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Social network research has been confined to small groups because large networks are intractable, and no systematic theory of network sampling exists. This paper describes a practical method for sampling average acquaintance volume (the average number of people known by each person) from large populations and derives confidence limits on the resulting estimates. It is shown that this average figure also yields an estimate of what has been called "network density." Applications of the procedure to community studies, hierarchical structures, and interorganizational networks are proposed. Problems in developing a general theory of network sampling are discussed.

This paper describes alternative methods for careful sampling of special populations. It discusses the use of single or multiple lists, even when incomplete, along with supplementary measures using telephone and mail techniques. If the special populations are clustered there are several alternatives for reducing costs substantially. These involve telephone or mail screenings to eliminate zero segments with a single contact, use of two or three contacts to eliminate zero segments if face-to-face screening is required, use of lists to identify nonzero segments, use of combined screening, and reducing the sampling rate in nonzero areas that have low densities. For some populations the use of network sampling can be very effective. Some populations can be defined by their activities and sampled at locations where they participate. These careful methods should be used if one is trying to generalize to a total special population.

Network sampling is a potentially invaluable method of studying density of large networks, but its feasibility in practice is largely unknown. Two pretests of a network sampling instrument in a favourable setting (a network with moderate size, high density, and cooperative respondents) with a relatively representative population are reported in this paper. The results indicate that network sampling is indeed viable in such settings. Some suggestions for successful applications are offered.

Resume Nous appliquons la spectrometrie de masse des ions secondaires (SIMS) a l'etude des aerosols emis dans l'atmosphere par les sources industrielles reliees au phenomene des precipitations acides. Un reseau d'echantillonnage visant a recueillir des aerosols a ete etablie dans la region de Rouyn-Noranda afin d'etudier les retombees locales provenant de la plus importante source de SO 2 de la province de Quebec. Des instruments d'echantillonnage ont egalement ete construits. D'une part, les aerosols sont analyses individuellement via un SIMS equipe d'une microsonde au Ga + de 1 μm de diametre et avec un analyseur de rayons x par dispersion d'energie (EDAX) couple a un microscope electronique a balayage (MEB). Des supports speciaux permettent la localisation des aerosols dans la chambre d'analyse du SIMS apres leur analyse a l'EDAX. Une analyse avec un LAMMA (Laser Microprobe Mass Analysis) est aussi possible. D'autre part, ils sont analyses en volume par l'intermediaire d'un SIMS equipe d'une source de 200 μm de diametre a l'Ar + . Une analyse multivariee en composantes principales a ete effectuee sur des spectres SIMS-Ar + . Des liens entre les conditions d'echantillonnage et les spectres sont donnes, de meme qu'une association entre des groupes de composes chimiques et les facteurs mis en evidence par l'analyse multivariee.

We investigate several unbiased estimators of the size of a graph G which are based on sampled induced subgraphs, stars and dyads. By comparing their variances we find some general dominance relations between them, and we also give some conditions on G which guarantee some other instances of dominance.

In principle, network sampling is an attractive method for estimating density and degree for large populations. This paper, based on the first extensive application of network sampling, reports some of the problems that may often arise in sampling and reaching respondents. The network sample or samples may be based on a list imperfectly reflecting the target population, the samples may be drawn nonrandomly from the list, and response may be nonrandom. We suggest and illustrate strategies for coping with each of these problems.

Here we look at the past accomplishments, present difficulties, and future potentials of network research, stressing the unique advantages of investigating the new interactive technologies. A positive assessment of the past scientific accomplishments of network research is tempered by serious epistemological problems: inadequate attention to network theory, network sampling problems that restrict the generalizability of results, an underemphasis upon data-gathering and measurement, and an analytical shortchanging of the message content that flows through links over time. Partial solutions to certain of these difficulties may be found in network research on the new interactive communication systems.

One of the recurrent problems in the study of social organization is that survey researchers are forced to study individuals, while they are often interested in hypotheses about organizations or systems of organizations. This paper offers some methods which allow survey researchers to address hypotheses about the relations generated among individuals by organizations, the organizations themselves, and the relations among organizations. Estimators are developed for the number and size distribution of organizations in communities, the density of relations among individuals generated by organizations, the number and density of interorganizational linkages, and the amount of membership overlap among organizations. This approach combines Granovetter's idea of network sampling with Breiger's notion of the duality of persons and groups to produce a quantitative approach to the study of voluntary organization. We provide an illustration of the methods we develop by testing two of Blau's structural hypotheses on data concerning voluntary organizations. The hypotheses that structural differentiation increases with system size and that this increase is at a decreasing rate are strongly supported. The paper closes with some suggestions for further elaborations of the hypernetwork approach.

A network sampling theory developed previously is used to obtain limiting probabilities of geostrophic wind components predicted at 500 mb in several synoptic cases. Analogous probabilities are calculated for the kinetic energies of these components. Data are from an earlier study described in this Journal. An example of wind-probability forecasts is presented. Theoretical and empirical probabilities are found to agree satisfactorily. These results and others are discussed.

