Dynamic network analysis of contact diaries

Analyzing two-mode networks linking actors to events they attend may help to uncover the structure and evolution of social networks. This classic social network insight is particularly valuable in the analysis of data extracted from contact diaries where contact events produce — and at the same time are the product of relations among participants. Contact events may comprise any number of actors meeting at a specific point in time. In this paper we recall the correspondence between two-mode actor–event networks and hypergraphs, and propose relational hyperevent models (RHEM) as a general modeling framework for networks of time-stamped multi-actor events in which the diarist (“ego”) simultaneously meets several of her alters. RHEM can estimate event intensities associated with each possible subset of actors that may jointly participate in events, and test network effects that may be of theoretical or empirical interest. Examples of such effects include preferential attachment, prior shared activity (familiarity), closure, and covariate effects explaining the propensity of actors to co-attend events. Statistical tests of these effects can uncover processes that govern the formation and evolution of informal groups among the diarist’s alters. We illustrate the empirical value of RHEM using data comprising almost 2000 meeting events of former British Prime Minister Margaret Thatcher with her cabinet ministers, transcribed from contact diaries covering her first term in office (1979–1983).     

Analysing networks of networks

We consider data with multiple observations or reports on a network in the case when these networks themselves are connected through some form of network ties. We could take the example of a cognitive social structure where there is another type of tie connecting the actors that provide the reports; or the study of interpersonal spillover effects from one cultural domain to another facilitated by the social ties. Another example is when the individual semantic structures are represented as semantic networks of a group of actors and connected through these actors’ social ties to constitute knowledge of a social group. How to jointly represent the two types of networks is not trivial as the layers and not the nodes of the layers of the reported networks are coupled through a network on the reports. We propose to transform the different multiple networks using line graphs, where actors are affiliated with ties represented as nodes, and represent the totality of the different types of ties as a multilevel network. This affords studying the associations between the social network and the reports as well as the alignment of the reports to a criterion graph. We illustrate how the procedure can be applied to studying the social construction of knowledge in local flood management groups. Here we use multilevel exponential random graph models but the representation also lends itself to stochastic actor-oriented models, multilevel blockmodels, and any model capable of handling multilevel networks.     

Logit Models for Affiliation Networks

Once confined to networks in which dyads could be reasonably assumed to be independent, the statistical analysis of network data has blossomed in recent years. New modeling and estimation strategies have made it possible to propose and evaluate very complex structures of dependency between and among ties in social networks. These advances have focused exclusively on one-mode networks—that is, networks of direct ties between actors. We generalize these models to affiliation networks, networks in which actors are tied to each other only indirectly through belonging to some group or event. We formulate models that allow us to study the (log) odds of an actor's belonging to an event (or an event including an actor) as a function of properties of the two-mode network of actors' memberships in events. We also provide illustrative analysis of some classic data sets on affiliation networks.     

Partitioning signed social networks

Structural balance theory has proven useful for delineating the blockmodel structure of signed social networks. Even so, most of the observed signed networks are not perfectly balanced. One possibility for this is that in examining the dynamics underlying the generation of signed social networks, insufficient attention has been given to other processes and features of signed networks. These include: actors who have positive ties to pairs of actors linked by a negative relation or who belong to two mutually hostile subgroups; some actors that are viewed positively across the network despite the presence of negative ties and subsets of actors with negative ties towards each other. We suggest that instead viewing these situations as violations of structural balance, they can be seen as belonging to other relevant processes we call mediation, differential popularity and internal subgroup hostility. Formalizing these ideas leads to the relaxed structural balance blockmodel as a proper generalization of structural balance blockmodels. Some formal properties concerning the relation between these two models are presented along with the properties of the fitting method proposed for the new blockmodel type. The new method is applied to four empirical data sets where improved fits with more nuanced interpretations are obtained.     

