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Using the 9thround of European Social Survey (ESS), we explored the relationship between Europeans’ basic values and their attitudes towards immigrants. Employing a latent class analysis (LCA), we classified the respondents based on three items capturing the extent to which participants would support allowing three groups of immigrants to enter and live in their countries: immigrants of same ethnic groups, immigrants of different ethnic groups, and immigrants from poorer countries outside Europe. Fourclassesof Europeans with mutually exclusive response patterns with respect to their inclusive attitudes towards immigrants were found. The classes were named Inclusive (highly inclusive), Some (selective), Few (highly selective), and Exclusive (highly exclusive). Next, using a network technique, a partial correlation network of 10 basic human values was estimated for each class of participants. The four networks were compared to each other based on three network properties namely:global connectivity,community detection, andassortativity coefficient. The global connectivity (the overall level of interconnections) between the 10 basic values was found to be mostly invariant across the four networks. However, results of the community detection analysis revealed a more complex value structure among the most inclusive class of Europeans. Further, according to the assortativity analysis, as expected, for the most inclusive Europeans, values with similar motivational backgrounds were found to be interconnected most strongly to one another. We further discussed the theoretical and practical implications of our findings.

In light of the long-term pressure and short-term impact of economic and technological globalization, regional and urban resilience has become an important issue in research. As a new organizational form of regional urban systems, the resilience of urban networks generated by flow space has emerged as a popular subject of research. By gathering 2017 data from the Baidu search index, the Tencent location service, and social statistics, this study constructs information, transportation, and economic networks among 344 cities in China to analyze the spatial patterns of urban networks and explore their structural characteristics from the perspectives of hierarchy and assortativity. Transmissibility and diversity were used to represent the resilience of the network structure in interruption scenarios (node failure and maximum load attack). The results show the following: The information, transportation, and economic networks of cities at the prefecture level and higher in China exhibit a dense pattern of spatial distribution in the east and a sparse pattern in the west; however, there are significant differences in terms of hierarchy and assortativity. The order of resilience of network transmissibility and diversity from strong to weak was information, economic, transportation. Transmissibility and diversity had nearly identical scores in response to the interruption of urban nodes. Moreover, a highly heterogeneous network was more likely to cause shocks to the network structure, owing to its cross-regional urban links in case of disturbance. We identified 12 dominant nodes and 93 vulnerable nodes that can help accurately determine the impetus behind network structure resilience. The capacity of regions for resistance and recovery can be improved by strengthening the construction of emergency systems and risk prevention mechanisms.

Background: We aimed to explore what kind of social networks characterizable as “consult/be consulted” are built among healthcare professionals in a community and the impact of providing the professionals with these findings.Methods: We adopted mixed methods exploratory study using social network analysis (SNA) and content analysis. SNA can visualize social network structures such as relationships between individuals. The healthcare professionals were asked about the key persons they consulted and were consulted by concerning these healthcare issues: (1) daily work; (2) a person with acute back pain; (3) a garbage-filled house reported by a neighbor; (4) a person with dementia; and (5) a study meeting. We identified the key roles depending on the issues using SNA. After analysis, the analytical findings were shared with the participants. To explore their cognitive responses, an open-ended questionnaire was delivered and a content analysis was implemented.Results: Of 54 healthcare professional participants, the data of 52 were available for analysis. The findings (in the respective order of the five topics above) were as follows: the number of nodes was 165, 95, 85, 82, and 68; clustering coefficient was 0.19, 0.03, 0.02, 0.11, and 0.23; assortativity was −0.043, −0.11, −0.23, −0.17, and −0.23; reciprocity was 0.35, 0.31, 0.39, 0.29, and 0.48. The top three centralities included nurses. Eighty-seven free comments were received, of which 39 were categorized as descriptive, 10 as analytical, and 38 as critical.Discussion: The structure of “consult/be consulted” networks differed by topic. SNA is available to detect the healthcare resources network and it may have helped them to reflect on their own networks.

Millions commute to work every day in cities and interact with colleagues, partners, friends, and strangers. Commuting facilitates the mixing of people from distant and diverse neighborhoods, but whether this has an imprint on social inclusion or instead, connections remain assortative is less explored. In this paper, we aim to better understand income sorting in social networks inside cities and investigate how commuting distance conditions the online social ties of Twitter users in the 50 largest metropolitan areas of the United States. An above-median commuting distance in cities is linked to more diverse individual networks, moreover, we find that longer commutes are associated with a nearly uniform, moderate reduction of overall social tie assortativity across all cities. This suggests a universal relation between long-distance commutes and the integration of social networks. Our results inform policy that facilitating access across distant neighborhoods can advance the social inclusion of low-income groups.

