A comparative study of social network models: Network evolution models and nodal attribute models

This paper reviews, classifies and compares recent models for social networks that have mainly been published within the physics-oriented complex networks literature. The models fall into two categories: those in which the addition of new links is dependent on the (typically local) network structure (network evolution models, NEMs), and those in which links are generated based only on nodal attributes (nodal attribute models, NAMs). An exponential random graph model (ERGM) with structural dependencies is included for comparison. We fit models from each of these categories to two empirical acquaintance networks with respect to basic network properties. We compare higher order structures in the resulting networks with those in the data, with the aim of determining which models produce the most realistic network structure with respect to degree distributions, assortativity, clustering spectra, geodesic path distributions, and community structure (subgroups with dense internal connections). We find that the nodal attribute models successfully produce assortative networks and very clear community structure. However, they generate unrealistic clustering spectra and peaked degree distributions that do not match empirical data on large social networks. On the other hand, many of the network evolution models produce degree distributions and clustering spectra that agree more closely with data. They also generate assortative networks and community structure, although often not to the same extent as in the data. The ERGM model, which turned out to be near-degenerate in the parameter region best fitting our data, produces the weakest community structure.     

The structure and vulnerability of a drug trafficking collaboration network

Prior research has established that collaboration networks exhibit heavy-tailed degree distributions, assortative degree mixing, and large clustering coefficients. Using court record data, we assess these properties in a collaboration network among heroin traffickers. Consistent with prior research, we find an exponential degree distribution and strong local clustering. However, the traffickers mix dissortatively by degree rather than assortatively. Using a graph sampling method, we show that a consequence of dissortative mixing is that targeted vertex removals have a greater impact on the connectivity and cohesion of the trafficking network. We also note the importance of degree mixing for characterizing and identifying topological weaknesses.     

Advances in Exponential Random Graph (p*) Models Applied to a Large Social Network

Recent advances in statistical network analysis based on the family of exponential random graph (ERG) models have greatly improved our ability to conduct inference on dependence in large social networks (Snijders 2002, Pattison and Robins 2002, Handcock 2002, Handcock 2003, Snijders et al. 2006, Hunter et al. 2005, Goodreau et al. 2005, previous papers this issue). This paper applies advances in both model parameterizations and computational algorithms to an examination of the structure observed in an adolescent friendship network of 1,681 actors from the National Longitudinal Study of Adolescent Health (AddHealth). ERG models of social network structure are fit using the R package statnet, and their adequacy assessed through comparison of model predictions with the observed data for higher-order network statistics.For this friendship network, the commonly used model of Markov dependence leads to the problems of degeneracy discussed by Handcock (2002, 2003). On the other hand, model parameterizations introduced by Snijders et al (2006) and Hunter and Handcock (2006) avoid degeneracy and provide reasonable fit to the data. Degree-only models did a poor job of capturing observed network structure; those that did best included terms both for heterogeneous mixing on exogenous attributes (grade and self-reported race) as well as endogenous clustering. Networks simulated from this model were largely consistent with the observed network on multiple higher-order network statistics, including the number of triangles, the size of the largest component, the overall reachability, the distribution of geodesic distances, the degree distribution, and the shared partner distribution. The ability to fit such models to large datasets and to make inference about the underling processes generating the network represents a major advance in the field of statistical network analysis.     

Use of the Internet for Community Practice

ABSTRACT

The Internet is a relatively new community practice tool with immense potential for more effective and efficient delivery of services. The lack of research about Internet community practice is an obstacle to the effective and efficient use of Internet tools. This research used the Delphi technique to elicit the opinions of experts on how the Internet tools of email, newsgroups, text, listservs, search engines, and chat are used by the four primary models of community practice found on the Internet (information dissemination, organization building, mobilization, and community planning). Findings show that a substantial number of people use community practice sites. The most useful Internet tools for the four Internet community practice models studied were text presentation and email. The least useful tools were newsgroups and chat. Barriers to Internet community practice include quality and currency of information, bandwidth, staff time, agency support, rapid technological change, marketing, browser consistency, user skills, and the lack of Internet access by potential users. The study concluded that Internet community practice is effective and will continue to grow as society enters the information age. The findings are particularly relevant for community practice agencies interested in setting up or enhancing an agency Web site to help provide services.     

