Structural Properties Of Social Networks Research Articles

A model-based opinion dynamics approach to tackle vaccine hesitancy

Uncovering the mechanisms underlying the diffusion of vaccine hesitancy is crucial in fighting epidemic spreading. Toward this ambitious goal, we treat vaccine hesitancy as an opinion, whose diffusion in a social group can be shaped over time by the influence of personal beliefs, social pressure, and other exogenous actions, such as pro-vaccine campaigns. We propose a simple mathematical model that, calibrated on survey data, can predict the modification of the pre-existing individual willingness to be vaccinated and estimate the fraction of a population that is expected to adhere to an immunization program. This work paves the way for enabling tools from network control towards the simulation of different intervention plans and the design of more effective targeted pro-vaccine campaigns. Compared to traditional mass media alternatives, these model-based campaigns can exploit the structural properties of social networks to provide a potentially pivotal advantage in epidemic mitigation.

AN OPTIMIZED OVERLAPPING AND DISJOINT COMMUNITY DETECTION TECHNIQUES USING IMPROVED COMMUNITY OVERLAP PROPAGATION ALGORITHM IN COMPLEX NETWORKS

The Internet and Social network has become the essential part of life which ease people for sharing information and media with friends. The formation of community structure is particularly important in network analysis practically. The community structure is one in which it merges the alike users by interacting with friends in social networks. Apart from this recognition of online communities users in social networks, there are various applications such as identifying a group of expert customers, a group of customers with shared endeavors, and a alike people’s group for marketing objectives. The structural Properties of social networks are analyzed with the help of Community detection algorithms. Enhanced Nearest Neighbor-Based Clustering (ENNC) is best among the community detection algorithms in which modularity based community detection is achieved in the prevailing technique. Although there exist some hindrances of overlapping communities due to the probability of user joining in more than a group is high. Hence there requires a detecting overlapping communities while analyzing realistic network. This research concentrates on Optimized Overlapping and disjoint Community Detection (OODCD) technique by adopting a by Improved Vertex Imitation Co-efficient based Community Overlap Propagation Algorithm (IVIC-COPRA).In this, the Enhanced Nearest Neighbor-Based Clustering (ENNC) approach is utilized basically which is modularity based approach for partitioning the network into minor local communities. The Louvain method is then used for detecting the disjoint community in the given network. The belonging matrix plays a vital in this research which is regularly updated whose matrix elemental value decides the role of node belonging to a community and thus the overlapping communities is found with the support of Improved Vertex Imitation Co-efficient Community Overlap Propagation Algorithm (IVIC-COPRA).The Performance of the anticipated research is examined and contrasted with the numerous eminent other overlapping community detection algorithms by means of simulation.

Homophily as a Process Generating Social Networks: Insights from Social Distance Attachment Model

Real-world social networks often exhibit high levels of clustering, positive degree assortativity, short average path lengths (small-world property) and right-skewed but rarely power law degree distributions. On the other hand homophily, defined as the propensity of similar agents to connect to each other, is one of the most fundamental social processes observed in many human and animal societies. In this paper we examine the extent to which homophily is sufficient to produce the typical structural properties of social networks. To do so, we conduct a simulation study based on the Social Distance Attachment (SDA) model, a particular kind of Random Geometric Graph (RGG), in which nodes are embedded in a social space and connection probabilities depend functionally on distances between nodes. We derive the form of the model from first principles based on existing analytical results and argue that the mathematical construction of RGGs corresponds directly to the homophily principle, so they provide a good model for it. We find that homophily, especially when combined with a random edge rewiring, is sufficient to reproduce many of the characteristic features of social networks. Additionally, we devise a hybrid model combining SDA with the configuration model that allows generating homophilic networks with arbitrary degree sequences and we use it to study interactions of homophily with processes imposing constraints on degree distributions. We show that the effects of homophily on clustering are robust with respect to distribution constraints, while degree assortativity can be highly dependent on the particular kind of enforced degree sequence.

Interaction Models for Detecting Nodal Activities in Temporal Social Media Networks

Detecting nodal activities in dynamic social networks has strategic importance in many applications, such as online marketing campaigns and homeland security surveillance. How peer-to-peer exchanges in social media can facilitate nodal activity detection is not well explored. Existing models assume network nodes to be static in time and do not adequately consider features from social theories. This research developed and validated two theory-based models, Random Interaction Model (RIM) and Preferential Interaction Model (PIM), to characterize temporal nodal activities in social media networks of human agents. The models capture the network characteristics of randomness and preferential interaction due to community size, human bias, declining connection cost, and rising reachability. The models were compared against three benchmark models (abbreviated as EAM, TAM, and DBMM) using a social media community consisting of 790,462 users who posted over 3,286,473 tweets and formed more than 3,055,797 links during 2013–2015. The experimental results show that both RIM and PIM outperformed EAM and TAM significantly in accuracy across different dates and time windows. Both PIM and RIM scored significantly smaller errors than DBMM did. Structural properties of social networks were found to provide a simple and yet accurate approach to predicting model performances. These results indicate the models’ strong capability of accounting for user interactions in real-world social media networks and temporal activity detection. The research should provide new approaches for temporal network activity detection, develop relevant new measures, and report new findings from large social media datasets.