The study was performed using archival data from the laboratory of genetic epidemiology of the Research Center for Medical Genetics. Of the 65 747 marriage records for Udmurtia, Chuvashia, Mari El, Bashkiria, and Tatarstan dating back to the end of the 20th century, 15 804 were selected in which at least one of the spouses was Russian. Russians make up almost 270 000 people out of the 1.2 million population of the surveyed areas. The intensity of crossbreeding of the Russian population was 52.5%. The marital ethnic assortativity was 2.3. Data is provided for all five republics.

Background Globally, tuberculosis disease (TB) is more common among males than females. Recent research proposes that differences in social mixing by sex could alter infection patterns in TB. We examine evidence for two mechanisms by which social-mixing could increase men’s contact rates with TB cases. First, men could be positioned in social networks such that they contact more people or social groups. Second, preferential mixing by sex could prime men to have more exposure to TB cases. Methods We compared the networks of male and female TB cases and healthy matched controls living in Kampala, Uganda. Specifically, we estimated their positions in social networks (network distance to TB cases, degree, betweenness, and closeness) and assortativity patterns (mixing with adult men, women, and children inside and outside the household). Results The observed network consisted of 11,840 individuals. There were few differences in estimates of node position by sex. We found distinct mixing patterns by sex and TB disease status including that TB cases have proportionally more adult male contacts and fewer contacts with children. Conclusions This analysis used a network approach to study how social mixing patterns are associated with TB disease. Understanding these mechanisms may have implications for designing targeted intervention strategies in high-burden populations.

Critical services depend on infrastructure networks for their operation and any disruption to these networks can have significant impacts on society, the economy, and quality of life. Such networks can be characterised as graphs which can be used to understand their structural properties, and the effect on their behaviour and robustness to hazards. Using a suite of graphs and critical infrastructure networks, this study aims to show that networks which exhibit a hierarchical structure are more likely to be less robust comparatively to non-hierarchical networks when exposed to failures, including those which supply critical services. This study investigates the properties of a hierarchical structure through identifying a set of key characteristics from an ensemble of graph models which are then used in a comparative analysis against a suite of spatial critical infrastructure networks. A failure model is implemented and applied to understand the implications of hierarchical structures in real world networks for their robustness to perturbations. The study concludes that a set of three graph metrics, cycle basis, maximum betweenness centrality and assortativity coefficient, can be used to identify the extent of a hierarchy in graphs, where a lack of robustness is linked to the hierarchical structure, a feature exhibited in both graph models and infrastructure networks.

Globalisation continuously produces novel economic relationships mediated by flows of goods, services, capital, and information between countries. The activity of multinational corporations (MNCs) has become a primary driver of globalisation, shaping these relationships through vast networks of firms and their subsidiaries. Extensive empirical research has suggested that globalisation is not a singular process, and that variation in the intensity of international economic interactions can be captured by 'multiple globalisations', however how this differs across industry sectors has remained unclear. This paper analyses how sectoral variation in the 'structural architecture' of international economic relations can be understood using a combination of social network analysis (SNA) measures based on firm-subsidiary ownership linkages. Applying an approach that combines network-level measures (Density, Clustering, Degree, Assortativity) in ways yet to be explored in the spatial networks literature, a typology of four idealised international network structures is presented to allow for comparison between sectors. All sectoral networks were found to be disassortative, indicating that international networks based on intraorganisational ties are characterised by a core-periphery structure, with professional services sectors such as Banks and Insurance being the most hierarchically differentiated. Retail sector networks, including Food & Staples Retailing, are the least clustered while the two most clustered networks-Materials and Capital Goods-have also the highest average degree, evidence of their extensive globalisations. Our findings suggest that the multiple globalisations characterising international economic interactions can be better understood through the 'structural architecture' of sectoral variation, which result from the advantages conferred by cross-border activity within each. ispartof: PLOS ONE vol:16 issue:8 ispartof: location:United States status: published

International audience; Identifying influential nodes in social networks is a fundamental issue. Indeed, it has many applications, such as inhibiting epidemic spreading, accelerating information diffusion, preventing terrorist attacks, and much more. Classically, centrality measures quantify the node's importance based on various topological properties of the network, such as Degree and Betweenness. Nonetheless, these measures are agnostic of the community structure, although it is a ubiquitous characteristic encountered in many real-world networks. To overcome this drawback, there is a growing trend to design so-called community-aware centrality measures. Although several works investigate the relationship between various classical centrality measures [1-3], the interplay between classical and community-aware centrality measures is still unexplored. This work presents an extensive investigation aimed at a better understanding of the relationship between community-aware centrality measures, classical centrality measures, and network topology. Artificial and real-world networks are used in the experiments. The Kendall's Tau correlation quantifies the interaction between ten classical and twenty-eight community-aware centrality measures. The community-aware centrality measures are divided into three groups. The first group's ten measures are based on the intra-community links of a node (local measures). The second group's twelve measures are based on the inter-community links of a node (global measures). Finally, the six measures of the third group consider both types of links (mixed measures). The LFR algorithm generates artificial networks with controlled properties. Indeed, the community structure strength (µ), the exponent of the degree distribution (γ), and the community size distribution (θ) can be specified. The experiments show that the community structure strength is the main feature governing the correlation between classical and community-aware centrality measures. The heatmap on the left of Figure 1 represents the correlation in an artificial network with a strong community structure. The global community-aware centrality measures exhibit a low correlation with classical centrality measures, while local communityaware centrality measures show a high correlation. The results are inverted when the network has a weak community structure. Differences are more subtle for mixed community-aware measures. One can also notice that results are relatively insensitive to variation of the degree and community size distributions' exponents. Fifty real-world networks originating from various domains are also investigated. Linear regression is performed considering six macroscopic (

