemic, which serves as a basis to be further explored with the expertise of different disciplines, particularly sociology and policy makers. The study should also be understood as a methodological contribution to getting a handle on the unusual complexity of the COVID-19 pandemic.

Electroencephalography-Derived Functional Connectivity in Sensorimotor Networks in Post Stroke Fatigue

BackgroundPoor suppression of anticipated sensory information from muscle contractions is thought to underlie high fatigue. Such diminished task-related sensory attenuation is reflected in resting state connectivity. Here we test the hypothesis ‘altered electroencephalography (EEG)-derived functional connectivity in somatosensory network in the beta band, is a signature of fatigue in post-stroke fatigue’.MethodsIn non-depressed, minimally impaired stroke survivors (n = 29), with median disease duration of 5 years, resting state neuronal activity was measured using 64-channel EEG. Graph theory-based network analysis measure of functional connectivity via small-world index (SW) was calculated focusing on right and left motor (Brodmann areas 4, 6, 8, 9, 24 and 32) and sensory (Brodmann areas 1, 2, 3, 5, 7, 40 and 43) networks, in the beta (13–30 Hz) frequency range. Fatigue was measured using Fatigue Severity Scale - FSS (Stroke), with scores of > 4, defined as high fatigue.ResultsResults confirmed the working hypothesis, with high fatigue stroke survivors showing higher small-worldness in the somatosensory networks when compared to low fatigue.ConclusionHigh levels of small-worldness in somatosensory networks indicates altered processing of somesthetic input. Such altered processing would explain high effort perception within the sensory attenuation model of fatigue.

Innovation mentor community of practice: a social network analysis perspective

Purpose To exploit collaboration-driven innovation, in recent years, many government-sponsored innovation programs and mentor services have emerged. These services support an effective exchange of knowledge among innovation actors, including innovation mentors and enable mentor connectedness as an important factor to develop and sustain effective innovation mentors’ community of practice (CoP). The purpose of this paper is to examine the degree of connectedness in an innovation mentor CoP. Design/methodology/approach In this study, the innovation mentors CoP as part of a national innovation program is considered a network. The connectedness and assortative mixing of this CoP and the effects of these two on each other were examined by using social network measures, including component analysis, the giant component (GC) and assortativity. Findings The authors provide the analytical interconnectedness results for both the GC and the whole network with network analysis and assortativity measurements of three attributes of mentors (institution, title and degrees). The degree of correlation of community for the GC shows preferential attachment between high-ranking and low-ranking mentors, while preferential attachment was not observed for the whole network. The correlation coefficient for the institution attribute has the highest value for GC, while the title has the highest value for the whole network. Originality/value The study is one of the early attempts to apply social network analysis for an innovation mentor CoP. This study reveals the criticality of evaluating the GC and the whole network separately and provides a number of research and practical directions that will contribute to the development of the innovation mentor CoP.

Bacterial Wars-a tool for the prediction of bacterial predominance based on network analysis measures.

Bacterial Wars (BW) is a network-based tool that applies a two-step pipeline to display information on the competition of bacterial species found in the same microbiome. It utilizes antimicrobial peptide (AMP) sequence similarities to obtain a relationship between species. The working hypothesis (putative AMP defense) is that friendly species share sequence similarity among the putative AMPs of their proteomes and are therefore immune to their AMPs. This may not happen in competing bacterial species with dissimilar putative AMPs. Similarities in the putative AMPs of bacterial proteomes may be thus used to predict predominance. The tool provides insights as to which bacterial species are more likely to 'die' in a competing environmental niche.

A compact triband antenna using artificial magnetic conductor for wireless body area network communicationsFront

The proliferation of the Internet of Things devices and advancements in wireless communication have fostered the growth of Wireless Body Area Networks (WBAN). This research provides a triband antenna supported by a 4 × 4 Artificial Magnetic Conductor (AMC) array surface that has a low Specific Absorption Rate (SAR), a high Front to Back Ratio (FBR), and increased gain for use in wearable devices. For WBAN communications, the proposed antenna operates in the Industrial, Scientific, and Medical (2.4 GHz) band, the C (3.7–4.2 GHz), and the Wi-Fi 6E (5.925–7.125 GHz) bands. The dual-band AMC unit cell exhibits Double-Negative and angular stability behaviour at 2.45 GHz and 6.5 GHz. AMC-backed antenna achieved multiband functionality by incorporating slots into the unit cell and a defective ground structure into the antenna. The antenna was positioned 0.139λ0 above the AMC surface, which measured 0.556λ0 × 0.556λ0 × 0.013λ0 (at 2.45 GHz). The antenna exhibited good gain and return loss variations when mounted on curved surfaces. The proposed integrated design yielded substantial enhancements, as evidenced by the increase of 8.2 dBi in maximum gain, 25.2 dB in FBR, and over 93% in total efficiency. The AMC-backed antenna’s − 10 dB impedance bandwidth is 18.4%, 21.2%, and 22.3%, with corresponding frequency ranges of 2.25–2.66 GHz, 3.66–4.53 GHz, and 5.9–7.35 GHz. Additionally, the AMC surface showed an average reduction in SAR of 93.22%. Vector Network Analyzer and Anechoic chamber measurements proved simulation accuracy. As a result, it is strongly recommended that the integrated antenna design be acknowledged in WBAN communications.