Homophily and minority-group size explain perception biases in social networks.

People's perceptions about the size of minority groups in social networks can be biased, often showing systematic over- or underestimation. These social perception biases are often attributed to biased cognitive or motivational processes. Here we show that both over- and underestimation of the size of a minority group can emerge solely from structural properties of social networks. Using a generative network model, we show that these biases depend on the level of homophily, its asymmetric nature and on the size of the minority group. Our model predictions correspond well with empirical data from a cross-cultural survey and with numerical calculations from six real-world networks. We also identify circumstances under which individuals can reduce their biases by relying on perceptions of their neighbours. This work advances our understanding of the impact of network structure on social perception biases and offers a quantitative approach for addressing related issues in society.

Hygiene and Health: Who Do Mothers in Vanuatu Communicate with about Health?

Health information-seeking behaviour of mothers with children five years of age and younger in Vanuatu was examined using the structural properties of social networks. Data were collected from a rural village from two islands and an urban settlement in the capital, Port Vila, by face-to-face interviews using a structured questionnaire. Sociometric data on the structure of the network, the characteristics of key informants, and associations with outside sources of health information were analysed as interpersonal predictors of health promotion and behavior change. Rural mothers preferred the health advice of biomedical practitioners for diarrheal disease over traditional custom practitioners. Interpersonal connections were restricted in the urban mother network indicating that mothers were merely acquaintances or do not seek health advice from each other. Our findings suggest that biomedical practitioners are the best option for diffusing health and hygiene information for rural and urban mothers. Traditional healers and paraprofessionals could be strategically used to complete the missing links in network connectedness to optimally spread new information. The novel use of cross-sectional social network data can create a baseline evaluation to purposefully frame a health intervention. Our study provided a unique explanation of how network analysis offers insight into how key players can be identified and the circumstances in which they are likely to be able to influence hygiene practices of their peers.

NEW SURVEY QUESTIONS AND ESTIMATORS FOR NETWORK CLUSTERING WITH RESPONDENT-DRIVEN SAMPLING DATA.

Respondent-driven sampling (RDS) is a popular method for sampling hard-to-survey populations that leverages social network connections through peer recruitment. While RDS is most frequently applied to estimate the prevalence of infections and risk behaviors of interest to public health, such as HIV/AIDS or condom use, it is rarely used to draw inferences about the structural properties of social networks among such populations because it does not typically collect the necessary data. Drawing on recent advances in computer science, we introduce a set of data collection instruments and RDS estimators for network clustering, an important topological property that has been linked to a network's potential for diffusion of information, disease, and health behaviors. We use simulations to explore how these estimators, originally developed for random walk samples of computer networks, perform when applied to RDS samples with characteristics encountered in realistic field settings that depart from random walks. In particular, we explore the effects of multiple seeds, without replacement versus with replacement, branching chains, imperfect response rates, preferential recruitment, and misreporting of ties. We find that clustering coefficient estimators retain desirable properties in RDS samples. This paper takes an important step toward calculating network characteristics using nontraditional sampling methods, and it expands the potential of RDS to tell researchers more about hidden populations and the social factors driving disease prevalence.

Impact of human mobility on social networks

Mobile phone carriers face challenges from three synergistic dimensions: Wireless, social, and mobile. Despite significant advances that have been made about social networks and human mobility, respectively, our knowledge about the interplay between two layers remains largely limited, partly due to the difficulty in obtaining large-scale datasets that could offer at the same time social and mobile information across a substantial population over an extended period of time. In this paper, we take advantage of a massive, longitudinal mobile phone dataset that consists of human mobility and social network information simultaneously, allowing us to explore the impact of human mobility patterns on the underlying social network. We find that human mobility plays an important role in shaping both local and global structural properties of social network. In contrast to the lack of scale in social networks and human movements, we discovered a characteristic distance in physical space between 10 and 20 km that impacts both local clustering and modular structure in social network. We also find a surprising distinction in trajectory overlap that segments social ties into two categories. Our results are of fundamental relevance to quantitative studies of human behavior, and could serve as the basis of anchoring potential theoretical models of human behavior and building and developing new applications using social and mobile technologies.