Complex networks have become an increasingly relevant research topic in machine learning, with many learning systems in the literature successfully exploring complex network properties and measures. In data classification, the use of complex networks allows the detection of structural and topological patterns related, for example, to the formation pattern of the input data. Some measures of complex networks have already been used in this sense. However, a systematic study capable of characterizing such measures in the context of data classification is lacking in the literature. In this work, we evaluate comparatively the predictive performance of some measures. Specifically, eight complex network measures were selected from the literature, namely: assortativity, average local clustering coefficient, average degree, betweenness, average shortest path length, closeness, global clustering coefficient and eigenvector centrality. For our analyses, both artificial and real-world data sets were considered. The results show that measures such as average shortest path and assortativity, besides presenting high predictive capability, are also more robust to the variation of the network structure. In summary, this research paves a way to support other related works in selecting more appropriate complex network measures for data classification.

Recent studies provide novel insights into the meso-scale organization of the brain, highlighting the co-occurrence of different structures: classic assortative (modular), disassortative, and core-periphery. However, the spectral properties of the brain meso-scale remain mostly unexplored. To fill this knowledge gap, we investigated how the meso-scale structure is organized across the frequency domain. We analyzed the resting state activity of healthy participants with source-localized high-density electroencephalography signals. Then, we inferred the community structure using weighted stochastic block-model (WSBM) to capture the landscape of meso-scale structures across the frequency domain. We found that different meso-scale modalities co-exist and are diversely organized over the frequency spectrum. Specifically, we found a core-periphery structure dominance, but we also highlighted a selective increase of disassortativity in the low frequency bands (

Biodiversity results from differentiation mechanisms developing within biological populations. Such mechanisms are influenced by the properties of the landscape over which individuals interact, disperse and evolve. Notably, landscape connectivity and habitat heterogeneity constrain the movement and survival of individuals, thereby promoting differentiation through drift and local adaptation. Nevertheless, the complexity of landscape features can blur our understanding of how they drive differentiation. Here, we formulate a stochastic, eco-evolutionary model where individuals are structured over a graph that captures complex connectivity patterns and accounts for habitat heterogeneity. Individuals possess neutral and adaptive traits, whose divergence results in differentiation at the population level. The modelling framework enables an analytical underpinning of emerging macroscopic properties, which we complement with numerical simulations to investigate how the graph topology and the spatial habitat distribution affect differentiation. We show that in the absence of selection, graphs with high characteristic length and high heterogeneity in degree promote neutral differentiation. Habitat assortativity, a metric that captures habitat spatial autocorrelation in graphs, additionally drives differentiation patterns under habitat-dependent selection. While assortativity systematically amplifies adaptive differentiation, it can foster or depress neutral differentiation depending on the migration regime. By formalising the eco-evolutionary and spatial dynamics of biological populations in complex landscapes, our study establishes the link between landscape features and the emergence of diversification, contributing to a fundamental understanding of the origin of biodiversity gradients.Significance statementIt is not clear how landscape connectivity and habitat heterogeneity influence differentiation in biological populations. To obtain a mechanistic understanding of underlying processes, we construct an individualbased model that accounts for eco-evolutionary and spatial dynamics over graphs. Individuals possess both neutral and adaptive traits, whose co-evolution results in differentiation at the population level. In agreement with empirical studies, we show that characteristic length, heterogeneity in degree and habitat assortativity drive differentiation. By using analytical tools that permit a macroscopic description of the dynamics, we further link differentiation patterns to the mechanisms that generate them. Our study provides support for a mechanistic understanding of how landscape features affect diversification.

