The performance of permutations and exponential random graph models when analyzing animal networks

Social network analysis has typically concerned analysis of one type of tie connecting nodes of the same type. It has however been recognised that people are connected through multiple types of ties and that people in addition are affiliated with multiple types of non-people nodes. Exponential random graph models (ERGM) is a family of statistical models for social networks that at this point allows for a number of different types of network data, including one-mode networks, bipartite networks, multiplex data, as well as multilevel network data. Multilevel networks have been proposed as a joint representation of associations between multiple types of entities or nodes, such as people and organization, where two types of nodes gives rise to three distinct types of ties. The typical roster data collection method may be impractical or infeasible when the node sets are hard to detect or define or because of the cognitive demands on respondents. Multilevel multilayered networks allow us to consider a multitude of different sources of data and to sample on different types of nodes and relations. We consider modelling multilevel multilayered networks using exponential random graph models and extend a recently developed Bayesian data-augmentation scheme to allow for partially missing data. We illustrate the proposed inference procedures for the case of multilevel snowball sampling and sampling with error based on the Noordin Top network.

Advances in exponential random graph ( p*) models applied to a large social network

BackgroundNext-generation sequencing (NGS) has become an indispensable diagnostic tool across various diseases. However, sequencing and analysis errors remain major barriers to clinical implementation. In cancer diagnostics, detecting low-level somatic variants is particularly challenging due to tumor heterogeneity and contamination from normal cells.ResultsWe assess targeted next-generation sequencing (T-NGS) performance using reference-standard DNA mixtures of homozygote hydatidiform mole and heterozygote blood DNA at varying ratios, analyzed by certified NGS providers. Analytical sensitivity differs by up to 13.9-fold, and false positive (FP) error rates vary up to 615-fold, depending on provider and pipeline. For identical raw data, DRAGEN and the in-house pipeline differ by up to 36.3-fold in FP error rates. Moderately recurrent FP-prone alleles, although representing only 5.37% of all FP sites, contribute to 36.7% of total FP errors in the Geninus in-house result. Among 22 discordant variant calls between DRAGEN and in-house analyses, more than half of them are not confirmed by single base extension assays, indicating likely false positives. Compared to DRAGEN, a conventional BWA + GATK Mutect2 pipeline maintains equivalent sensitivity but produces a 4-fold increase in FP errors, along with a notable enrichment of recurrent FP-prone alleles.ConclusionsT-NGS results from certified providers exhibit substantial variability in both sensitivity and FP error rates. Conventional pipelines not only increase FP errors but also accumulate recurrent FP-prone alleles. These findings underscore the urgent need for standardized pipelines and rigorous quality control measures to ensure the reliability of T-NGS in clinical diagnostics.Supplementary InformationThe online version contains supplementary material available at 10.1186/s13059-025-03882-2.

To evaluate the false-negative and false-positive error rates both in a screening and a non-screening population. A total of 4192 prostatic biopsies were reported in a 6-year period by 15 consultant histopathologists, two of whom had an interest in uropathology and were deemed to be specialists (J.O. and C.S.). All biopsies were reviewed prior to the multidisciplinary team (MDT) meeting. The overall false-negative rate was 1.7% (screening 2.1%, non-screening 1.5%). The overall false-positive rate was 0.5% (screening 0.9%, non-screening 0.4%). These error rates varied among pathologists, with the false-negative rate ranging from 0% to 9.3%, and the false-positive rate ranging from 0% to 3.8%. The false-negative rate was three times greater than the false-positive rate, showing that detection of significant pathology is far greater in the negative biopsies. More errors occurred in the screening population than in the non-screening population. The consultants making the most errors were non-specialists, but the specialists also made false-negative errors, suggesting that just using specialist reporting alone would not have eradicated errors.

Site occupancy models have been developed that allow for imperfect species detection or "false negative" observations. Such models have become widely adopted in surveys of many taxa. The most fundamental assumption underlying these models is that "false positive" errors are not possible. That is, one cannot detect a species where it does not occur. However, such errors are possible in many sampling situations for a number of reasons, and even low false positive error rates can induce extreme bias in estimates of site occupancy when they are not accounted for. In this paper, we develop a model for site occupancy that allows for both false negative and false positive error rates. This model can be represented as a two-component finite mixture model and can be easily fitted using freely available software. We provide an analysis of avian survey data using the proposed model and present results of a brief simulation study evaluating the performance of the maximum-likelihood estimator and the naive estimator in the presence of false positive errors.