Universal evolution patterns of degree assortativity in social networks

Degree assortativity characterizes the propensity for large-degree nodes to connect to other large-degree nodes and low-degree to low-degree. It is important to describe the forces forming the network and to predict the behavior of dynamic systems on the network. To understand the evolutionary dynamics of degree assortativity, we collect a variety of empirical temporal social networks, and find that there is a universal pattern that the degree assortativity increases at the beginning of evolution and then decreases to a long-lasting stable level. We develop a bidirectional selection model to re-construct the evolution dynamic. In our model, we assume each individual has a social status that—in analogy to Pareto’s wealth distribution —follows a power-law distribution. We assume the social status determines the probability of an interaction between two actors. By varying the ratio of link establishment from within the same status level to across different status levels, the simulated network can be tuned to be assortative or disassortative. This suggests that the rise-and-fall pattern of degree assortativity is a consequence of the different network-forming forces active at different mixing of status. Our simulations indicate that Pareto social status distribution in the population may drive the social evolution in a way of self-optimization to promote the social interaction among individuals and the status gap plays an important role for the assortativity of the social network.     

Measurement error in network data: A re-classification

Research on measurement error in network data has typically focused on missing data. We embed missing data, which we term false negative nodes and edges, in a broader classification of error scenarios. This includes false positive nodes and edges and falsely aggregated and disaggregated nodes. We simulate these six measurement errors using an online social network and a publication citation network, reporting their effects on four node-level measures – degree centrality, clustering coefficient, network constraint, and eigenvector centrality. Our results suggest that in networks with more positively-skewed degree distributions and higher average clustering, these measures tend to be less resistant to most forms of measurement error. In addition, we argue that the sensitivity of a given measure to an error scenario depends on the idiosyncracies of the measure's calculation, thus revising the general claim from past research that the more ‘global’ a measure, the less resistant it is to measurement error. Finally, we anchor our discussion to commonly-used networks in past research that suffer from these different forms of measurement error and make recommendations for correction strategies.     

Neighborhoods in the Network Society the e-Neighbors study

This study examines whether the Internet is increasingly a part of everyday neighborhood interactions, and in what specific contexts Internet use affords the formation of local social ties. Studies of Internet and community have found that information and communication technologies provide new opportunities for social interaction, but that they may also increase privatism by isolating people in their homes. This paper argues that while the Internet may encourage communication across great distances, it may also facilitate interactions near the home. Unlike traditional community networking studies, which focus on bridging the digital divide, this study focuses on bridging the divide between the electronic and parochial realms. Detailed, longitudinal social network surveys were completed with the residents of four contrasting neighborhoods over a period of three years. Three of the four neighborhoods were provided with a neighborhood email discussion list and a neighborhood website. Hierarchical linear modeling (HLM) was used to model over time the number of strong and weak ties, emailed, met in-person, and talked to on the telephone. The neighborhood email lists were also analyzed for content. The results suggest that with experience using the Internet, the size of local social networks and email communication with local networks increases. The addition of a neighborhood email list further increases the number of weak neighborhood ties, but does not increase communication multiplexity. However, neighborhood effects reduce the influence of everyday Internet use, as well as the experimental intervention, in communities that lack the context to support local tie formation.     

Spine segments in small world networks

Investigations of small world contact networks, defined as networks with a short characteristic path length and a substantial local clustering of contacts in the neighborhood of each node, have emphasized the process performance of such networks. The argument that large-scale, small world, contact networks are structures with startlingly efficient process performance is premised on the existence of shortcuts, without which the characteristic path lengths of the networks would be substantially larger. No doubt, given a high probability of transmission in each contact of a network, such shortcuts are a potential structural basis of reliable flows of information, influence, material and disease. However, interpersonal contacts are often markedly unreliable transmission conduits, and the average shortcut contact may be a more unreliable, episodic, transmission conduit than the average contact of cliques. With markedly unreliable contacts, fundamental helix substructures, that are parallel-transmission subsystems of the contact network, importantly enter into the analysis of network performance. These substructures of disjoint path redundancies are based on the local clustering of contacts in the neighborhoods of each node. Drawing on network reliability theory, this article presents an approach in which intersecting cliques of contact networks are a theoretically important construct in the specification of the transmission implications of observed contact networks. Clique intersections are a structural basis of path redundancies that enable reliable transmission among the nodes of contact networks consisting of contacts that may or may not be active conduits of transmissions during some period of time. The strong contacts that occur among clique members further enhance the contributions of these path redundancies.     

