Comparing alternatives to the fixed degree sequence model for extracting the backbone of bipartite projections

Patterns of link reciprocity in directed, signed networks

Most of the analyses concerning signed networks have focused on balance theory, hence identifying frustration with undirected, triadic motifs having an odd number of negative edges; much less attention has been paid to their directed counterparts. To fill this gap, we focus on signed, directed connections, with the aim of exploring the notion of frustration in such a context. When dealing with signed, directed edges, frustration is a multifaceted concept, admitting different definitions at different scales: if we limit ourselves to consider cycles of length 2, frustration is related to reciprocity, i.e., the tendency of edges to admit the presence of partners pointing in the opposite direction. As the reciprocity of signed networks is still poorly understood, we adopt a principled approach for its study, defining quantities and introducing models to consistently capture empirical patterns of the kind. In order to quantify the tendency of empirical networks to form either mutualistic or antagonistic cycles of length 2, we extend the exponential random graph framework to binary, directed, signed networks with global and local constraints and then compare the empirical abundance of the aforementioned patterns with the one expected under each model. We find that the (directed extension of the) balance theory is not capable of providing a consistent explanation of the patterns characterizing the directed, signed networks considered in this work. Although part of the ambiguities can be solved by adopting a coarser definition of balance, our results call for a different theory, accounting for the directionality of edges in a coherent manner. In any case, the evidence that the empirical, signed networks can be highly reciprocated leads us to recommend to explicitly account for the role played by bidirectional dyads in determining frustration at higher levels (e.g., the triadic one).

How strong is strong? The challenge of interpreting network edge weights

Weighted networks are information-rich and highly-flexible, but they can be difficult to analyze because the interpretation of edges weights is often ambiguous. Specifically, the meaning of a given edge's weight is locally contingent, so that a given weight may be strong for one dyad, but weak for other dyad, even in the same network. I use backbone models to distinguish strong and weak edges in a corpus of 110 weighted networks, and used the results to examine the magnitude of this ambiguity. Although strong edges have larger weights than weak edges on average, a large fraction of edges' weights provide ambiguous information about whether it is strong or weak. Based on these results, I recommend that strong edges should be identified by applying an appropriate backbone model, and that once strong edges have been identified using a backbone model, their original weights should not be directly interpreted or used in subsequent analysis.

Proper network randomization is key to assessing social balance

Social ties, either positive or negative, lead to signed network patterns, the subject of balance theory. For example, strong balance introduces cycles with even numbers of negative edges. The statistical significance of such patterns is routinely assessed by comparisons to null models. Yet, results in signed networks remain controversial. Here, we show that even if a network exhibits strong balance by construction, current null models can fail to identify it. Our results indicate that matching the signed degree preferences of the nodes is a critical step and so is the preservation of network topology in the null model. As a solution, we propose the STP null model, which integrates both constraints within a maximum entropy framework. STP randomization leads to qualitatively different results, with most social networks consistently demonstrating strong balance in three- and four-node patterns. On the basis our results, we present a potential wiring mechanism behind the observed signed patterns and outline further applications of STP randomization.

Detecting Variation in Clinical Practice Patterns for Geriatric Trauma Care Using Social Network Analysis

OBJECTIVE

To characterize hospital-level professional networks of physicians caring for older trauma patients as a function of trauma patient age distribution.

BACKGROUND

The causal factors associated with between-hospital variation in geriatric trauma outcomes are poorly understood. Variation in physician practice patterns reflected by differences in professional networks might contribute to hospital-level differences in outcomes for older trauma patients.

METHODS

This is a population-based, cross-sectional study of injured older adults (age 65 or above) and their physicians from January 1, 2014, to December 31, 2015, using Health Care Cost and Utilization Project inpatient data and Medicare claims from 158 hospitals in Florida. We used social network analyses to characterize the hospitals in terms of network density, cohesion, small-worldness, and heterogeneity, then used bivariate statistics to assess the relationship between network characteristics and hospital-level proportion of trauma patients who were aged 65 or above.

RESULTS

We identified 107,713 older trauma patients and 169,282 patient-physician dyads. The hospital-level proportion of trauma patients who were aged 65 or above ranged from 21.5% to 89.1%. Network density, cohesion, and small-worldness in physician networks were positively correlated with hospital geriatric trauma proportions ( R =0.29, P <0.001; R =0.16, P =0.048; and R =0.19, P <0.001, respectively). Network heterogeneity was negatively correlated with geriatric trauma proportion ( R =0.40, P <0.001).

CONCLUSIONS

Characteristics of professional networks among physicians caring for injured older adults are associated with the hospital-level proportion of trauma patients who are older, indicating differences in practice patterns at hospitals with older trauma populations. Associations between interspecialty collaboration and patient outcomes should be explored as an opportunity to improve the treatment of injured older adults.

