Search

Compartmental models of disease spread have been well studied on networks built according to the Configuration Model, i.e., where the degree distribution of individual nodes is specified, but where connections are made randomly. Dynamics of spread on such "first order" networks were shown to be profoundly different compared to epidemics under the traditional mass action assumption. Assortativity, i.e., the preferential mixing of nodes according to degree, is a second order property that is thought to impact epidemic trajectory. We first show how assortative mixing can come about from individual preferences to connect with others of lower or higher degree, and propose an algorithm for constructing such a network. We then investigate via simulation how this network structure favors or inhibits diffusion processes, such as the spread of an infectious disease. [ABSTRACT FROM AUTHOR]

Abstract: This work aims to describe the spatial distribution of flow from characteristics of the underlying pore structure in heterogeneous porous media. Thousands of two-dimensional samples of polydispersed granular media are used to (1) obtain the velocity field via direct numerical simulations, and (2) conceptualize the pore network as a graph in each sample. Analysis of the flow field allows us to distinguish preferential from stagnant flow regions and to quantify how channelized the flow is. Then, the graph’s edges are weighted by geometric attributes of their corresponding pores to find the path of minimum resistance of each sample. Overlap between the preferential flow paths and the predicted minimum resistance path determines the accuracy in individual samples. An evolutionary algorithm is employed to determine the “fittest” weighting scheme (here, the channel’s arc length to pore throat ratio) that maximizes accuracy across the entire dataset while minimizing over-parameterization. Finally, the structural similarity of neighboring edges is analyzed to explain the spatial arrangement of preferential flow within the pore network. We find that connected edges within the preferential flow subnetwork are highly similar, while those within the stagnant flow subnetwork are dissimilar. The contrast in similarity between these regions increases with flow channelization, explaining the structural constraints to local flow. The proposed framework may be used for fast characterization of porous media heterogeneity relative to computationally expensive direct numerical simulations. Article Highlights: 1.A quantitative assessment of flow channeling is proposed that distinguishes pore-scale flow fields into preferential and stagnant flow regions.2.Geometry and topology of the pore network are used to predict the spatial distribution of fast flow paths from structural data alone.3.Local disorder of pore networks provides structural constraints for flow separation into preferential v stagnant regions and informs on their velocity contrast.

Researchers theorize that many real-world networks exhibit community structure where within-community edges are more likely than between-community edges. While numerous methods exist to cluster nodes into different communities, less work has addressed this question: given some network, does it exhibit statistically meaningful community structure? We answer this question in a principled manner by framing it as a statistical hypothesis test in terms of a general and model-agnostic community structure parameter. Leveraging this parameter, we propose a simple and interpretable test statistic used to formulate two separate hypothesis testing frameworks. The first is an asymptotic test against a baseline value of the parameter while the second tests against a baseline model using bootstrap-based thresholds. We prove theoretical properties of these tests and demonstrate how the proposed method yields rich insights into real-world datasets. [ABSTRACT FROM AUTHOR]

Compartmental models of disease spread have been well studied on networks built according to the Configuration Model, i.e., where the degree distribution of individual nodes is specified, but where connections are made randomly. Dynamics of spread on such “first order” networks were shown to be profoundly different compared to epidemics under the traditional mass action assumption. Assortativity, i.e., the preferential mixing of nodes according to degree, is a second order property that is thought to impact epidemic trajectory. We first show how assortative mixing can come about from individual preferences to connect with others of lower or higher degree, and propose an algorithm for constructing such a network. We then investigate via simulation how this network structure favors or inhibits diffusion processes, such as the spread of an infectious disease.

Contact networks are heterogeneous. People with similar characteristics are more likely to interact, a phenomenon called assortative mixing or homophily. Empirical age-stratified social contact matrices have been derived by extensive survey work. We lack however similar empirical studies that provide social contact matrices for a population stratified by attributes beyond age, such as gender, sexual orientation, or ethnicity. Accounting for heterogeneities with respect to these attributes can have a profound effect on model dynamics. Here, we introduce a new method, which uses linear algebra and non-linear optimization, to expand a given contact matrix to populations stratified by binary attributes with a known level of homophily. Using a standard epidemiological model, we highlight the effect homophily can have on model dynamics, and conclude by briefly describing more complicated extensions. The available Python source code enables any modeler to account for the presence of homophily with respect to binary attributes in contact patterns, ultimately yielding more accurate predictive models.

