The Niakhar Social Networks and Health Project.

This paper presents details of the design and implementation of the Niakhar Social Networks and Health Project (NSNHP), a large, mixed-methods project funded by the U.S. National Institute of General Medical Sciences (NIGMS). By redressing fundamental problems in conventional survey network data collection methods, the project is aimed at improving inferences concerning the association between social network structures and processes and health behaviors and outcomes. Fielded in collaboration with an ongoing demographic and health surveillance system in rural Senegal, the NSNHP includes qualitative data concerning the dimensions of social association and health ideologies and behaviors in the study zone, two panels of a new social network survey, and several supplementary and affiliated data sets. •Longitudinal social network survey linked to pre-existing surveillance data•Addresses fundamental methodological constraints in previous social network data•Enables social network analyses of health beliefs, behaviors, and outcomes.

Evolution of cooperation on large networks with community structure.

Cooperation is a major factor in the evolution of human societies. The structure of social networks, which affects the dynamics of cooperation and other interpersonal phenomena, have common structural signatures. One of these signatures is the tendency to organize as groups. This tendency gives rise to networks with community structure, which are composed of distinct modules. In this paper, we study analytically the evolutionary game dynamics on large modular networks in the limit of weak selection. We obtain novel analytical conditions such that natural selection favours cooperation over defection. We calculate the transition point for each community to favour cooperation. We find that a critical inter-community link creation probability exists for given group density, such that the overall network supports cooperation even if individual communities inhibit it. As a byproduct, we present solutions for the critical benefit-to-cost ratio which perform with remarkable accuracy for diverse generative network models, including those with community structure and heavy-tailed degree distributions. We also demonstrate the generalizability of the results to arbitrary two-player games.

Social learning, influence, and ethnomedicine: Individual, neighborhood and social network influences on attachment to an ethnomedical cultural model in rural Senegal.

The preference in many parts of the world for ethnomedical therapy over biomedical alternatives has long confounded scholars of medicine and public health. In the anthropological literature cultural and interactional contexts have been identified as fundamental mechanisms shaping adherence to ethnomedical beliefs and health seeking behaviors. In this paper, we examine the association between individual, neighborhood, and social network characteristics and the likelihood of attachment to an ethnomedical cultural model encompassing beliefs about etiology of disease, appropriate therapeutic and preventative measures, and more general beliefs about metaphysics and the efficacy of health systems in a rural population in Eastern Senegal. Using data from a unique social network survey, and supplemented by extensive qualitative research, we model attachment to the ethnomedical model at each of these levels as a function of demographic, economic and ideational characteristics, as well as perceived effectiveness of both biomedical and ethnomedical therapy. Individuals' attachment to the ethnomedical cultural model is found to be strongly associated with characteristics of their neighborhoods, and network alters. Experiences with ethnomedical care among neighbors, and both ethnomedical and biomedical care among network alters, are independently associated with attachment to the ethnomedical model, suggesting an important mechanism for cultural change. At the same time, we identify an independent association between network alters' cultural models and those of respondents, indicative of a direct cultural learning or influence mechanism, modified by the degree of global transitivity, or 'connectedness' of individuals' networks. This evidence supports the long held theoretical position that symbolic systems concerning illness and disease are shared, reproduced, and changed through mechanisms associated with social interaction. This has potentially important implications not only for public health programming, but for the understanding of the reproduction and evolution of cultural systems more generally.

Statistical methods for constructing disease comorbidity networks from longitudinal inpatient data.

Tools from network science can be utilized to study relations between diseases. Different studies focus on different types of inter-disease linkages. One of them is the comorbidity patterns derived from large-scale longitudinal data of hospital discharge records. Researchers seek to describe comorbidity relations as a network to characterize pathways of disease progressions and to predict future risks. The first step in such studies is the construction of the network itself, which subsequent analyses rest upon. There are different ways to build such a network. In this paper, we provide an overview of several existing statistical approaches in network science applicable to weighted directed networks. We discuss the differences between the null models that these models assume and their applications. We apply these methods to the inpatient data of approximately one million people, spanning approximately 17 years, pertaining to the Montreal Census Metropolitan Area. We discuss the differences in the structure of the networks built by different methods, and different features of the comorbidity relations that they extract. We also present several example applications of these methods.

Conjoining uncooperative societies facilitates evolution of cooperation.

Social structure affects the emergence and maintenance of cooperation. Here, we study the evolutionary dynamics of cooperation in fragmented societies, and show that conjoining segregated cooperation-inhibiting groups, if done properly, rescues the fate of collective cooperation. We highlight the essential role of intergroup ties, which sew the patches of the social network together and facilitate cooperation. We point out several examples of this phenomenon in actual settings. We explore random and non-random graphs, as well as empirical networks. In many cases, we find a marked reduction of the critical benefit-to-cost ratio needed for sustaining cooperation. Our finding gives hope that the increasing worldwide connectivity, if managed properly, can promote global cooperation.

Effect of node attributes on the temporal dynamics of network structure.