Dynamic social media affiliations among UK politicians

Inter-personal affiliations and coalitions are an important part of politicians’ behaviour, but are often difficult to observe. Since an increasing amount of political communication now occurs online, data from online interactions may offer a new toolkit to study ties between politicians; however, the methods by which robust insights can be derived from online data require further development, especially around the dynamics of political social networks. We develop a novel method for tracking the evolution of community structures, referred to as ‘multiplex community affiliation clustering’ (MCAC), and use it to study the online social networks of Members of Parliament (MPs) and Members of the European Parliament (MEPs) in the United Kingdom. Social interaction networks are derived from social media (Twitter) communication over an eventful 17-month period spanning the UK General Election in 2015 and the UK Referendum on membership of the European Union in 2016. We find that the social network structure linking MPs and MEPs evolves over time, with distinct communities forming and re-forming, driven by party affiliations and political events. Without including any information about time in our model, we nevertheless find that the evolving social network structure shows multiple persistent and recurring states of affiliation between politicians, which align with content states derived from topic analysis of tweet text. These findings show that the dominant state of partisan segregation can be challenged by major political events, ideology, and intra-party tension that transcend party affiliations.     

For Whom the Web Toils: How Internet Experience Predicts Web Reliance and Credibility

This study employs an online survey of politically interested Internet users to examine the degree to which Internet experience predicts reliance on the World Wide Web (Web) and Web credibility. Respondents in this study represent an experienced group of Internet users. The average user had been online 6.2 years. However, they regularly engaged in only 9.4 of 22 possible online activities, suggesting they regularly visit a core group of sites. Although years online and number of online activities emerged as distinct measures of Internet experience, neither strongly predicted Web reliance or Web credibility. Neither Internet experience variable predicted any of the six measures of Internet credibility. Number of activities did not influence Web reliance and years online was negatively related, indicating that those who had been online for a shorter time were more likely to rely on the Web than veteran users.     

The Link between Internet Activity and Community Experience in Rural Utah☆

The growth of high-speed Internet access in rural communities is a relatively recent event. In this exploratory study, we contribute to the literature regarding the Internet and local community by analyzing the influence of Internet activity on community experience, measured through community satisfaction and attachment, using the systemic model as controls. After surveying 24 rural communities in Utah, USA once in 2008 and again in 2017 with a cumulative analytic sample size of 2,236, we find a negative association between increased use of the Internet for amenity purposes and community experience. While our models show mixed findings that community experience has decreased over time in rural areas, we find evidence that Internet activities can affect community experience, strengthening arguments that researchers should control for more than merely Internet access. Due to the associations between Internet activities and community experience, we argue that rural policymakers should find place-based ways to strengthen community experience.     

The use of sparsest cuts to reveal the hierarchical community structure of social networks

We show that good community structures can be obtained by partitioning a social network in a succession of divisive sparsest cuts. A network flow algorithm based on fundamental principles of graph theory is introduced to identify the sparsest cuts and an underlying hierarchical community structure of the network via maximum concurrent flow. Matula [Matula, David W., 1985. Concurrent flow and concurrent connectivity in graphs. In: Alavi, Y., et al. (Eds.), Graph Theory and its Applications to Algorithms and Computer Science. Wiley, New York, NY, pp. 543–559.] established the maximum concurrent flow problem (MCFP), and papers on divisive vs. agglomerative average-linkage hierarchical clustering [e.g., Matula, David W., 1983. Cluster validity by concurrent chaining. In: Felsenstein, J. (Ed.), Numerical Taxonomy: Proc. of the NATO Adv. Study Inst., vol. 1. Springer-Verlag, New York, pp. 156–166 (Proceedings of NATO ASI Series G); Matula, David W., 1986. Divisive vs. agglomerative average linkage hierarchical clustering. In: Gaul, W., and Schader, M. (Eds.), Classification as a Tool of Research. Elsevier, North-Holland, Amsterdam, pp. 289–301; Thompson, Byron J., 1985. A flow rerouting algorithm for the maximum concurrent flow problem with variable capacities and demands, and its application to cluster analysis. Master Thesis. School of Engineering and Applied Science, Southern Methodist University] provide the basis for partitioning a social network by way of sparsest cuts and/or maximum concurrent flow.     