The basic reproduction number (R0) is an established concept to describe the potential for an infectious disease to cause an epidemic and to derive estimates of the required effect of interventions for successful control. Calculating R0 from simple deterministic transmission models may result in biased estimates when important sources of heterogeneity related to transmission and control are ignored. Using stochastic simulations with a geographically stratified individual‐based SEIR (susceptible, exposed, infectious, recovered) model, we illustrate that if heterogeneity is ignored (i.e. no or too little assumed interindividual variation or assortative mixing) this may substantially overestimate the transmission rate and the potential course of the epidemic. Consequently, predictions for the impact of interventions then become relatively pessimistic. However, should such an intervention be suspended, then the potential for a consecutive epidemic wave will depend strongly on assumptions about heterogeneity, with more heterogeneity resulting in lower remaining epidemic potential, due to selection and depletion of high‐risk individuals during the early stages of the epidemic. These phenomena have likely also affected current model predictions regarding COVID‐19, as most transmission models assume homogeneous mixing or at most employ a simple age stratification, thereby leading to overcautious predictions of durations of lockdowns and required vaccine coverage levels. [ABSTRACT FROM AUTHOR]

Animal color pattern is a phenotypic trait that may mediate assortative mixing (also known as homophily), whereby similar looking individuals have stronger social associations. Masai giraffe (Giraffa camelopardalis tippelskirchi) coat spot patterns show high variation and some spot traits appear to be heritable. Giraffes also have high visual acuity, which may facilitate intraspecific communication and recognition based on spot patterns. Giraffe groupings are dynamic, merging and splitting throughout the day, but females form long‐term associations. We predicted that adult female giraffes show stronger associations with other females that have similar spot pattern traits. We quantified the spot pattern characteristics of 399 adult female Masai giraffes and determined the pattern similarity among pairs (dyads) in their social network. We then tested for an association between coat pattern similarity (spot size, shape, and orientation) and dyadic association strength, and quantified assortative mixing. The strength of social associations was positively correlated with similarity in spot shape. Our results are compatible with assortativity by coat patterns that are similar between mother and offspring, potentially reflecting an effect of relatedness on both pattern similarity and female social associations. These results offer evidence that spot pattern could function as a visual cue for intraspecific communication and kin or individual recognition in a fission‐fusion species. [ABSTRACT FROM AUTHOR]

Background: Most studies assessing the impact of online media and social media use on COVID-19 vaccine hesitancy predominantly rely on survey data, which often fail to capture the clustering of health opinions and behaviors within real-world networks. In contrast, research using social network analysis aims to uncover the diverse communities and discourse themes related to vaccine support and hesitancy within social media platforms. Despite these advancements, there is a gap in the literature on how a person's social circle affects vaccine acceptance, wherein an important part of social influence stems from offline interactions., Objective: We aimed to examine how online media consumption influences vaccination decisions within real-world social networks by analyzing unique quantitative network data collected from Romania, an Eastern European state and member of the European Union., Methods: We conducted 83 face-to-face interviews with participants from a living lab in Lerești, a small rural community in Romania, using a personal network analysis framework. This approach involved gathering data on both the respondents and individuals within their social circles (referred to as alters). After excluding cases with missing data, our analysis proceeded with 73% (61/83) of the complete personal networks. To examine the hierarchical structure of alters nested within ego networks, we used a mixed multilevel logistic regression model with random intercepts. The model aimed to predict vaccination status among alters, with the focal independent variable being the respondents' preferred source of health and prevention information. This variable was categorized into 3 types: traditional media, online media (including social media), and a combination of both, with traditional media as the reference category., Results: In this study, we analyzed 61 personal networks, encompassing between 15 and 25 alters each, totaling 1280 alters with valid data across all variables of interest. Our primary findings indicate that alters within personal networks, whose respondents rely solely on online media for health information, exhibit lower vaccination rates (odds ratio [OR] 0.37, 95% CI 0.15-0.92; P=.03). Conversely, the transition from exclusive traditional media use to a combination of both traditional and online media does not significantly impact vaccination rate odds (OR 0.75, 95% CI 0.32-1.78; P=.52). In addition, our analysis revealed that alters in personal networks of respondents who received the vaccine are more likely to have received the vaccine themselves (OR 3.75, 95% CI 1.79-7.85; P<.001)., Conclusions: Real-world networks combine diverse human interactions and attributes along with consequences on health opinions and behaviors. As individuals' vaccination status is influenced by how their social alters use online media and vaccination behavior, further insights are needed to create tailored communication campaigns and interventions regarding vaccination in areas with low levels of digital health literacy and vaccination rates, as Romania exposes., (©Iulian Oană, Marian-Gabriel Hâncean, Matjaž Perc, Jürgen Lerner, Bianca-Elena Mihăilă, Marius Geantă, José Luis Molina, Isabela Tincă, Carolina Espina. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 25.10.2024.)