Network data often contain sensitive relational information. One approach to protecting sensitive information while offering flexibility for network analysis is to share synthesized networks based on the information in originally observed networks. We employ differential privacy (DP) and exponential random graph models (ERGMs) and propose the DP-ERGM method to synthesize network data. We apply DP-ERGM to two real-world networks. We then compare the utility of synthesized networks generated by DP-ERGM, the DyadWise Randomized Response (DWRR) approach, and the Synthesis through Conditional distribution of Edge given nodal Attribute (SCEA) approach. In general, the results suggest that DP-ERGM preserves the original information significantly better than two other approaches in network structural statistics and inference for ERGMs and latent space models. Furthermore, DP-ERGM satisfies node DP through modeling the global network structure with ERGM, a stronger notion of privacy than the edge DP under which DWRR and SCEA operate.

Since the 1970s, communication researchers have utilized social network analysis to understand mass, health, organizational, and interpersonal communication. This article introduces communication researchers to a new class of social network analysis methods, exponential random graph (p*) models. This new method represents the latest advancement in social network methodology and will enhance the trajectory of social network research in the communication discipline. The benefits of this class of models include allowing for the simultaneous estimation of attribute and structural parameters, accounting for the interdependent nature of network data, and retaining the complexity of network observations throughout the analysis. An example analysis using data from Shumate, Fulk, and Monge (2005) is provided to illustrate the potentials of exponential random graph modeling. Five different social network software programs capable of the analysis discussed in this article are introduced with regard to their respective benefits. Finally, a brief tutorial based on data from Palazzolo (2005) is given on how to conduct an ERGM analysis using the PNET software program.

Cannabis sativa L. is widely used as recreational illegal drugs. Illicit Cannabis profiling, comparing seized samples, is challenging due to natural Cannabis heterogeneity. The aim of this study was to use GC–FID and GC–MS herbal fingerprints for intra (within)- and inter (between)-location variability evaluation. This study focused on finding an acceptable threshold to link seized samples. Through Pearson correlation-coefficient calculations between intra-location samples, ‘linked’ thresholds were derived using 95% and 99% confidence limits. False negative (FN) and false positive (FP) error rate calculations, aiming at obtaining the lowest possible FP value, were performed for different data pre-treatments. Fingerprint-alignment parameters were optimized using Automated Correlation-Optimized Warping (ACOW) or Design of Experiments (DoE), which presented similar results. Hence, ACOW data, as reference, showed 54% and 65% FP values (95 and 99% confidence, respectively). An additional fourth root normalization pre-treatment provided the best results for both the GC–FID and GC–MS datasets. For GC–FID, which showed the best improved FP error rate, 54 and 65% FP for the reference data decreased to 24 and 32%, respectively, after fourth root transformation. Cross-validation showed FP values similar as the entire calibration set, indicating the representativeness of the thresholds. A noteworthy improvement in discrimination between seized Cannabis samples could be concluded.

When detecting positive selection in proteins, the prevalence of errors resulting from misalignment and the ability of alignment filters to mitigate such errors are not well understood, but filters are commonly applied to try to avoid false positive results. Focusing on the sitewise detection of positive selection across a wide range of divergence levels and indel rates, we performed simulation experiments to quantify the false positives and false negatives introduced by alignment error and the ability of alignment filters to improve performance. We found that some aligners led to many false positives, whereas others resulted in very few. False negatives were a problem for all aligners, increasing with sequence divergence. Of the aligners tested, PRANK's codon-based alignments consistently performed the best and ClustalW performed the worst. Of the filters tested, GUIDANCE performed the best and Gblocks performed the worst. Although some filters showed good ability to reduce the error rates from ClustalW and MAFFT alignments, none were found to substantially improve the performance of PRANK alignments under most conditions. Our results revealed distinct trends in error rates and power levels for aligners and filters within a biologically plausible parameter space. With the best aligner, a low false positive rate was maintained even with extremely divergent indel-prone sequences. Controls using the true alignment and an optimal filtering method suggested that performance improvements could be gained by improving aligners or filters to reduce the prevalence of false negatives, especially at higher divergence levels and indel rates.

Recent advances in microarray technology have opened new ways for functional annotation of previously uncharacterised genes on a genomic scale. This has been demonstrated by unsupervised clustering of co-expressed genes and, more importantly, by supervised learning algorithms. Using prior knowledge, these algorithms can assign functional annotations based on more complex expression signatures found in existing functional classes. Previously, support vector machines (SVMs) and other machine-learning methods have been applied to a limited number of functional classes for this purpose. Here we present, for the first time, the comprehensive application of supervised neural networks (SNNs) for functional annotation. Our study is novel in that we report systematic results for ~100 classes in the Munich Information Center for Protein Sequences (MIPS) functional catalog. We found that only ~10% of these are learnable (based on the rate of false negatives). A closer analysis reveals that false positives (and negatives) in a machine-learning context are not necessarily "false" in a biological sense. We show that the high degree of interconnections among functional classes confounds the signatures that ought to be learned for a unique class. We term this the "Borges effect" and introduce two new numerical indices for its quantification. Our analysis indicates that classification systems with a lower Borges effect are better suitable for machine learning. Furthermore, we introduce a learning procedure for combining false positives with the original class. We show that in a few iterations this process converges to a gene set that is learnable with considerably low rates of false positives and negatives and contains genes that are biologically related to the original class, allowing for a coarse reconstruction of the interactions between associated biological pathways. We exemplify this methodology using the well-studied tricarboxylic acid cycle.