Network analysis of innovation mentor community of practice

PurposePositive effect of knowledge sharing (KS) on innovation has come to the fore and government-supported innovation and mentoring communities or mentor networks have become widespread. This article aims to examine the community connectedness and mentors' preferences for professional competency-based KS of such innovation community of practice networks (CoPNs).Design/methodology/approachThe paper constructs a directed weighted CoPN model with a node-attribute-based novel fingerprint edge weights. Based on the CoPN, Social Network Analysis (SNA) metrics and measures including Giant Component (GC) were proposed and analyzed to identify mentors' connectedness preferences. The fingerprint was proposed as a novel binarized node attribute of competence. Jaccard similarity of fingerprints was proposed as edge weights to reveal correlations between competences and preferences for KS.FindingsThe work opted to conduct a survey of 28 innovation mentors to measure a CoPN. Both a name generator question and a second set of questions were employed to invite respondents to name their collaborators and indicate their professional competence. SNA metrics result in differing values for GC and the rest, which lead us to focus on GC to reveal salient metrics of connectedness. Jaccard similarity analysis results on GC demonstrate that mentors collaborate in an interdisciplinary manner.Originality/valueBased on the CoPN, the methods proposed may be effective in predicting preferred relationships for interdisciplinary collaborations, providing the managers with an analytical decision support tool for KS in practice.

Opinion Leaders for Information Diffusion Using Graph Neural Network in Online Social Networks

Various opportunities are available to depict different domains due to the diverse nature of social networks and researchers' insatiable. An opinion leader is a human entity or cluster of people who can redirect human assessment strategy by intellectual skills in a social network. A more comprehensive range of approaches is developed to detect opinion leaders based on network-specific and heuristic parameters. For many years, deep learning–based models have solved various real-world multifaceted, graph-based problems with high accuracy and efficiency. The Graph Neural Network (GNN) is a deep learning–based model that modernized neural networks’ efficiency by analyzing and extracting latent dependencies and confined embedding via messaging and neighborhood aggregation of data in the network. In this article, we have proposed an exclusive GNN for Opinion Leader Identification (GOLI) model utilizing the power of GNNs to categorize the opinion leaders and their impact on online social networks. In this model, we first measure the n-node neighbor's reputation of the node based on materialized trust. Next, we perform centrality conciliation instead of the input data's conventional node-embedding mechanism. We experiment with the proposed model on six different online social networks consisting of billions of users’ data to validate the model's authenticity. Finally, after training, we found the top-N opinion leaders for each dataset and analyzed how the opinion leaders are influential in information diffusion. The training-testing accuracy and error rate are also measured and compared with the other state-of-art standard Social Network Analysis (SNA) measures. We determined that the GNN-based model produced high performance concerning accuracy and precision.

Scattering Parameter Measurements of the Long Wavelength Array Antenna and Front End Electronics

We present recent 2-port vector network analyzer (VNA) measurements of the complete set of scattering parameters for the antenna used within the Long Wavelength Array (LWA) and the associated front end electronics (FEEs). Full scattering parameter measurements of the antenna yield not only the reflection coefficient for each polarization, S11 and S22, but also the coupling between polarizations, S12 and S21. These had been previously modeled using simulations, but direct measurements had not been obtained until now. The measurements are used to derive a frequency dependent impedance mismatch factor (IMF) which represents the fraction of power that is passed through the antenna–FEE interface and not reflected due to a mismatch between the impedance of the antenna and the impedance of the FEE. We also present results from a two-antenna experiment where each antenna is hooked up to a separate port on the VNA. This allows for cross–antenna coupling to be measured for all four possible polarization combinations. Finally, we apply the newly measured IMF and FEE forward gain corrections to LWA data to investigate how well they remove instrumental effects.

Resilience, indirect effects and cycling in ecological networks

Indirect effects index (IEI) and cycling index (FCI) are two key measures of ecological network analysis that are known to be correlated. Systems with high IEI values are assumed to be more stable and resilient. While this makes sense from a theoretical point of view, there is lack of evidence on the contribution of indirect interactions to ecosystem stability and resilience. Investigation of this relationship between resilience and indirect effects leads to a better understanding of IEI, that it is actually a sum of three distinct components (IEI-cycle, IEI-mixed and IEI-pure), each of which is identified by their relation to cycling. IEI and FCI are highly correlated because both values increase with average path length path, which is the average number of compartments a unit flow material passes through before exiting the system. While IEI and FCI are highly correlated, the third component of IEI, IEI-pure, is totally uncorrelated with cycling.This is because the effects of cycling are excluded from IEI-pure, by definition. Interestingly, we find out that IEI-pure is the major contributor to ecosystem resilience.

The ties that bind: Social network analysis describes the social element of medical workforce recruitment to rural/remote Australia

We present the argument that medical recruitment to a previously under-recruited remote town was effected through what Social Network Analysis (SNA) measures as “brokerage” which operates amidst “structural holes”. We proposed that medical graduates being generated by the national Rural Health School movement in Australia were particularly affected by the combined effect of workforce lacks (structural holes) and strong social commitments (brokerage) – all key SNA concepts. We therefore chose SNA to assess whether the characteristics of RCS-related rural recruitment had feature that SNA might be able to identify, as operantly measured using the industry-standard UCINET's suite of statistical and graphical tools. The result was clear. Graphical output from the UCINET editor showed one individual as being central to all recently recruited doctors to one rural town with recruitment issues like all the others. The statistical outputs from UCINET characterised this person as the single point of most connections. The real-world engagements of this central doctor were in accord with the description of brokerage, a core SNA construct, relationship with reported the reason for these new graduates both coming and staying in town. SNA thus proved fruitful in this first quantification of the role of social networks in drawing new medical recruits to particular rural towns. It allowed description at the level of individual actors with a potent influence on recruitment to rural Australia. We propose these measures could be helpful as key performance indicators for the national Rural Clinical School programme that is generating and distributing a large workforce in Australia, which appears from this work to have a strong social basis. This redistribution of medical workforce from urban to rural is needed internationally.