We aimed to investigate the sexual mixing by human papillomavirus (HPV) vaccination status in male-male partnerships and estimate the proportion of male-male partnerships protected against HPV. We analyzed male-male partnerships attending the Melbourne Sexual Health Center between 2018 and 2019. Data on self-reported HPV vaccination status were collected. Newman’s assortativity coefficient was used to examine the sexual mixing by HPV vaccination status. Assortativity refers to the tendency of individuals to have partners with similar characteristics (i.e. same vaccination status). Of 321 male-male partnerships where both men reported their HPV vaccination status, 52.6% (95% CI: 47.0–58.2%) partnerships had both men vaccinated, 32.1% (95% CI: 27.0–37.5%) partnerships had only one man vaccinated, and 15.3% (95% CI: 11.5–19.7%) had both men unvaccinated. The assortativity on HPV vaccination status was moderate (assortativity coefficient = 0.265, 95% CI: 0.196–0.335). There were about 15% of male-male partnerships where both men were not protected against HPV. Interventions targeting vaccinated individuals to encourage their unvaccinated partners to be vaccinated might increase the HPV vaccine coverage.

The paper concerns the development of methods of EEG functional connectivity estimation including short overview of the currently applied measures describing their advantages and flaws. Linear and non-linear, bivariate and multivariate methods are confronted. The performance of different connectivity measures in respect of robustness to noise, common drive effect and volume conduction is considered providing a guidance towards future developments in the field, which involve evaluation not only functional, but also effective (causal) connectivity. The time-varying connectivity measure making possible estimation of dynamical information processing in brain is presented. The methods of post-processing of connectivity results are considered involving application of advanced graph analysis taking into account community structure of networks and providing hierarchy of networks rather than the single, binary networks currently used. [ABSTRACT FROM AUTHOR]

The paper concerns the development of methods of EEG functional connectivity estimation including short overview of the currently applied measures describing their advantages and flaws. Linear and non-linear, bivariate and multivariate methods are confronted. The performance of different connectivity measures in respect of robustness to noise, common drive effect and volume conduction is considered providing a guidance towards future developments in the field, which involve evaluation not only functional, but also effective (causal) connectivity. The time-varying connectivity measure making possible estimation of dynamical information processing in brain is presented. The methods of post-processing of connectivity results are considered involving application of advanced graph analysis taking into account community structure of networks and providing hierarchy of networks rather than the single, binary networks currently used.