Micro-structural foundations of network inequality: Evidence from a field experiment in professional networking

Sociological accounts of network inequality typically rely on the logic of preferential attachment, holding that individuals in a social network prefer to form ties with central rather than peripheral actors. We develop an alternative explanation for the growth of network inequality that does not require actors to have knowledge about the social position of others or to hold explicit preferences for partners based on such knowledge. Instead, we theorize that central actors benefit from being exposed to more opportunities for triadic closure, which confounds a quality- or popularity-based signal that their greater connectedness might also send. We test this prediction an observational study and a field experiment across multiple professional conferences. In the field experiment, we test whether network centrality is predictive of tie formation if the benefits that central actors receive through their disproportional exposure to second-order network neighbors are randomly suppressed. The findings demonstrate that for the same level of exposure to opportunities for triadic closure, central actors and less central actors are equally likely to be selected as network partners. We discuss how the proposed mechanism may be used to rectify social capital disadvantages among disadvantaged groups.     

Network knowledge and the use of power

Complementing recent work on the effects of power on network perceptions, we offer a theory specifying how knowledge of network structures and exchange processes differentially affect the use of power by advantaged and disadvantaged positions. We argue that under certain conditions, network knowledge is beneficial to occupants of low-power positions, but not to occupants of high-power positions. Any low-power actor can benefit from having superior information, but if all low-power actors have equally sound knowledge, then all are worse off—a type of social trap. We tested these arguments by manipulating power and the availability of information on network structure and exchange processes in an experimental exchange network setting. The results were supportive.     

Because they were there: Access,deliberation, and the mobilization of networks for support

When people need help, what is the process through which they decide whom in their network to turn to? Research on social support has described a process that is deliberative in nature: people determine their needs, assess who in their network has the needed attributes—such as skill, trustworthiness, intimacy, and accessibility—and then activate that tie. Nevertheless, research in behavioral economics and other fields has shown that people make many decisions not deliberatively but intuitively. We examine this possibility in the context of social support by focusing on one factor: accessibility. Although researchers have argued that people weigh the accessibility of potential helpers as they do any other attribute, accessibility may be not only an attribute of the helper but also a condition of the situation. We develop a framework to make this question tractable for survey research and evaluate competing hypotheses using original data on an analytically strategic sample of ∼2000 college students, probing concrete instances of social support. We identify and document not one but three decision processes, reflective, incidental, and spontaneous activation, which differ in the extent to which actors had deliberated on whether to seek help and on whom to approach before activating the tie. We find that while the process was reflective (consistent with existing theory) when skill or trustworthiness played a role, it was significantly less so (consistent with the alternative) when accessibility did. Findings suggest that actors decide whom in their network to mobilize through at least three systematically different processes, two of which are consistent less with either active “mobilization” or explicit “help seeking” than with responsiveness to opportunity and context.     

Hybrid social and news media protest events: from #MarchinMarch to #BusttheBudget in Australia

Public protest events are now both social media and news media events. They are deeply entangled, with news media actors – such as journalists or news organisations – directly participating in the protest by tweeting about the event using the protest hashtag; and social media actors sharing news items published online by professional news agencies. Protesters have always deployed tactics to engage the media and use news media agencies’ resources to amplify their reach, with the dual aim of mobilising new supporters and adding their voice to public, mediatised debate. When protest moves between a physical space and a virtual space, the interactions between protesters and media stop being asynchronous or post hoc and turn instantaneous. In this new media-protest ecosystem, traditional media are still relevant sources of information and legitimacy, yet this dynamic is increasingly underpinned by a hybrid interdependency between traditional news and social media sources. In this paper we focus on an anti-austerity government movement that arose in Australia in early 2014 and was mobilised as a series of social media driven, connective action protest events. We show that there is a complex symbiotic interdependency between the movement and the traditional media for recognition and amplification of initial protest events, but that over time as media interest wanes, the movements’ network becomes disconnected and momentum is lost. This suggests that the active role traditional media play in protest events is being underestimated in the current research agenda on connective action.     