Advances in magnetic resonance imaging, particularly diffusion imaging, have allowed researchers to analyze brain connectivity. Identification of structural connectivity differences between patients with normal cognition, cognitive impairment, and dementia could lead to new biomarker discoveries that could improve dementia diagnostics. In our study, we analyzed 22 patients (11 control group patients, 11 dementia group patients) that underwent 3T MRI diffusion tensor imaging (DTI) scans and the Montreal Cognitive Assessment (MoCA) test. We reconstructed DTI images and used the Desikan–Killiany–Tourville cortical parcellation atlas. The connectivity matrix was calculated, and graph theoretical analysis was conducted using DSI Studio. We found statistically significant differences between groups in the graph density, network characteristic path length, small-worldness, global efficiency, and rich club organization. We did not find statistically significant differences between groups in the average clustering coefficient and the assortativity coefficient. These statistically significant graph theory measures could potentially be used as quantitative biomarkers in cognitive impairment and dementia diagnostics.

ObjectivesThe purpose of this study was to (1) explore the changes in topological properties of static and dynamic brain functional networks after nasopharyngeal carcinoma (NPC) radiotherapy (RT) using rs-fMRI and graph theoretical analysis, (2) explore the correlation between cognitive function and changes in brain function, and (3) add to the understanding of the pathogenesis of radiation brain injury (RBI).MethodsFifty-four patients were divided into 3 groups according to time after RT: PT1 (0–6 months); PT2 (>6 to ≤12 months); and PT3 (>12 months). 29 normal controls (NCs) were included. The subjects’ topological properties were evaluated by graph-theoretic network analysis, the functional connectivity of static functional networks was calculated using network-based statistics, and the dynamic functional network matrix was subjected to cluster analysis. Finally, correlation analyses were conducted to explore the relationship between the altered network parameters and cognitive function.ResultsAssortativity, hierarchy, and network efficiency were significantly abnormal in the PT1 group compared with the NC or PT3 group. The small-world variance in the PT3 group was smaller than that in NCs. The Nodal ClustCoeff of Postcentral_R in the PT2 group was significantly smaller than that in PT3 and NC groups. Functional connectivities were significantly reduced in the patient groups. Most of the functional connectivities of the middle temporal gyrus (MTG) were shown to be significantly reduced in all three patient groups. Most of the functional connectivities of the insula showed significantly reduced in the PT1 and PT3 groups, and most of the functional connectivities in brain regions such as frontal and parietal lobes showed significantly reduced in the PT2 and PT3 groups. These abnormal functional connectivities were correlated with scores on multiple scales that primarily assessed memory, executive ability, and overall cognitive function. The frequency F of occurrence of various states in each subject differed significantly, and the interaction effect of group and state was significant.ConclusionThe disruption of static and dynamic functional network stability, reduced network efficiency and reduced functional connectivity may be potential biomarkers of RBI. Our findings may provide new insights into the pathogenesis of RBI from the perspective of functional networks.

We propose a quantitative metric (called relative assortativity index, RAI) to assess the extent with which a real-world network would become relatively more assortative due to link addition(s) using a link prediction technique. Our methodology is as follows: for a link prediction technique applied on a particular real-world network, we keep track of the assortativity index values incurred during the sequence of link additions until there is negligible change in the assortativity index values for successive link additions. We count the number of network instances for which the assortativity index after a link addition is greater or lower than the assortativity index prior to the link addition and refer to these counts as relative assortativity count and relative dissortativity count, respectively. RAI is computed as (relative assortativity count − relative dissortativity count) / (relative assortativity count + relative dissortativity count). We analyzed a suite of 80 real-world networks across different domains using 3 representative neighborhood-based link prediction techniques (Preferential attachment, Adamic Adar and Jaccard coefficients [JACs]). We observe the RAI values for the JAC technique to be positive and larger for several real-world networks, while most of the biological networks exhibited positive RAI values for all the three techniques.

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.

Understanding the neural mechanisms of suicidal behavior is crucial. While regional brain alterations have previously been reported, knowledge about brain functional connectomics is currently limited. Here, we investigated differences in global topologic network properties and local network-based functional organization in both suicide attempters and suicide relatives. Two independent samples of depressed suicide attempters (N = 42), depressed patient controls (N = 43), healthy controls (N = 66) as well as one sample of healthy relatives of suicide victims (N = 16) and relatives of depressed patients (N = 16) were investigated with functional magnetic resonance imaging in the resting-state condition. Graph theory analyses were performed. Assortativity, clustering coefficients, global efficiency, and rich-club coefficients were calculated. A network-based statistic approach was finally used to examine functional connectivity matrices. In comparison to healthy controls, both patient groups showed significant reduction in assortativity, and decreased functional connectivity in largely central and posterior brain networks. Suicide attempters only differed from patient controls in terms of higher rich-club coefficients for the highest degree nodes. Compared to patient relatives and healthy controls, suicide relatives showed reduced assortativity, reduced clustering coefficients, increased global efficiency, and increased rich-club coefficients for the highest degree nodes. Suicide relatives also showed reduced functional connectivity in one anterior and one posterior sub-network in comparison to healthy controls, and in a largely anterior brain network in comparison to patient relatives. In conclusion, these results suggest that the vulnerability to suicidal behavior may be associated with heritable deficits in global brain functioning – characterized by weak resilience and poor segregation - and in functional organization with reduced connectivities affecting the ventral and dorsal prefrontal cortex, the anterior cingulate, thalamus, striatum, and possibly the insula, fusiform gyrus and the cerebellum.