A toy model to examine the effect of a heterogeneous edge weight structure on assortative mixing patterns is developed. This model is used as a benchmark to assess assortative mixing patterns in a real military personnel network describing occupation changes among recruits to the Canadian Armed Forces. Mixing patterns on the network suggest a strong tendency for members to transfer between different occupation groups; possible areas on which to focus retention strategies are identified. [ABSTRACT FROM AUTHOR]

Abstract In this paper we present a generalization of the classical configuration model. Like the classical configuration model, the generalized configuration model allows users to specify an arbitrary degree distribution. In our generalized configuration model, we partition the stubs in the configuration model into b blocks of equal sizes and choose a permutation function h for these blocks. In each block, we randomly designate a number proportional to q of stubs as type 1 stubs, where q is a parameter in the range [0,1]. Other stubs are designated as type 2 stubs. To construct a network, randomly select an unconnected stub. Suppose that this stub is in block i. If it is a type 1 stub, connect this stub to a randomly selected unconnected type 1 stub in block h(i). If it is a type 2 stub, connect it to a randomly selected unconnected type 2 stub. We repeat this process until all stubs are connected. Under an assumption, we derive a closed form for the joint degree distribution of two random neighboring vertices in the constructed graph. Based on this joint degree distribution, we show that the Pearson degree correlation function is linear in q for any fixed b. By properly choosing h, we show that our construction algorithm can create assortative networks as well as disassortative networks. We present a percolation analysis of this model. We verify our results by extensive computer simulations.

Abstract The friendship paradox is the observation that friends of individuals tend to have more friends or be more popular than the individuals themselves. In this work, we first study local metrics to capture the strength of the paradox and the direction of the paradox from the perspective of individual nodes, i.e., an indication of whether the individual is more or less popular than its friends. These local metrics are aggregated, and global metrics are proposed to express the phenomenon on a network-wide level. Theoretical results show that the defined metrics are well-behaved enough to capture the friendship paradox. We also theoretically analyze the behavior of the friendship paradox for popular network models in order to understand regimes where friendship paradox occurs. These theoretical findings are complemented by experimental results on both network models and real-world networks. By conducting a correlation study between the proposed metrics and degree assortativity, we experimentally demonstrate that the phenomenon of the friendship paradox is related to the well-known phenomenon of assortative mixing.

A monopoly sells a network good to a large population of consumers. We explore how the monopoly's profit and the consumer surplus vary with the arrival of public information about the network structure. The analysis reveals that, under homogeneous preferences for the good, degree assortativity ensures that information arrival increases both profit and consumer surplus. In contrast, heterogeneous preferences for the good can create a tension between consumer surplus and profit. [ABSTRACT FROM AUTHOR]

Abstract We focus on spatially-extended networks during their transition from short-range connectivities to a scale-free structure expressed by heavy-tailed degree-distribution. In particular, a model is introduced for the generation of such graphs, which combines spatial growth and preferential attachment. In this model the transition to heterogeneous structures is always accompanied by a change in the graph’s degree-degree correlation properties: while high assortativity levels characterize the dominance of short distance couplings, long-range connectivity structures are associated with small amounts of disassortativity. Our results allow to infer that a disassortative mixing is essential for establishing long-range links. We discuss also how our findings are consistent with recent experimental studies of 2-dimensional neuronal cultures.

In recent years, seismic time series has been used to construct complex network models in order to describe the seismic complexity. The effect of the factor focal depth has been elided in some of these models. In this paper, we aim to construct a new complex network model for seismicity by considering depth factor from the earthquake catalog and investigate the statistical properties of the network. Since the networks have been proved to be scale-free and small-world properties, the new network models should be studied whether the properties have changed. The results show that the new network model by considering depth factor is still scale-free and small-world. However, it is found that its average degree is smaller than the original network. The clustering coefficient increases at the year including mainshocks. The assortativity coefficient, which demonstrates preferential attachment of nodes, is positive and shows consistent pattern when main shocks occur. [ABSTRACT FROM AUTHOR]

Transmission of EEG activity during a visual and auditory version of the working memory task based on the paradigm of linear syllogism was investigated. Our aim was to find possible similarities and differences in the synchronization patterns between brain structures during the same mental activity performed on different modality stimuli. The EEG activity transmission was evaluated by means of full frequency Directed Transfer Function (ffDTF) and short-time Directed Transfer Function (SDTF). SDTF provided information on dynamical propagation of EEG activity. The assortative mixing approach was applied to quantify coupling between regions of interest encompassing frontal, central and two posterior modules. The results showed similar schemes of coupling for both modalities with stronger coupling within the regions of interests than between them, which is concordant with the theories concerning efficient wiring and metabolic energy saving. The patterns of transmission showed main sources of activity in the anterior and posterior regions communicating intermittently in a broad frequency range. The differences between the patterns of transmission between the visual and auditory versions of working memory tasks were subtle and involved bigger propagation from the posterior electrodes towards the frontal ones during the visual task as well as from the temporal sites to the frontal ones during the auditory task. [ABSTRACT FROM AUTHOR]