Bridging: Locating critical connectors in a network

This paper proposes several measures for bridging in networks derived from Granovetter's (1973) insight that links which reduce distances in a network are important structural bridges. Bridging is calculated by systematically deleting links and calculating the resultant changes in network cohesion (measured as the inverse average path length). The average change for each node's links provides an individual level measure of bridging. We also present a normalized version which controls for network size and a network-level bridging index. Bridging properties are demonstrated on hypothetical networks, empirical networks, and a set of 100 randomly generated networks to show how the bridging measure correlates with existing network measures such as degree, personal network density, constraint, closeness centrality, betweenness centrality, and vitality. Bridging and the accompanying methodology provide a family of new network measures useful for studying network structure, network dynamics, and network effects on substantive behavioral phenomenon.     

Relational collaboration among spatial multipoint competitors

The presence of network ties between multipoint competitors is frequently assumed but rarely examined directly. The outcomes of multipoint competition, therefore, are better understood than their underlying relational mechanisms. Using original fieldwork and data that we have collected on an interorganizational network of patient transfer relations within a regional community of hospitals, we report and interpret estimates of Exponential Random Graph Models (ERGM) that specify the probability of observing network ties between organizations as a function of the degree of their spatial multipoint contact. We find that hospitals competing more intensely for patients across multiple geographical segments of their market (spatial multipoint competitors) are significantly more likely to collaborate. This conclusion is robust to alternative explanations for the formation of network ties based on organizational size differences, resource complementarities, performance differentials, and capacity constraints. We show that interorganizational networks between spatial multipoint competitors are characterized by clear tendencies toward clustering and a global core-periphery structure arising as consequences of multiple mechanisms of triadic closure operating simultaneously. We conclude that the effects of competition on the structure of interorganizational fields depends on how markets as physical and social settings are connected by cross-cutting network ties between competitors.     

Do resources of network members help in help seeking? Social capital and health information search

Does social capital as resources of network members affect health information search? Analyzing data from the 2004 General Social Survey in the United States, this study measures two social capital indicators (average education of network members and proportion of network members with a high school degree or higher) using the name generator. Most results are consistent using those two indicators. Both indicators are positively associated with frequency of health information seeking and seekers’ frequency of use of two sources (friends or relatives and the Internet). Also average education of network members is positively associated with seekers’ diversity of used sources and frequency of consultation with medical professionals. But neither indicator is associated with seekers’ frequency of use of other four sources (health-related magazines or newsletters, general magazines, daily newspapers, and radio or television programs). The findings demonstrate the theoretical utility of social capital in the social dynamics of medical help-seeking.     

From Culture to Connection: Internet Community Studies

Research into Internet communities and online behaviour constituted one of the earliest and most dynamic fields of interest in the new field of Internet studies. Studies focused a range of issues including understanding how social order was possible in mediated environments, the social and political dimensions of community, the impacts that online communities could have on their offline counterparts. At the same time, studies of Internet community provided Internet studies more generally with methodological innovations and new means of examining the new electronic worlds that had opened up. However, Internet community studies have been a victim of its own success to the extent that it has recently become fractured into a number of specialist arenas. As a consequence, the field has become both narrower and dominated by intractable problematics. However, recent research offers a new and revitalised agenda, focusing around the altering political status of community and new, and pressing questions have been opened up for the study of online community.     