Analysis of Opinion Evolution in a Multi-cultural Student Social Network

The spread of opinions in social networks is dependent on structural properties of the network and the individual characteristics of its nodes. To capture this dependence, several abstract models of such spread were proposed. First, we model the difference between the dynamics of opinion spread in communities with a static social network versus a dynamic social network. Here we use the theoretical model of spread of opinions called the Binary Agreement Model based on the naming game. Using this model, we study potential mechanisms for the dependencies observed in the data by matching model generated evolution of opinion with the empirically observed evolution in the data. Second, we examine the unique set of behavioral network data (based on electronic logs of dyadic contact via smartphones) collected at the University of Notre Dame. The participants are a sample of members of the entering class of freshmen in the fall of 2011 whose opinions on a wide variety of political and social issues have been regularly recorded—at the beginning and end of each semester—for the last three years. Using this data set, we measure the evolution of participants’ opinions and ascertain how much this evolution depends on the cultural traits of individuals and the structural properties of social networks that they form. Our analysis of our empirical dataset shows that ties among people who are more likely to share opinions (e.g. same race, gender, or socioeconomic class) decay at a slower rate than ties among persons who are likely to have different opinions. The analysis also indicates that the partner selection of individuals is associated sharing a (political) opinion. These results offer an assessment of the level of impact of culture and social network dynamics on the evolution of opinions in multi-cultural social networks.

Evaluating the benefits of collaboration in simulation games: the case of health care.

BackgroundOrganizations have used simulation games for health promotion and communication. To evaluate how simulation games can foster collaboration among stakeholders, this paper develops two social network measures.ObjectiveThe paper aims to initiate two specific measures that facilitate organizations and researchers to evaluate the effectiveness of Web-based simulation games in fostering collaboration.MethodsThe two measures are: (1) network density and (2) network diversity. They measure the level of connectedness and communication evenness within social networks. To illustrate how these measures may be used, a hypothetical game about health policy is outlined.ResultsWeb-based games can serve as an effective platform to engage stakeholders because interaction among them is quite convenient. Yet, systematic evaluation and planning are necessary to realize the benefits of these games. The paper suggests directions for testing how the social network dimension of Web-based games can augment individual-level benefits that stakeholders can obtain from playing simulation games.ConclusionsWhile this paper focuses on measuring the structural properties of social networks in Web-based games, further research should focus more attention on the appropriateness of game contents. In addition, empirical research should cover different geographical areas, such as East Asian countries where video games are very popular.

Dyad and Triad Census Analysis of Crisis Communication Network

Dyad and triad census summarize much of the network level structural information of a given directed network. They have been found very useful in analyzing structural properties of social networks. This study aims to explore crisis communication network by following dyad and triad census analysis approach to investigate the association of microlevel communication patterns with organizational crisis. This study further tests hypothesis related to the process of data generation and tendency of the structural pattern of transitivity using dyad and triad census output. The changing communication network at Enron Corporation during the period of its crisis is analyzed in this study. Significant differences in the presence of different isomorphism classes or microlevel patterns of both dyad and triad census are noticed in crisis and non-crisis period network of Enron email corpus. It is also noticed that crisis communication network shows more transitivity compared to the non-crisis communication network.

Why Can Collective Action Fail in Local Agri-Food Systems? A Social Network Analysis of Cheese Producers in Aculco, Mexico

Quality standards in food-processing industries have become a growing challenge for farmers in developing countries. This challenge is the target of collective action-based programs aiming at framing the cooperation context and the rules system that would enable a local community to turn inherited resources and know-how into new market opportunities. Literature acknowledges that social capital is a crucial local resource for successful collective actions since it is a source of trust and collective commitment within a community. The paper deals with testing the role of this resource in the collective action case of the Aculco cheese producers (Mexico). Focusing on the connectedness dimension of social capital, we propose an original methodology that relies on the multiplex and structural properties of social networks. This allows us to disentangle the opposite and ambivalent effects of social capital on the ability of a community to reach the goals assigned by the collective action program. Our qualitative and quantitative empirical study shows that these critical properties can work in favor of the adherence to collective action but can also create exclusiveness, clannish behaviors and entry barriers, and thus a failure of collective action

Identifying communities in blogs: roles for social network analysis and survey instruments

We examine the problem of identifying, measuring and evaluating communities in blogs. In our approach, we use sense of community (McMillan and Chavis, 1986) and centrality measures derived from social network analysis. We then describe a method for identifying communities in blogs using both sense of community measurement and Social Network Analysis (SNA). We apply this framework to a blog on Canadian independent music. Hypotheses are developed concerning the specific structural properties of social networks formed by blogging relationships and how those properties are related to the sense of community experienced by the participants (authors and readers of the blogs). Finally, we discuss the implications for blog implementation and use.