An evaluation tool for backbone extraction techniques in weighted complex networks

Networks are essential for analyzing complex systems. However, their growing size necessitates backbone extraction techniques aimed at reducing their size while retaining critical features. In practice, selecting, implementing, and evaluating the most suitable backbone extraction method may be challenging. This paper introduces netbone, a Python package designed for assessing the performance of backbone extraction techniques in weighted networks. Its comparison framework is the standout feature of netbone. Indeed, the tool incorporates state-of-the-art backbone extraction techniques. Furthermore, it provides a comprehensive suite of evaluation metrics allowing users to evaluate different backbones techniques. We illustrate the flexibility and effectiveness of netbone through the US air transportation network analysis. We compare the performance of different backbone extraction techniques using the evaluation metrics. We also show how users can integrate a new backbone extraction method into the comparison framework. netbone is publicly available as an open-source tool, ensuring its accessibility to researchers and practitioners. Promoting standardized evaluation practices contributes to the advancement of backbone extraction techniques and fosters reproducibility and comparability in research efforts. We anticipate that netbone will serve as a valuable resource for researchers and practitioners enabling them to make informed decisions when selecting backbone extraction techniques to gain insights into the structural and functional properties of complex systems.

Urban economic fitness and complexity from patent data

Over the years, the growing availability of extensive datasets about registered patents allowed researchers to get a deeper insight into the drivers of technological innovation. In this work, we investigate how patents' technological contents characterise metropolitan areas' development and how innovation is related to GDP per capita. Exploiting worldwide data from 1980 to 2014, and through network-based techniques that only use information about patents, we identify coherent distinguished groups of metropolitan areas, either clustered in the same geographical area or similar in terms of their economic features. Moreover, we extend the notion of coherent diversification to patent production and show how it is linked to the economic growth of metropolitan areas. Our findings draw a picture in which technological innovation can play a key role in the economic development of urban areas. We contend that the tools introduced in this paper can be used to further explore the interplay between urban growth and technological innovation.

Where do knowledge-intensive firms locate in Germany?-An explanatory framework using exponential random graph modeling

This paper analyzes how positional and relational data in 186 regions of Germany influence the location choices of knowledge-based firms. Where firms locate depends on specific local and interconnected resources, which are unevenly distributed in space. This paper presents an innovative way to study such firm location decisions through network analysis that relates exponential random graph modeling (ERGM) to the interlocking network model (INM). By combining attribute and relational data into a comprehensive dataset, we capture both the spatial point characteristics and the relationships between locations. Our approach departs from the general description of individual location decisions in cities and puts extensive networks of knowledge-intensive firms at the center of inquiry. This method can therefore be used to investigate the individual importance of accessibility and supra-local connectivity in firm networks. We use attributional data for transport (rail, air), universities, and population, each on a functional regional level; we use relational data for travel time (rail, road, air) and frequency of relations (rail, air) between two regions. The 186 functional regions are assigned to a three-level grade of urbanization, while knowledge-intensive economic activities are grouped into four knowledge bases. This research is vital to understand further the network structure under which firms choose locations. The results indicate that spatial features, such as the population of or universities in a region, seem to be favorable but also reveal distinct differences, i.e., the proximity to transport infrastructure and different valuations for accessibility for each knowledge base.

Bow-tie structures of twitter discursive communities

Bow-tie structures were introduced to describe the World Wide Web (WWW): in the direct network in which the nodes are the websites and the edges are the hyperlinks connecting them, the greatest number of nodes takes part to a bow-tie, i.e. a Weakly Connected Component (WCC) composed of 3 main sectors: IN, OUT and SCC. SCC is the main Strongly Connected Component of WCC, i.e. the greatest subgraph in which each node is reachable by any other one. The IN and OUT sectors are the set of nodes not included in SCC that, respectively, can access and are accessible to nodes in SCC. In the WWW, the greatest part of the websites can be found in the SCC, while the search engines belong to IN and the authorities, as Wikipedia, are in OUT. In the analysis of Twitter debate, the recent literature focused on discursive communities, i.e. clusters of accounts interacting among themselves via retweets. In the present work, we studied discursive communities in 8 different thematic Twitter datasets in various languages. Surprisingly, we observed that almost all discursive communities therein display a bow-tie structure during political or societal debates. Instead, they are absent when the argument of the discussion is different as sport events, as in the case of Euro2020 Turkish and Italian datasets. We furthermore analysed the quality of the content created in the various sectors of the different discursive communities, using the domain annotation from the fact-checking website Newsguard: we observe that, when the discursive community is affected by m/disinformation, the content with the lowest quality is the one produced and shared in SCC and, in particular, a strong incidence of low- or non-reputable messages is present in the flow of retweets between the SCC and the OUT sectors. In this sense, in discursive communities affected by m/disinformation, the greatest part of the accounts has access to a great variety of contents, but whose quality is, in general, quite low; such a situation perfectly describes the phenomenon of infodemic, i.e. the access to "an excessive amount of information about a problem, which makes it difficult to identify a solution", according to WHO.

backbone: An R package to extract network backbones

Networks are useful for representing phenomena in a broad range of domains. Although their ability to represent complexity can be a virtue, it is sometimes useful to focus on a simplified network that contains only the most important edges: the backbone. This paper introduces and demonstrates a substantially expanded version of the backbone package for R, which now provides methods for extracting backbones from weighted networks, weighted bipartite projections, and unweighted networks. For each type of network, fully replicable code is presented first for small toy examples, then for complete empirical examples using transportation, political, and social networks. The paper also demonstrates the implications of several issues of statistical inference that arise in backbone extraction. It concludes by briefly reviewing existing applications of backbone extraction using the backbone package, and future directions for research on network backbone extraction.