Pathogens can spread epidemically through populations. Beneficial contagions, such as viruses that enhance host survival or technological innovations that improve quality of life, also have the potential to spread epidemically. How do the dynamics of beneficial biological and social epidemics differ from those of detrimental epidemics? We investigate this question using three theoretical approaches. First, in the context of population genetics, we show that a horizontally-transmissible element that increases fitness, such as viral DNA, spreads superexponentially through a population, more quickly than a beneficial mutation. Second, in the context of behavioral epidemiology, we show that infections that cause increased connectivity lead to superexponential fixation in the population. Third, in the context of dynamic social networks, we find that preferences for increased global infection accelerate spread and produce superexponential fixation, but preferences for local assortativity halt epidemics by disconnecting the infected from the susceptible. We conclude that the dynamics of beneficial biological and social epidemics are characterized by the rapid spread of beneficial elements, which is facilitated in biological systems by horizontal transmission and in social systems by active spreading behavior of infected individuals., Comment: The original version of this paper [v1] was produced, from conception of idea, to execution, to writing, by a team in just 72 hours (see Appendix of [v1]). This is a revised version

Spontaneous emergence of institutionalized cooperation in ubiquitous social dilemmas still is a field of highest relevance in behavioral and organizational economic research. In contrast to the theoretical prediction of defective behavior, manifold forms and degrees of cooperation exist in reality. We explain the emergence of general cooperation, even in one-shot interactions with strangers, from local interactions in a network through what we call a social-leverage mechanism. By this, more agents than just the two interaction partners get involved in an interaction, particularly common acquaintances. We analyze the social-leverage mechanism and conditions of cooperation under locality, common acquaintanceship, related assortativity, as well as “weak” and “strong ties” in the social network. We trace the co-evolution of the network, its structural dynamics, and stable cooperative equilibria. Our model relates to the tradition of emergent coordination in decentralized, non-Walrasian search, coordination and exchange systems (e.g., Diamond 1984; Axtell 2005) and in purely local interactions on, e.g., ring networks (e.g., Albin and Foley J Econ Behav Organ 18(1), 27–51, 1992). We conclude that, under social leverage, just local interaction may generate and stabilize general cooperation. We consider this the emergence of an exchange and trade system (a market) and relate this to empirical cases of the emergence of exchange cultures in the early Silk Road (seventh–ninth century) and in contemporary African countries, when formal enforcement through states and courts are largely lacking. We conclude applications and policy implications for cutting-edge techno-organizational areas of AI and platform economies.

Motivation: Epistatic miniarrary profile (EMAP) studies have enabled the mapping of large-scale genetic interaction networks and generated large amounts of data in model organisms. It provides an incredible set of molecular tools and advanced technologies that should be efficiently understanding the relationship between the genotypes and phenotypes of individuals. However, the network information gained from EMAP cannot be fully exploited using the traditional statistical network models. Because the genetic network is always heterogeneous, for example, the network structure features for one subset of nodes are different from those of the left nodes. Exponential-family random graph models (ERGMs) are a family of statistical models, which provide a principled and flexible way to describe the structural features (e.g., the density, centrality, and assortativity) of an observed network. However, the single ERGM is not enough to capture this heterogeneity of networks. In this paper, we consider a mixture ERGM (MixtureEGRM) networks, which model a network with several communities, where each community is described by a single EGRM. Results: EM algorithm is a classical method to solve the mixture problem, however, it will be very slow when the data size is huge in the numerous applications. We adopt an efficient novel online graph clustering algorithm to classify the graph nodes and estimate the ERGM parameters for the MixtureERGM. In comparison studies, the MixtureERGM outperforms the role analysis for the network cluster in which the mixture of exponential-family random graph model is developed for many ego-network according to their roles. One genetic interaction network of yeast and two real social networks (provided as supplemental materials, which can be found on the Computer Society Digital Library at http://doi.ieeecomputersociety.org/10.1109/TCBB.2017.2743711) show the wide potential application of the MixtureERGM.

The proposal of the Belt and Road Initiative has created new opportunities for agricultural product trading. In this study, we conduct an empirical analysis of the competitiveness and complementarity of agri-trade among countries in the region by constructing dynamic networks based on the Export Similarity Index and Trade Complementarity Index from 2005 to 2016. The results indicate that competitiveness and complementarity came to coexist within 10 years and the density of both competitiveness and complementarity network increased; overall, complementarity was greater than competitiveness. The assortativity and disassortativity of competitiveness and complementarity also coexisted in agri-trade; country pairs with high complementarity in the region increased during the study period, indicating that there are more opportunities for cooperation among countries; there are three important competitive blocks and an important complementary block in the network. One competitive block was led by China, including countries with developed basic agriculture (e.g., Egypt). The other two were composed of countries in Central East Europe and West Asia & Middle East as well as several Southeast Asian countries. Countries in the important complementary block were mainly from CEE and WA & ME in 2005, with some Southeast and South Asian countries joining by 2016.