Social context and network formation: An experimental study

Recently, there has been increasing interest in determining which social network structures emerge as a consequence of the conscious actions of actors. Motivated by the belief that “networks matter” in reaching personal objectives, it is a natural assumption that actors try to optimize their network position. Starting from the notion that an optimal network position depends on the social context, we examine how actors change their networks to reach better positions in various contexts. Distinguishing between three social contexts (a neutral context, a context in which closed triads are costly, and a context in which closed triads are beneficial), theoretical results predict that emerging networks are contingent on the incentives that are present in these contexts. Experiments are used to test whether networks that are theoretically predicted to be stable are also stable experimentally. We find that emerging networks correspond to a large extent with the predicted networks. Consequently, they are contingent on the incentives present in various social contexts. In addition, we find that subjects tend to form specific stable networks with a higher probability than predicted, namely, efficient networks and networks in which everyone is equally well off.     

Exponential random graph models for little networks

Statistical models for social networks have enabled researchers to study complex social phenomena that give rise to observed patterns of relationships among social actors and to gain a rich understanding of the interdependent nature of social ties and actors. Much of this research has focused on social networks within medium to large social groups. To date, these advances in statistical models for social networks, and in particular, of Exponential-Family Random Graph Models (ERGMS), have rarely been applied to the study of small networks, despite small network data in teams, families, and personal networks being common in many fields. In this paper, we revisit the estimation of ERGMs for small networks and propose using exhaustive enumeration when possible. We developed an R package that implements the estimation of pooled ERGMs for small networks using Maximum Likelihood Estimation (MLE), called “ergmito”. Based on the results of an extensive simulation study to assess the properties of the MLE estimator, we conclude that there are several benefits of direct MLE estimation compared to approximate methods and that this creates opportunities for valuable methodological innovations that can be applied to modeling social networks with ERGMs.     

Actors and networks as objects

This paper explores the application of two contemporary computational methods to the development of sociological theory. Specifically, we combine the methods of object-orientation with discrete event simulation. This approach has several advantages for constructing and evaluating dynamic social theories.In object-oriented program design, objects combine and integrate the traditional concepts of data structures and algorithms, the building blocks of structured programming. Algorithms associated with objects are called methods or member functions. Constructing social actors as objects involves defining both their data attributes and the methods associated with these attributes. We also treat a social network as a computational object. It has data types of nodes and ties. As an object, the network must also have methods that add and delete nodes and ties. Once a network exists, we can create other data types and methods that describe and analyze the network. For example, networks have in-degree and out-degree vectors, and measures of hierarchy. In principle, we can create attributes of networks for all of the structural measures we use to describe networks.We use actor and network objects in a discrete event simulation of a process of formation of dominance structures, exploring several dynamic variations of the underlying theoretical model.     

Network centrality and social movement media coverage: A two-mode network analytic approach

This article examines the relationship between structural location (namely, degree centrality) and news media coverage. Our central hypothesis is that the network centrality of social movement actors is positively associated with the prevalence of actors being cited in the print news media. This paper uses two-mode data from a communication network of environmentalists in British Columbia, and examines the relationship between their structural location and the frequency by which they are cited in newsprint media with regard to particular frames (about forest conservation, environmental protest, and related issues). We asked a sample of social movement participants about their ties to a target list of relatively high profile actors (environmental activists). We turned the resulting network matrix into a bipartite graph that examined the relationships amongst the target actors vis a vis the respondents. Next we calculated point in-degree for the target actors. For the target actors we also have data from a representative sample of 957 print news articles about forestry and conservation of old growth forests in British Columbia. We compare the effects of network centrality of the target actor versus several attributes of the target actors (gender, level of radicalism, leadership status) on the amount of media coverage that each of the target actors receives. We find that network centrality is associated with media coverage controlling for actor attributes. We discuss theoretical implications of this research. Finally, we also discuss the methodological pros and cons of using a “target name roster” to construct two-mode data on social movement activists.     

