Researchers collecting survey data on inter-organizational networks typically choose a single informant with the most senior job title in each organization from whom to obtain a report of the organization’s inter-organizational ties. This approach to informant selection is based on the logic that greater seniority confers greater knowledge of inter-organizational relationships. The present study investigated the wisdom of this logic, using data in which multiple informants’ reports of inter-organizational network ties were collected for each organization. We calculated the degree of agreement in network reports between the informant with the most senior job title and a second informant in the organization. To determine if alternative criteria to seniority serve as better approaches to informant selection, we assessed other potential predictors of agreement in informants’ reports. Results indicated that (1) informants’ perceptions of the network differed significantly according to job title, suggesting little agreement between senior informants and their more junior colleagues; and (2) greater informant tenure in the network and industry were associated with greater agreement among informants. These results call a common data collection practice into question and suggest that tenure may trump title as a criterion for informant selection in inter-organizational network research.

Previous research stresses the importance of social networks for obesity. We draw on friendship data from 18,133 adolescents in four European countries to investigate the relationship between individuals’ body mass index (BMI) and the BMI of their friends. Our study reveals strong evidence for BMI clustering in England, Germany, the Netherlands, and Sweden; adolescents tend to be friends with others who have a similar BMI. Furthermore, we extend current debate and explore friendship characteristics that moderate the relationship between social networks and BMI. We demonstrate that BMI clustering is more pronounced in (1) strong compared to weak friendships and (2) between adolescents of the same biological sex. These findings indicate thatmore research on social networks and health is needed which distinguishes between different kinds of relationships.

One of the most challenging problems in biomedicine and genomics is the identification of disease biomarkers. In this study, proteomics data from seven major cancers were used to construct two weighted protein–protein interaction networks, i.e., one for the normal and another for the cancer conditions. We developed rigorous, yet mathematically simple, methodology based on the degeneracy at –1 eigenvalues to identify structural symmetry or motif structures in network. Utilizing eigenvectors corresponding to degenerate eigenvalues in the weighted adjacency matrix, we identified structural symmetry in underlying weighted protein–protein interaction networks constructed using seven cancer data. Functional assessment of proteins forming these structural symmetry exhibited the property of cancer hallmarks. Survival analysis refined further this protein list proposing BMI, MAPK11, DDIT4, CDKN2A, and FYN as putative multicancer biomarkers. The combined framework of networks and spectral graph theory developed here can be applied to identify symmetrical patterns in other disease networks to predict proteins as potential disease biomarkers.

We investigated human understanding of different network visualizations in a large-scale online experiment. Three types of network visualizations were examined: node-link and two different sorting variants of matrix representations on a representative social network of either 20 or 50 nodes. Understanding of the network was quantified using task time and accuracy metrics on questions that were derived from an established task taxonomy. The sample size in our experiment was more than an order of magnitude larger (N = 600) than in previous research, leading to high statistical power and thus more precise estimation of detailed effects. Specifically, high statistical power allowed us to consider modern interaction capabilities as part of the evaluated visualizations, and to evaluate overall learning rates as well as ambient (implicit) learning. Findings indicate that participant understanding was best for the node-link visualization, with higher accuracy and faster task times than the two matrix visualizations. Analysis of participant learning indicated a large initial difference in task time between the node-link and matrix visualizations, with matrix performance steadily approaching that of the node-link visualization over the course of the experiment. This research is reproducible as the web-based module and results have been made available at: https://osf.io/qct84/.

We introduce a weighted configuration model graph, where edge weights correspond to the probability of infection in an epidemic on the graph. On these graphs, we study the development of a Susceptible–Infectious–Recovered epidemic using both Reed–Frost and Markovian settings. For the special case of having two different edge types, we determine the basic reproduction numberR0, the probability of a major outbreak, and the relative final size of a major outbreak. Results are compared with those for a calibrated unweighted graph. The degree distributions are based on both theoretical constructs and empirical network data. In addition, bivariate standard normal copulas are used to model the dependence between the degrees of the two edge types, allowing for modeling the correlation between edge types over a wide range. Among the results are that the weighted graph produces much richer results than the unweighted graph. Also, while R0 always increases with increasing correlation between the two degrees, this is not necessarily true for the probability of a major outbreak nor for the relative final size of a major outbreak. When using copulas we see that these can produce results that are similar to those of the empirical degree distributions, indicating that in some cases a copula is a viable alternative to using the full empirical data.

Vipps is a peer-to-peer mobile payment solution launched by Norway’s largest financial services group DNB. The Vipps transaction data may be viewed as a graph with users corresponding to the nodes, and the financial transactions between the users defining the edges. We have followed the evolution of this graph from May 2015 to September 2016. This is a unique data set, as information about transactions of individuals is usually not available for research. In this paper, we use an advanced statistical model where preferential attachment is combined with fitness. We show that the intrinsic quality of the nodes in the Vipps network plays an important part in the evolution of the network. This insight may, e.g., be used to identify influential nodes for viral marketing.

Network representations of systems from various scientific and societal domains are neither completely random nor fully regular, but instead appear to contain recurring structural features. These features tend to be shared by networks belonging to the same broad class, such as the class of social networks or the class of biological networks. Within each such class, networks describing similar systems tend to have similar features. This occurs presumably because networks representing similar systems would be expected to be generated by a shared set of domain-specific mechanisms, and it should therefore be possible to classify networks based on their features at various structural levels. Here we describe and demonstrate a new hybrid approach that combines manual selection of network features of potential interest with existing automated classification methods. In particular, selecting well-known network features that have been studied extensively in social network analysis and network science literature, and then classifying networks on the basis of these features using methods such as random forest, which is known to handle the type of feature collinearity that arises in this setting, we find that our approach is able to achieve both higher accuracy and greater interpretability in shorter computation time than other methods.