This study provided a game model to simulate the evolution of coauthorship networks, which is a geometric hypergraph built on a circle. A fraction of nodes are randomly selected to attach an arc to express their reputation. The cooperation condition of a new node and existing nodes depends on their distance and the existing nodes’ reputation. The condition gives an expression of kin selection and network reciprocity, two typical mechanisms of cooperation. The size of a node’s reputation is expressed by the length of its arc, which is defined by a function of time and hyperdegree. The function describes the heterogeneity in the size of reputation on nodes and that in the fading speed of reputation on hyperdegrees. The model reveals that the heterogeneities can reproduce the dichotomy of node clustering and degree assortativity, as well as the trichotomy of degree and hyperdegree distributions: generalized Poisson, power-law, and exponential cutoff.

Link prediction (LP) permits to infer missing or future connections in a network. The network organization defines how information spreads through the nodes. In turn, the spreading may induce changes in the connections and speed up the network evolution. Although many LP methods have been reported in the literature, as well some methodologies to evaluate them as a classification task or ranking problem, none have systematically investigated the effects on spreading and the structural network evolution. Here, we systematic analyze LP algorithms in a framework concerning: (1) different diffusion process – Epidemics, Information, and Rumor models; (2) which LP method most improve the spreading on the network by the addition of new links; (3) the structural properties of the LP-evolved networks. From extensive numerical simulations with representative existing LP methods on different datasets, we show that spreading improve in evolved scale-free networks with lower shortest-path and structural holes. We also find that properties like triangles, modularity, assortativity, or coreness may not increase the propagation. This work contributes as an overview of LP methods and network evolution and can be used as a practical guide of LP methods selection and evaluation in terms of computational cost, spreading capacity and network structure.

Improved access to housing and recovery support is a low-cost, high-potential opportunity to help people recovering from alcohol and substance use sustain their recoveries. Oxford House (OH) recovery homes represent a recovery-favorable social environment for at least some people, but it is still unclear which resident characteristics and relational dynamics affect the social integration of residents. In the current study, OH residents in three geographic locations completed a social network instrument and self-rated their quality of life (QOL). The instruments were administered to the current (per wave) residents of 42 OHs at three time points over a period of a year. Findings indicated that those with a higher QOL were more likely to form friendships with those with a lower QOL than with their similar QOL peers, and vice versa. This finding would not have been predicted based on relationship mechanisms typical of broader social contexts, where homophily (similarity-based assortativity) is common. The self-governance model that characterizes OH residences, in which success among residents is necessarily viewed as mutually dependent and therefore mutually beneficial, seems a likely explanation for our result. Specifically, and aligned with current knowledge about what works in peer oriented recovery, our results suggest the governance mechanisms of OH favor relationships between those more stable in their recovery and those who are at a higher risk of dropout or relapse. This study reveals a potential research avenue examining an important ingredient for the effectiveness of OH.

We develop and test a rewiring method (originally proposed by Newman) which allows to build random networks having pre-assigned degree distribution and two-point correlations. For the case of scale-free degree distributions, we discretize the tail of the distribution according to the general prescription by Dorogovtsev and Mendes. The application of this method to Barabasi-Albert (BA) networks is possible thanks to recent analytical results on their correlations, and allows to compare the ensemble of random networks generated in the configuration model with that of "real" networks obtained from preferential attachment. For $\beta\ge 2$ ($\beta$ is the number of parent nodes in the preferential attachment scheme) the networks obtained with the configuration model are completely connected (giant component equal to 100%). In both generation schemes a clear disassortativity of the small degree nodes is demonstrated from the computation of the function $k_{nn}$. We also develop an efficient rewiring method which produces tunable variations of the assortativity coefficient $r$, and we use it to obtain maximally disassortative networks having the same degree distribution of BA networks with given $\beta$. Possible applications of this method concern assortative social networks., Comment: 17 pages, 5 figures

We measure the gender homophily (and other network statistics) on large-scale online book markets: amazon.com and amazon.co.uk , using datasets describing millions of books sold to readers. Large book networks are created by sales (two books are connected if many readers have bought both books) and can recommend new books to buy. The networks are analysed by the gender of their first author: is book consumption assortative by gender? Book networks are indeed gender-assortative: readers globally prefer to read from one author gender (the global assortativity coefficients by gender is around 0.4). Although 33% of first authors among all books are female, female books are not proportionally sold together with male books: an average of 20% (and median of 11%) of books co-bought with male books are female books. Instead, female books make up on average more than half of the books co-bought with other female books. The gender makeup of literary genres and structural book communities show that the gender homophily originates in a gender skew not only in certain literary genres (a fact known from prior studies), but even more strongly in certain book communities, with these book communities spanning multiple literary genres.