Tweeting about emergency: A semantic network analysis of government organizations’ social media messaging during Hurricane Harvey

While social media like Twitter have been increasingly adopted by public-sector organizations, it remains less explored as to how government and emergency management (EM) organizations use these platforms to communicate with the public in response to emerging natural disasters. Extending the Situational Crisis Communication Theory (SCCT) to the realm of social media, this study examines the emerging semantic networks from 67 government and EM organizations’ official tweets during Hurricane Harvey over a three-week period. It identifies how multiple crisis response strategies—including instructing information, adjusting information, and bolstering—are constituted of different issues, actions, and organizational actors before, during, and immediately after the disaster event. Results suggest that government agencies use the strategy of instructing information predominantly before and during the disaster, whereas adjusting information and bolstering strategies are utilized more during post-disaster recovery. The study offers theoretical and practical implications of using a semantic network approach to studying organizational crisis responses.     

Social positions in influence networks

In this article we derive implications about social positions from a formal theory of social influence. The formal theory describes how, in a group of actors with heterogeneous initial opinions, a network of interpersonal influences enters into the formation of actors' settled opinions. We derive the following conclusions about a special form of structural equivalence. If actors are structurally equivalent in the network of interpersonal influences, then any dissimilarity of their initial opinions is reduced by the social influence process. If the social positions of actors are identical, i.e. if they have identical initial opinions and are structurally equivalent in the influence network, then they have identical opinions at equilibrium. If actors are not structurally equivalent in the network of interpersonal influences, then the social influence process does not necessarily reduce dissimilarities of initial opinions. We extend our analysis to consider automorphic equivalence.     

Social complex contagion in music listenership: A natural experiment with 1.3 million participants

Can live music events generate complex contagion in music streaming? This paper finds evidence in the affirmative—but only for the most popular artists. We generate a novel dataset from a music tracking website to analyse the listenership history of 1.3 million users over a two-month time horizon. We show that attending a music artist’s live concert increases that artist’s listenership among the attendees of the concert by approximately 1 song per day per attendee (p-value < 0.001). Moreover, this effect is contagious and can spread to users who did not attend the event. However, whether or not contagion occurs depends on the type of artist. We only observe contagious increases in listenership for popular artists (∼0.06 more daily plays per friend of an attendee [p < 0.001]), while the effect is absent for emerging stars. The contagion effect size increases monotonically with the number of friends who have attended the live event.     

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.     

Analyzing event stream dynamics in two-mode networks: An exploratory analysis of private communication in a question and answer community

Information about social networks can often be collected as event stream data. However, most methods in social network analysis are defined for static network snapshots or for panel data. We propose an actor oriented Markov process framework to analyze the structural dynamics in event streams. Estimated parameters are similar to what is known from exponential random graph models or stochastic actor oriented models as implemented in SIENA. We apply the methodology on a question and answer web community and show how the relevance of different kinds of one- and two-mode network structures can be tested using a new software.     

To err is social: Network prominence and its effects on self-estimation

In studying professional productivity, researchers find that actors do not always accurately perceive their productivity levels. Yet the literature does not provide an adequate explanation as to why these errors occur. This study examines the effects of network prominence on the self-estimation of productivity. I contend that network prominence is indicated through a variety of dimensions—visibility, connectedness, and professional affiliation—each of which has varying influence on the self-estimation process. This influence is best understood by exploring actors' internal vs. external attributions of prominence. Specifically, when actors attribute their prominence to internal efforts, they will tend to overestimate their productivity. Conversely, when actors attribute their network prominence to external circumstances, they will underestimate their productivity. The distinction between internal and external attributions of prominence relies, in large part, on the process of social comparison.     

The stability of exchange networks

Economic and sociological exchange theories predict divisions of exchange benefits given an assumed fixed network of exchange relations. Since network structure has been found to have a large impact on actors’ payoffs, actors have strong incentives for network change. We answer the question what happens to both the network structure and actor payoffs when myopic actors change their links in order to maximize their payoffs. We investigate the networks that are stable, the networks that are efficient or egalitarian with varying tie costs, and the occurrence of social dilemmas. Only few networks are stable over a wide range of tie costs, and all of them can be divided into two types: efficient networks consisting of only dyads and at most one isolate, and Pareto efficient and egalitarian cycles with an odd number of actors. Social dilemmas are observed in even-sized networks at low tie costs.     

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.