Background: The extent to which individuals are similar to their sexual partners influences STI-transmission probabilities, yet there is a dearth of empirical data, especially those representative of the population.Methods: Analyses of data reported by 10 759 sexually active people aged 16-74 y interviewed for a British national probability survey undertaken in 2010-12. Computer-assisted self-interviews asked about partner numbers and characteristics of participants' three most recent partnerships (MRPs). Opposite-sex MRPs were weighted to represent all such partnerships in the past year (N = 16 451). Estimates of disassortative age mixing (≥±5-y difference), ethnic mixing (partner of a different ethnic group) and geographical mixing (partner from a different region/country when they first met) were calculated, stratified by gender, age group and partnership status (casual/steady). Multivariable regression examined how these disassortative mixing measures were associated with STI-risk measures: condom use at first sex together at the partnership-level and, at the participant-level, STI-risk perception and reporting STI diagnoses.Results: Disassortative age mixing occurred in around one-third of opposite-sex partnerships, with men ≥5 y older in most cases, although this proportion varied by participant's gender and age group. Ethnic mixing occurred less frequently (11.3% of men's and 8.6% of women's partnerships) as did geographical mixing (14.1 and 16.3%, respectively). Disassortative mixing was more common among casual vs steady partnerships. Condom use at first sex was less likely in women's partnerships that were age-disassortative [adjusted odds ratio (AOR): 0.79, 95% confidence interval (CI): 0.69-0.95], whereas men reporting disassortative ethnic mixing were more likely to perceive themselves at STI risk (AOR: 1.76, 95% CI: 1.23-2.52) and report STI diagnoses (AOR: 2.37, 95% CI: 1.22-4.59).Conclusions: Disassortative mixing, although uncommon among opposite-sex partnerships in Britain, is independently associated with STI risk, warranting consideration in STI-prevention efforts. [ABSTRACT FROM AUTHOR]

Academic collaboration in the social sciences is characterized by a polarization between hermeneutic and nomological researchers. This polarization is expressed in different publication strategies. The present article analyzes the complete co-authorship networks in a social science discipline in two separate countries over five years using an exponential random graph model. It examines whether and how assortative mixing in publication strategies is present and leads to a polarization in scientific collaboration. In the empirical analysis, assortative mixing is found to play a role in shaping the topology of the network and significantly explains collaboration. Co-authorship edges are more prevalent within each of the groups, but this mixing pattern does not fully account for the extent of polarization. Instead, a thought experiment reveals that other components of the complex system dampen or amplify polarization in the data-generating process and that microscopic interventions targeting behavior change with regard to assortativity would be hindered by the resilience of the system. The resilience to interventions is quantified in a series of simulations on the effect of microscopic behavior on macroscopic polarization. The empirical study controls for geographic proximity, supervision, and topical similarity (using a vector space model), and the interplay of these factors is likely responsible for this resilience. The paper also predicts the co-authorship network in one country based on the model of collaborations in the other country. [ABSTRACT FROM AUTHOR]

We present a study of social networks based on the analysis of Brazilian and Portuguese family names (surnames). We construct networks whose nodes are names of families and whose edges represent parental relations between two families. From these networks we extract the connectivity distribution, clustering coefficient, shortest path and centrality. We find that the connectivity distribution follows an approximate power law. We associate the number of hubs, centrality and entropy to the degree of miscegenation in the societies in both countries. Our results show that Portuguese society has a higher miscegenation degree than Brazilian society. All networks analyzed lead to approximate inverse square power laws in the degree distribution. We conclude that the thermodynamic limit is reached for small networks (3 or 4 thousand nodes). The assortative mixing of all networks is negative, showing that the more connected vertices are connected to vertices with lower connectivity. Finally, the network of surnames presents some small world characteristics. [ABSTRACT FROM AUTHOR]