Previous experiments by Li and Juniper (2013) have shown that a hydrodynamically self-excited jet can synchronize with external acoustic forcing via one of two possible routes: a saddle-node (SN) bifurcation or a torus-death (TD) bifurcation. In this study, we use complex networks to analyze and forecast these two routes to synchronization in a prototypical self-excited flow – an axisymmetric low-density jet at an operating condition close to its first Hopf point. We build the complex networks using two different methods: the visibility algorithm and the recurrence condition. We find that the networks built with the visibility algorithm are high-clustering, hierarchical, and assortative in the degree of their vertices, although only the TD networks are scale free. Nevertheless, we find that the assortativity coefficient is a sufficiently sensitive indicator by which to distinguish between the SN and TD routes to synchronization and to forecast the onset of synchronization. As for the networks built with the recurrence condition, we find that their topological features differ between the two routes to synchronization, but vary predictably along either route. We quantify these variations using statistical measures such as the mean degree, spectral radius, and transitivity dimension. This study shows that complex networks can be a useful tool for distinguishing between the SN and TD routes to synchronization, and for forecasting the proximity of a system to its synchronization boundaries. These findings could open up new opportunities for complex networks to be used in the development of open-loop control strategies for hydrodynamically self-excited flows.

We consider the specification of effects of numerical actor attributes, having an interval level of measurement, in statistical models for directed social networks. A fundamental mechanism is homophily or assortativity, where actors have a higher likelihood to be tied with others having similar values of the variable under study. But there are other mechanisms that may also play a role in how the attribute values of two actors influence the likelihood of a tie between them. We discuss three additional mechanisms: aspiration, the tendency to send more ties to others having high values; attachment conformity, sending more ties to others whose values are close to the “social norm”; and sociability, where those having higher values will tend to send more ties generally. These mechanisms may operate jointly, and then their effects will be confounded. We present a specification representing these effects simultaneously by a four-parameter quadratic function of the values of sender and receiver. Flexibility can be increased by a five-parameter extension. We argue that for numerical actor attributes having important effects on directed networks, these specifications may provide an improvement. An illustration is given of dependence of advice ties on academic grades, analyzed by the Stochastic Actor-oriented Model.

To publish social graphs with differential privacy guarantees for reproducing valuable results of scientific researches, we study a workflow for graph synthesis and propose an improved approach based on weighted Privacy integrated query (wPINQ). The workflow starts with a seed graph to fit the noisy degree sequence, which essentially is the 1K-graph. In view of the inaccurate assortativity coefficient, we truncate the workflow to replace the seed graph with an optimal one by doing target 1K-rewiring while preserving the 1K-distribution. Subsequently, Markov chain Monte Carlo employs the new seed graph as the initial state, and proceeds step by step guided by the information of Triangles by intersect to increase the number of triangles in the synthetic graphs. The experimental results show that the proposed algorithm achieves better performance for the published social graphs.

Several studies have suggested that functional connectivity (FC) is constrained by the underlying structural connectivity (SC) and mutually correlated. However, not many studies have focused on differences in the network organization of SC and FC, and on how these differences may inform us about their mutual interaction. To explore this issue, we adopt a multi-layer framework, with SC and FC, constructed using Magnetic Resonance Imaging (MRI) data from the Human Connectome Project, forming a two-layer multiplex network. In particular, we examine node strength assortativity within and between the SC and FC layer. We find that, in general, SC is organized assortatively, indicating brain regions are on average connected to other brain regions with similar node strengths. On the other hand, FC shows disassortative mixing. This discrepancy is apparent also among individual resting-state networks within SC and FC. In addition, these patterns show lateralization, with disassortative mixing within FC subnetworks mainly driven from the left hemisphere. We discuss our findings in the context of robustness to structural failure, and we suggest that discordant and lateralized patterns of associativity in SC and FC may provide clues to understand laterality of some neurological dysfunctions and recovery.

Global crude oil trade is a direct reflection of global economic development and can be mathematically modeled as a network with mass trajectories of crude oil vessels between ports, as well as loading and unloading volume at ports. In this study, we build the global transport networks of crude oil between ports by using automatic identification system (AIS) data of crude oil vessels from 2009 to 2016 and design an assessment framework based on complex network indices—including centrality indexes, clustering and assortativity coefficient, and k-core decomposition—to evaluate the spatial-temporal characteristics of port-port crude oil transport networks. Compared with the original results obtained from statistical data of different countries, the port-port networks of global crude oil transport built with the AIS trajectories present a more complicated “hub-and-spoke” structure with an increasing number of hub ports and routes passing through the hub ports over time. The identified backbone networks have a significant impact on the entire crude oil transport network and have been strengthened over time. Moreover, Rotterdam, Singapore, and Antwerp ports remain the top three hub ports over time, they are also been identified the significant control capacities over the whole network.