Motivation: Certain studies use short-TE MRS to estimate metabolites such as gamma-aminobutyric acid. The findings of such studies hinge on the correlation of this technique to the gold-standard edited MRS technique. Goal(s): We investigate how the false negative rate and false positive rate of studies vary as a function of correlation between short-TE and MEGA-edited metabolite estimates. Approach: Simulated data was used to numerically estimate the false positive and false negative rates, using parameters obtained from the literature. Results: We show that these studies can have largely inflated false positive and false negative rates as compared to their nominally designed values. Impact: The false negative and false positive rate can be markedly inflated when using short-TE MRS to estimate metabolites such as gamma-aminobutyric acid. This calls for increased caution in interpreting results, and the accommodation of these effects when designing statistical analyses.

Exponential random graph models (ERGMs) are increasingly applied to observed network data and are central to understanding social structure and network processes. The chapters in this edited volume provide a self-contained, exhaustive account of the theoretical and methodological underpinnings of ERGMs, including models for univariate, multivariate, bipartite, longitudinal and social-influence type ERGMs. Each method is applied in individual case studies illustrating how social science theories may be examined empirically using ERGMs. The authors supply the reader with sufficient detail to specify ERGMs, fit them to data with any of the available software packages and interpret the results.

The uncertainty underlying real-world phenomena has attracted attention toward statistical analysis approaches. In this regard, many problems can be modeled as networks. Thus, the statistical analysis of networked problems has received special attention from many researchers in recent years. Exponential Random Graph Models, known as ERGMs, are one of the popular statistical methods for analyzing the graphs of networked data. ERGM is a generative statistical network model whose ultimate goal is to present a subset of networks with particular characteristics as a statistical distribution. In the context of ERGMs, these graph’s characteristics are called statistics or configurations. Most of the time they are the number of repeated subgraphs across the graphs. Some examples include the number of triangles or the number of cycle of an arbitrary length. Also, any other census of the graph, as with the edge density, can be considered as one of the graph’s statistics. In this review paper, after explaining the building blocks and classic methods of ERGMs, we have reviewed their newly presented approaches and research papers. Further, we have conducted a comprehensive study on the applications of ERGMs in many research areas which to the best of our knowledge has not been done before. This review paper can be used as an introduction for scientists from various disciplines whose aim is to use ERGMs in some networked data in their field of expertise.

Synthetic power grids enable real-world energy system simulations and are crucial for algorithm testing, resilience assessment, and policy formulation. We propose a novel method for the generation of synthetic transmission power grids using exponential random graph (ERG) models. Our two main contributions are (1) the formulation of an ERG model tailored specifically for capturing the topological nuances of power grids and (2) a general procedure for estimating the parameters of such a model conditioned on working with connected graphs. From a modeling perspective, we identify edge counts per bus type and k-triangles as crucial topological characteristics for synthetic power-grid generation. From a technical perspective, we develop a rigorous methodology to estimate the parameters of an ERG constrained to the space of connected graphs. The proposed model is flexible, easy to implement, and successfully captures the desired topological properties of power grids. Published by the American Physical Society 2024

Earlier research using a very limited data base gave encouraging results for the automated screening of exfoliated cytologic samples using coherent optical processing techniques to examine individual isolated cells. A more thorough investigation involving a larger data base has confirmed our initial results. This investigation was performed using a specially designed Fourier spectrum analyzer and a solid state optical detector array. An analysis was made to determine the performance of a screening system using such a cell-by-cell discriminating device. This analysis indicated that less than 20,000 cells would have to be examined to obtain a system performance level of 1% false negative and 10% false positive error rates with a 1% probability of occurrence of malignant cells in a malignant sample. This performance figure was inferred from measured statistical performance characteristics of a laboratory cell-by-cell screening device using optically generated Fourier transfrom techniques for cell discrimination. The performance of the system was shown to be much more sensitive to cell-by-cell false error rates than false negative error rates. It was also found that the majority of false positive errors were due to misclassifying parabasal cells as malignant. By eliminating parabasal cells, which comprised more than 25% of our normal cell data base, the number of cells needed to be screened dropped by an order of magnitude. It was also shown that there is an inverse quadratic relationship between the percentage of malignant cells in a malignant sample and the number of cells that must be screened to achieve any desired system performance.