Purpose: to understand the social representations of health professionals about terminally ill children and adolescents in different work settings. Methods: a qualitative, descriptive, exploratory study was conducted with ten health professionals, selected through the technique of network sampling. The instruments for data collection were semi-structured interviews, and free association of words. The analysis of the data followed the steps of content analysis, subsidized by the theory of social representations, with the support of Atlas.ti software. Results: the analysis of the interviews presented 115 excerpts of statements, condensed into 11 codes, which were grouped into three categories: experiences, strategies, and consequences of conviviality with terminality; mission and amorousness in a terminal condition; terminality as the end of life. The free association of words resulted in 52 evocations, with an emphasis on suffering, pain, love, mission, and family. Conclusion: the social representations of health professionals about terminally ill children and adolescents are associated with the situations being experienced, the strategies developed to deal with these moments in the exercise of the profession, and the consequences that this experience causes in the health professionals.

The sampling of rare and elusive populations is difficult because the costs of locating such populations are substantial and can exceed actual interviewing costs. There are efficient probability methods that have been developed recently that reduce these costs. If the special populations are geographically clustered, efficient sampling involves the rapid location of segments in which no members of the special population are located with the use of Census data, telephone screening, or incomplete lists. Populations that are not geographicaily clustered can be located by network sampling and use of large previously gathered samples. Characteristics of mobile populations such as the homeless can be estimated by capture-recapture methods.

Telecommunications companies must process large-scale social networks that reveal the communication patterns among their customers. These networks are dynamic in nature as new customers appear, old customers leave, and the interaction among customers changes over time. One way to uncover the evolution patterns of such entities is by monitoring the evolution of the communities they belong to. Large-scale networks typically comprise thousands, or hundreds of thousands, of communities and not all of them are worth monitoring, or interesting from the business perspective. Several methods have been proposed for tracking the evolution of groups of entities in dynamic networks but these methods lack strategies to effectively extract knowledge and insight from the analysis. In this paper we tackle this problem by proposing an integrated business-oriented framework to track and interpret the evolution of communities in very large networks. The framework encompasses several steps such as network sampling, community detection, community selection, monitoring of dynamic communities and rule-based interpretation of community evolutionary profiles. The usefulness of the proposed framework is illustrated using a real-world large-scale social network from a major telecommunications company.

Interactions within a population shape the spread of infectious diseases but contact patterns between individuals are difficult to access. We hypothesised that key properties of these patterns can be inferred from multiple infection data in longitudinal follow-ups. We developed a simulator for epidemics with multiple infections on networks and analysed the resulting individual infection time series by introducing similarity metrics between hosts based on their multiple infection histories. We find that, depending on infection multiplicity and network sampling, multiple infection summary statistics can recover network properties such as degree distribution. Furthermore, we show that by mining simulation outputs for multiple infection patterns, one can detect immunological interference between pathogens (i.e. the fact that past infections in a host condition future probability of infection). The combination of individual-based simulations and analysis of multiple infection histories opens promising perspectives to infer and validate transmission networks and immunological interference for infectious diseases from longitudinal cohort data.

The influence of the number of the water samples on classifying surface water into water quality classes with regard to nitrate was determined. The characteristic values c 90 (percentile 90%) of the nitrate nitrogen concentrations were computed according to following three ways: – from the monthly time series of nitrate nitrogen concentrations monitored in Ondava River by Slovak Hydrometeorogical Institute (SHMI) at basic network sampling site under Stropkov (period 1987–1991); – from the daily time series of nitrate nitrogen concentrations monitored by Institute of Hydrology of Slovak Academy of Science (IH SAS) at Ondava: Stropkov sampling site; – from the theoretical log-normal distribution estimated from the daily time series.

Interactions within a population shape the spread of infectious diseases but contact patterns between individuals are difficult to access. We hypothesised that key properties of these patterns can be inferred from multiple infection data in longitudinal follow-ups. We developed a simulator for epidemics with multiple infections on networks and analysed the resulting individual infection time series by introducing similarity metrics between hosts based on their multiple infection histories. We find that, depending on infection multiplicity and network sampling, multiple infection summary statistics can recover network properties such as degree distribution. Furthermore, we show that by mining simulation outputs for multiple infection patterns, one can detect immunological interference between pathogens (i.e. the fact that past infections in a host condition future probability of infection). The combination of individual-based simulations and analysis of multiple infection histories opens promising perspectives to infer and validate transmission networks and immunological interference for infectious diseases from longitudinal cohort data., Author summary Infectious disease dynamics are constrained both by between-host contacts and pathogen interactions within a host. The circulation of multiple parasites within a population constitutes a unique signature for each host’s infection lifespan. We hypothesise that such individual multiple infection histories can inform us about the host contact networks on which parasites can be transmitted, but also on within-host interactions, where prior infections shape susceptibility to new infections. For proof-of-concept, we develop a simulator for multiple infections on networks. By combining intuitive novel metrics for multiple infection events and established tools from computational data analysis, we show that similarity in infection history between two hosts correlates with their proximity in the contact network. By analysing pathogens co-occurrence patterns within hosts, we also recover between-pathogen interference at the population level. The demonstrated robustness of our results in terms of observability, network clustering, and pathogen diversity opens new perspectives to extract host contact and between-pathogen interference information from longitudinal infection data.