Many natural and social networks evolve in time and their structures are dynamic. In most networks, nodes are heterogeneous, and their roles in the evolution of structure differ. This paper focuses on the role of individual attributes on the temporal dynamics of network structure. We focus on a basic model for growing networks that incorporates node attributes (which we call "quality"), and we focus on the problem of forecasting the structural properties of the network in arbitrary times for an arbitrary initial network. That is, we address the following question: If we are given a certain initial network with given arbitrary structure and known node attributes, then how does the structure change in time as new nodes with given distribution of attributes join the network? We solve the model analytically and obtain the quality-degree joint distribution and degree correlations. We characterize the role of individual attributes in the position of individual nodes in the hierarchy of connections. We confirm the theoretical findings with Monte Carlo simulations.

Evolutionary dynamics on any population structure.

Evolution occurs in populations of reproducing individuals. The structure of a population can affect which traits evolve. Understanding evolutionary game dynamics in structured populations remains difficult. Mathematical results are known for special structures in which all individuals have the same number of neighbours. The general case, in which the number of neighbours can vary, has remained open. For arbitrary selection intensity, the problem is in a computational complexity class that suggests there is no efficient algorithm. Whether a simple solution for weak selection exists has remained unanswered. Here we provide a solution for weak selection that applies to any graph or network. Our method relies on calculating the coalescence times of random walks. We evaluate large numbers of diverse population structures for their propensity to favour cooperation. We study how small changes in population structure-graph surgery-affect evolutionary outcomes. We find that cooperation flourishes most in societies that are based on strong pairwise ties.

Temporal dynamics of connectivity and epidemic properties of growing networks.

Traditional mathematical models of epidemic disease had for decades conventionally considered static structure for contacts. Recently, an upsurge of theoretical inquiry has strived towards rendering the models more realistic by incorporating the temporal aspects of networks of contacts, societal and online, that are of interest in the study of epidemics (and other similar diffusion processes). However, temporal dynamics have predominantly focused on link fluctuations and nodal activities, and less attention has been paid to the growth of the underlying network. Many real networks grow: Online networks are evidently in constant growth, and societal networks can grow due to migration flux and reproduction. The effect of network growth on the epidemic properties of networks is hitherto unknown, mainly due to the predominant focus of the network growth literature on the so-called steady state. This paper takes a step towards alleviating this gap. We analytically study the degree dynamics of a given arbitrary network that is subject to growth. We use the theoretical findings to predict the epidemic properties of the network as a function of time. We observe that the introduction of new individuals into the network can enhance or diminish its resilience against endemic outbreaks and investigate how this regime shift depends upon the connectivity of newcomers and on how they establish connections to existing nodes. Throughout, theoretical findings are corroborated with Monte Carlo simulations over synthetic and real networks. The results shed light on the effects of network growth on the future epidemic properties of networks and offers insights for devising a priori immunization strategies.

Growing multiplex networks with arbitrary number of layers.

This paper focuses on the problem of growing multiplex networks. Currently, the results on the joint degree distribution of growing multiplex networks present in the literature pertain to the case of two layers and are confined to the special case of homogeneous growth and are limited to the state state (that is, the limit of infinite size). In the present paper, we first obtain closed-form solutions for the joint degree distribution of heterogeneously growing multiplex networks with arbitrary number of layers in the steady state. Heterogeneous growth means that each incoming node establishes different numbers of links in different layers. We consider both uniform and preferential growth. We then extend the analysis of the uniform growth mechanism to arbitrary times. We obtain a closed-form solution for the time-dependent joint degree distribution of a growing multiplex network with arbitrary initial conditions. Throughout, theoretical findings are corroborated with Monte Carlo simulations. The results shed light on the effects of the initial network on the transient dynamics of growing multiplex networks and takes a step towards characterizing the temporal variations of the connectivity of growing multiplex networks, as well as predicting their future structural properties.

Dynamics of influence on hierarchical structures.

Dichotomous spin dynamics on a pyramidal hierarchical structure (the Bethe lattice) are studied. The system embodies a number of classes, where a class comprises nodes that are equidistant from the root (head node). Weighted links exist between nodes from the same and different classes. The spin (hereafter state) of the head node is fixed. We solve for the dynamics of the system for different boundary conditions. We find necessary conditions so that the classes eventually repudiate or acquiesce in the state imposed by the head node. The results indicate that to reach unanimity across the hierarchy, it suffices that the bottommost class adopts the same state as the head node. Then the rest of the hierarchy will inevitably comply. This also sheds light on the importance of mass media as a means of synchronization between the topmost and bottommost classes. Surprisingly, in the case of discord between the head node and the bottommost classes, the average state over all nodes inclines towards that of the bottommost class regardless of the link weights and intraclass configurations. Hence the role of the bottommost class is signified.

Network growth with arbitrary initial conditions: degree dynamics for uniform and preferential attachment.

This paper provides time-dependent expressions for the expected degree distribution of a given network that is subject to growth. We consider both uniform attachment, where incoming nodes form links to existing nodes selected uniformly at random, and preferential attachment, where probabilities are assigned proportional to the degrees of the existing nodes. We consider the cases of single and multiple links being formed by each newly introduced node. The initial conditions are arbitrary, that is, the solution depends on the degree distribution of the initial graph which is the substrate of the growth. Previous work in the literature focuses on the asymptotic state, that is, when the number of nodes added to the initial graph tends to infinity, rendering the effect of the initial graph negligible. Our contribution provides a solution for the expected degree distribution as a function of time, for arbitrary initial condition. Previous results match our results in the asymptotic limit. The results are discrete in the degree domain and continuous in the time domain, where the addition of new nodes to the graph are approximated by a continuous arrival rate.