Explaining the power-law degree distribution in a social commerce network

Social commerce is an emerging trend in which online shops create referral hyperlinks to other shops in the same online marketplace. We study the evolution of a social commerce network in a large online marketplace. Our dataset starts before the birth of the network (at which points shops were not linked to each other) and includes the birth of the network. The network under study exhibits a typical power-law degree distribution. We empirically compare a set of edge formation mechanisms (including preferential attachment and triadic closure) that may explain the emergence of this property. Our results suggest that the evolution of the network and the emergence of its power-law degree distribution are better explained by a network evolution mechanism that relies on vertex attributes that are not based on the structure of the network. Specifically, our analysis suggests that the power-law degree distribution emerges because shops prefer to connect to shops with more diverse assortments, and assortment diversity follows a power-law distribution. Shops with more diverse assortments are more attractive to link to because they are more likely to bring traffic from consumers browsing the WWW. Therefore, our results also imply that social commerce networks should not be studied in isolation, but rather in the context of the broader network in which they are embedded (the WWW).     

Territorial bounds to virtual space: transnational online and offline networks of Iranian and Turkish–Kurdish immigrants in the Netherlands

Abstract In this article we question a central trope of transnationalism and new media – deterritorialization – and its application to border crossing Internet usage by Iranian and Turkish‐Kurdish migrants in the Netherlands. Their Internet usage indicates the extent to which territoriality channels these groups' online practice. We found Dutch‐Iranian sites reflected correspondingly sparse offline community networks and state boundaries moulded their transnational ties, while regionally specific transnational dynamics were evident in Turkish‐Kurdish website surfing. These cases indicate that transnationalism and new media need not broaden or dissolve geographical identity or connectivity, but may reinforce it. Finally, we address the relations of territoriality with generation (first and second) and network medium (web forums versus conventional sites). Whereas first‐generation migrants' life online often reveals extensions of offline networks, the online practice of the second generation frequently reflects these networks in subtler ways, forming partially sovereign online communities that pivot on hyphenated identities. However, the relations of generation and network medium differ for Turkish Kurds and Iranians in the Netherlands.     

Estimating network properties from snowball sampled data

This article addresses the estimation of topological network parameters from data obtained with a snowball sampling design. An approximate expression for the probability of a vertex to be included in the sample is derived. Based on this sampling distribution, estimators for the mean degree, the degree correlation, and the clustering coefficient are proposed. The performance of these estimators and their sensitivity with respect to the response rate are validated through Monte Carlo simulations on several test networks. Our approach has no complex computational requirements and is straightforward to apply to real-world survey data. In a snowball sample design, each respondent is typically enquired only once. Different from the widely used estimator for Respondent-Driven Sampling (RDS), which assumes sampling with replacement, the proposed approach relies on sampling without replacement and is thus also applicable for large sample fractions. From the simulation experiments, we conclude that the estimation quality decreases with increasing variance of the network degree distribution. Yet, if the degree distribution is not to broad, our approach results in good estimates for the mean degree and the clustering coefficient, which, moreover, are almost independent from the response rate. The estimates for the degree correlation are of moderated quality.     

Stakeholder influence,government power,and new opportunities for Chinese Internet companies: A multilevel network analysis of cross-sector networks

In recent decades, Chinese Internet companies have experienced exponential growth. As the Internet industry increasingly commends tremendous financial resources, they also face growing stakeholder expectations for corporate social responsibility (CSR) actions. One way through which Chinese Internet companies conduct CSR is by building cross-sectoral collaborations with nonprofit and nongovernmental organizations (NGOs) and governmental agencies. Aiming to understand Internet companies’ strategic relationship building on CSR issues, the researchers drew from stakeholder influence theory and research on a network approach to stakeholder influence, and applied multilevel network analysis to model three networks related to Chinese Internet companies’ CSR collaborations. Specifically, we found that power and urgency are significant predictors of the structure of Internet companies’ cross-sector CSR alliance network. Organizations affiliated or endorsed by the central Chinese government are the most desirable CSR stakeholders. Additionally, the study also revealed that for Internet companies, devoting their attention to Internet-related social issues could increase their desirability as strategic stakeholders from other sectors and among Internet companies.