New Specifications for Exponential Random Graph Models

The most promising class of statistical models for expressing structural properties of social networks observed at one moment in time is the class of exponential random graph models (ERGMs), also known as p* models. The strong point of these models is that they can represent a variety of structural tendencies, such as transitivity, that define complicated dependence patterns not easily modeled by more basic probability models. Recently, Markov chain Monte Carlo (MCMC) algorithms have been developed that produce approximate maximum likelihood estimators. Applying these models in their traditional specification to observed network data often has led to problems, however, which can be traced back to the fact that important parts of the parameter space correspond to nearly degenerate distributions, which may lead to convergence problems of estimation algorithms, and a poor fit to empirical data. This paper proposes new specifications of exponential random graph models. These specifications represent structural properties such as transitivity and heterogeneity of degrees by more complicated graph statistics than the traditional star and triangle counts. Three kinds of statistics are proposed: geometrically weighted degree distributions, alternating k-triangles, and alternating independent two-paths. Examples are presented both of modeling graphs and digraphs, in which the new specifications lead to much better results than the earlier existing specifications of the ERGM. It is concluded that the new specifications increase the range and applicability of the ERGM as a tool for the statistical analysis of social networks.

Comparing social networks

This paper demonstrates limitations in usefulness of the triad census for studying similarities among local structural properties of social networks. A triad census succinctly summarizes the local structure of a network using the frequencies of sixteen isomorphism classes of triads (sub-graphs of three nodes). The empirical base for this study is a collection of 51 social networks measuring different relational contents (friendship, advice, agonistic encounters, victories in fights, dominance relations, and so on) among a variety of species (humans, chimpanzees, hyenas, monkeys, ponies, cows, and a number of bird species). Results show that, in aggregate, similarities among triad censuses of these empirical networks are largely explained by nodal and dyadic properties – the density of the network and distributions of mutual, asymmetric, and null dyads. These results remind us that the range of possible network-level properties is highly constrained by the size and density of the network and caution should be taken in interpreting higher order structural properties when they are largely explained by local network features.

Effects of missing data in social networks

We perform sensitivity analyses to assess the impact of missing data on the structural properties of social networks. The social network is conceived of as being generated by a bipartite graph, in which actors are linked together via multiple interaction contexts or affiliations. We discuss three principal missing data mechanisms: network boundary specification (non-inclusion of actors or affiliations), survey non-response, and censoring by vertex degree (fixed choice design), examining their impact on the scientific collaboration network from the Los Alamos E-print Archive as well as random bipartite graphs. The simulation results show that network boundary specification and fixed choice designs can dramatically alter estimates of network-level statistics. The observed clustering and assortativity coefficients are overestimated via omission of affiliations or fixed choice thereof, and underestimated via actor non-response, which results in inflated measurement error. We also find that social networks with multiple interaction contexts may have certain interesting properties due to the presence of overlapping cliques. In particular, assortativity by degree does not necessarily improve network robustness to random omission of nodes as predicted by current theory.

Social Networks and Interorganizational Trust

Much attention is drawn to an argument that social networks may influence development and performance of transactions and cooperation among firms, as many interfirm networks such as network organizations and strategic alliances recently show higher performance in market competition. Many scholars in economics, organizational theory and sociology argue that interorganizational trust is one of the critical conditions that can socially constrain opportunism in interfirm relations. The embeddedness approach from the new economic sociology reveals that social networks among organizations may facilitate the development of interorganizational trust with special relational and structural conditions such as high reciprocity and distributions of reputation. The aim of this paper is to review how the embeddedness approach explains the mechanism in which the special properties of social networks facilitate the development and the decline of interorganizational trust, by using the common concepts from the social exchange theory in, and to show that special relational and structural properties of social networks may facilitate its development.The embeddedness approach focuses on the structural effects of social networks among organizations on interorganizational trust in relational and structural dimensions, which are also known as “relational” and “structural embeddedness.” First, if social networks among organizations exhibit strong cohesion, they also have high reciprocity which is likely to facilitate firms to make a long-term commitment to their cooperation and “relational trust.” Second, if social networks have high density or many third-party mediations, they enjoy a great distribution of reputation that may encourage firms to share similar expectations for other companies, thus developing a “system-level trust.” Therefore, the embeddedness approach can explain how social networks affect the governance of economic exchange among firms through the development and the decline of interorganizational trust.

Asymptotic distributions in random graphs with applications to social networks

Various kinds of subgraph counts have been proposed as important statistics in the social sciences: for instance, in connection with studies of the structural properties of social networks. Since the empirical structure in question often involves an element of randomness, subgraph counts are random variables and, consequently, we need to describe their probabilistic properties. In this paper we give a survey of results dealing with the asymptotic distributions of general subgraph counts for a number of standard graph distributions. Although we do not include proofs for all the results, we illustrate the methodology used through studies of asymptotic behaviour for certain subgraph counts.