Many real-world networks are known to exhibit facts that counter our knowledge prescribed by the theories on network creation and communication patterns. A common prerequisite in network analysis is that information on nodes and links will be complete because network topologies are extremely sensitive to missing information of this kind. Therefore, many real-world networks that fail to meet this criterion under random sampling may be discarded. In this paper, we offer a framework for interpreting the missing observations in network data under the hypothesis that these observations are not missing at random. We demonstrate the methodology with a case study of a financial trade network, where the awareness of agents to the data collection procedure by a self-interested observer may result in strategic revealing or withholding of information. The non-random missingness has been overlooked despite the possibility of this being an important feature of the processes by which the network is generated. The analysis demonstrates that strategic information withholding may be a valid general phenomenon in complex systems. The evidence is sufficient to support the existence of an influential observer and to offer a compelling dynamic mechanism for the creation of the network. [ABSTRACT FROM AUTHOR]

The following paper presents the changes and stability of assortative mixing, and inter-group ties in Hungary from 1987 to 2005. The demographic categories under investigation are age, sex, and education. The analysis has a special focus on the rearrangement of the context of tie formation, and the inequality of receiving choices into personal networks along social categories. The most substantial change during the period, is the strong decrease in gender homophily, and some strengthening of intergenerationalties. Both of these findings are in line with the observation that personal networks are recruited more often among the members of the nuclear-family. This latter phenomenon is probably due to the shrinking network size. However, this set of finding is prone to the methodological criticism formulated in the US context, that these observations are in fact the result of the interviewer effect. Finally, the study found stable patterns of educational network prestige, and describes the changes of social capital attached to categories of gender and age.

Network theoretic approach has been used to model and study the flow of ecological information, growth and connectivity on landscape level of anemochory (wind dispersal) of Himalayan moist temperate forest species in the Western Himalaya region. A network is formally defined and derived for seed dispersion model of target floral species where vertices represent habitat patches which are connected by an edge if the distance between the patches is less than a threshold distance. We define centrality of a network and computationally identify the habitat patches that are central to the process of seed dispersion to occur across the network. These central patches are located on map and geographical regions critically important for the flow of ecological information across the network are identified as Gharwal region and eastern Himachal Pradesh of Indian Himalaya. We find that the network of habitat patches is a scale-free network and at the same time it also displays small-world property characterized by high clustering and low average shortest path length. As a result, ecological information propagates rapidly and evenly on a local scale. Hubs in the network are identified as important centres for dissemination of ecological information (seeds) and need to be conserved against a potential attack by malicious agents and also ecological shocks. The network showcase a well-formed community structure. As a consequence of these structural properties of the network, anemochory floral species studied in this work are likely to thrive across the ecological network of forest patches in the Western Himalaya region over time. [ABSTRACT FROM AUTHOR]

Human memory can process materials in a fast way and maintain its robust function in a dynamical method. Previous research assumed that there are efficient and robust patterns embedded in network structures of semantic memory. It also revealed two global structure patterns: small world structure which facilitates memory processes and heterogeneity that reinforces memory structures (Feng et al. 2009, Complex Syst. Complexity Sci., 6, 40-49). This article provides further in-depth evidences for the assumption that there are intrinsically embedded patterns of efficiency and robustness in the network structures of semantic memories. Based on two-word association thesauruses, network models are constructed, respectively, for the first-order relations of immediate associations (Type I networks) and the second-order relations of semantic framework sharing connections (Type II networks). The analysis explores the following patterns of heterogeneity, small world, assortative mixing and hierarchical clustering, and it also includes simulations for the random processes of degree-preserving rewiring and vertices/edges deleting which will presumably operate on memory systems. The results reveal that the Type II networks have intrinsic and stable patterns of quick retrieval and adaptive storage, and the Type I networks have similar but easily attacked patterns. Thereby this study provides complex network perspectives for further modelling and interpreting intrinsic and non-trivial patterns embedded in semantic memory. [ABSTRACT FROM AUTHOR]