This paper concentrates on the connections in the collaboration network and aims to explore what kinds of connections improve the joint output of the two nodes in connection, using the collaboration data of top institutions in the field of Information Science and Library Science for the period 2007–2016. More intensive connections are found between top institutions, and most institutions are connected into the largest component. The effect of international connection on performance is compared between US and non-US institutions. The homophily of centrality, tie strength and h-index measured as assortativity coefficient is described to show how institutions of similar properties tend to connect with each other in the graph. Furtherly, a negative binomial regression model is employed to investigate the relationship between the homogenous connections and the citation counts received by the connections. Characteristics of connections that contribute to good performance are then obtained.

An investor sharing game is proposed in a heterogeneous population which is divided into two kinds of groups, such as weak-power investors and strong-power investors. Economically, the degree of monopoly in the market is introduced in the game. Measuring the strategy-assortativity, the probability p decides what kind of investors’ partner in the game. We show that there are three kinds of equilibrium states in the process. In equilibrium states, all of weak-power investors cooperate while the density of strong-power cooperators depends on p . Social welfare is highest if every one cooperates. In order to promote cooperation in strong-power groups, higher strategy-assortativity is needed in a more monopolized market. Interestingly, given the degree of monopoly, when the ratio of strong-power investor’s amount to weak-power investor’s amount rises, the emergence of cooperative behaviors in strong-power groups only requires a lower p .

Many complex systems are abstractly regarded as complex networks in the study of complicated problems. The research field of information spreading in complex networks has attracted extensive interest. Many spreading strategies have been proposed for improving the spreading efficiency in complex networks. However, the strategies differ in terms of performance in various complex networks. In this paper, a hybrid and effective method for improving the spreading efficiency in small-world networks and assortative scale-free networks is proposed. The proposed method can be applied to solve the essential problem of low spreading efficiency due to spreading to small-degree vertices. The proposed method combines two strategies: 1) a set of top small-degree vertices are specified as the initial spreaders and 2) vertices preferentially spread information to large-degree neighbors. Sixty-eight groups of Monte Carlo experiments are conducted in three real complex networks and seventeen synthetic complex networks. According to the experimental results and theoretical analysis, the proposed method is efficient for improving the spreading efficiency in small-world networks and assortative scale-free networks. Moreover, in assortative scale-free networks, the improvement in the spreading efficiency that is realized via the proposed method increases with the assortativity coefficient.

Despite much theorizing, the evolutionary reasons why humans cooperate extensively with unrelated individuals are still largely unknown. While reciprocity explains many instances of non-kin cooperation, much remains to be understood. A recent suite of models based upon ‘cooperative assortativity’ suggest that non-kin cooperation can evolve if individuals preferentially assort with certain cooperative phenotypes, such as helping those who help others. Here, we test these assortative hypotheses among the Agta, a population of Filipino hunter-gatherers, using an experimental resource allocation game in which individuals divide resources between themselves and camp-mates. Individuals preferentially shared with less cooperative individuals, arguing against cooperative assortativity as a mechanism sustaining resource transfers in this population. Rather, sharing was often based on the recipient's level of need, in addition to kin-based transfers and reciprocal sharing. Contrary to several recent theoretical accounts, in this real-world setting we find no evidence for cooperative assortativity influencing patterns of cooperation. These results may reflect the demands of living in a foraging ecology characterized by high resource stochasticity, necessitating need-based sharing as a system of long-term reciprocity to mitigate repeated subsistence shortfalls.

In this study, we consider a scale-free network of nonidentical Chialvo neurons, coupled through electrical synapses. For sufficiently strong coupling, the system undergoes a transition from completely out of phase synchronized to phase synchronized state. The principal focus of this study is to investigate the effect of the degree of assortativity over the synchronization transition process. It is observed that, depending on assortativity, bistability between two asymptotically stable states allows one to develop a hysteresis loop which transforms the phase transition from second order to first order. An expansion in the area of hysteresis loop is noticeable with increasing degree-degree correlation in the network. Our study also reveals that effective frequencies of nodes simultaneously go through a continuous or sudden transition to the synchronized state with the corresponding phases. Further, we examine the robustness of the results under the effect of network size and average degree, as well as diverse frequency setup. Finally, we investigate the dynamical mechanism in the process of generating explosive synchronization. We observe a significant impact of lower degree nodes behind such phenomena: in a positive assortative network the low degree nodes delay the synchronization transition.