Centrality in social networks: ii. experimental results

Three competing hypotheses about structural centrality are explored by means of a replication of the early MIT experiments on communication structure and group problem-solving. It is shown that although two of the three kinds of measures of centrality have a demonstrable effect on individual responses and group processes, the classic measure of centrality based on distance is unrelated to any experimental variable. A suggestion is made that the positive results provided by distance-based centrality in earlier experiments is an artifact of the particular structures chosen for experimentation.     

Centrality in affiliation networks

This paper discusses the conceptualization, measurement, and interpretation of centrality in affiliation networks. Although centrality is a well-studied topic in social network analysis, and is one of the most widely used properties for studying affiliation networks, virtually all discussions of centrality and centralization have concerned themselves with one-mode networks. Bonacich's work on simultaneous group and individual centralities is a notable exception (Social Networks, 1991, 13, 155–168). I begin by outlining the distinctive features of affiliation networks and describe four motivations for centrality indices in affiliation networks. I then consider properties of some existing centrality indices for affiliation networks, including the relationship between centralities for actors and events in these networks, and present a new conceptualization of centrality that builds on the formal properties of affiliation networks and captures important theoretical insights about the positions of actors and events in these networks. These centralities are then illustrated on Galaskiewicz's data on club and board memberships of a sample of corporate executive officers (Social Organization of an Urban Grants Economy. New York: Academic Press, 1985). The conclusion to this paper discusses strengths and weaknesses of centrality indices when applied to affiliation networks.     

Familial groups in social networks

Many structural definitions for social community have been proposed in attempt to characterize and further understand the structure of social relationships. Algorithms using quantitative concepts such as centrality measures, spectral methods and other clustering measures have been used to compute social communities. While these methods have had much success in extracting meaningful subgroups in social and biological (and other) networks, they do not necessarily reveal the defining structure of social attraction.     

Measuring social networks cross-culturally

This paper explores the reliability and validity of a cross-cultural survey approach to network measurement using data from two ethnic groups in rural West Africa. Over a 2-week test-retest period, no significant differences in network size were detected. Network composition was less stable over the same period, particularly among peripheral versus core network members. Comparisons with support networks identified through lifecycle interviews and 24 h recall support the construct validity of the survey approach in both ethnic groups.     

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.     

Norm formation in social influence networks

I propose a mechanism of norm formation and maintenance that combines classical theory in social psychology on attitudes and social comparisons with a formal network theory of social influence. Underlying the formation of norms is the ubiquitous belief that there is a correct response for every situation and an abiding interest for persons to base their responses on these correct foundations. Given such a belief, a normative evaluation of a feeling, thought or action is likely to arise when persons perceive that their positive or negative attitudinal evaluation is shared by one or more influential others. If interpersonal agreements validate attitudes and transform attitudes into norms, then the development of a theory of norm formation may draw on extant “combinatorial” theories of consensus production that describe how shared attitudes are produced and maintained in groups. The network theory of social influence that I employ is one such combinatorial approach. An important sociological implication of this network theory is that the content of norms must be consistent with the social stratification (or more general pattern of inequality) of interpersonal influences in a group. I illustrate the theory with an analysis of Roethlisberger and Dickson’s (1939) classic observations on the Bank Wiring Observation Room.     

A conceptual tool: Making social workers' assumptions explicit

This paper emphasizes the importance for social work practitioners of making their assumptions about human nature explicit and of taking responsibility for the clinical consequences of using these assumptions. More specifically, it proposes a conceptual tool to help practitioners examine both the assumptions that guide their deliberations and the practical implications of these assumptions. This conceptual tool is based on nine fundamental assumptions about the human condition pertaining to matters such as freedom-determination, subjectivity-objectivity, proactivity-reactivity, and so forth. Each assumption is presented on a continuum along which practitioners can locate themselves. By deliberately placing themselves on each of the nine continua, the practitioners are encouraged to articulate their clinical concepts. This articulation enables them to maintain a critical dialogue with themselves about the ideas they use to understand and help their clients and makes the underlying concepts, and the decisions that stem from them, more amenable to the practitioner's conscious evaluation and control. The framework can also be used to compare the assumptions of practitioners to different theoretical approaches and can thereby help practitioners to better understand their own theoretical predilections. Finally, I propose that this framework be employed as an educational aid for the professional development of students and practitioners in social work education.     

Role similarity and complexity in social networks

A new concept of role similarity is presented; this definition is a generalisation of structural equivalence but is stricter than the idea of regular equivalence of White and Reitz (1983). This new definition forms a standard part of graph theory literature and as such has been well researched. Consequently a complexity measure first proposed by Mowshowitz, 1968a, Mowshowitz, 1968b, Mowshowitz, 1968c can be given a definite meaning in terms of network concepts. A new measure of structural complexity based on role similarity is also introduced.     

Sampling and estimation in large social networks

An unknown network is modelled by a directed or undirected graph having vertices of different kinds. Partial information is available concerning the vertex labels and the edge occurrences within a simple random sample of vertices. Using this information we find unbiased estimators and variance estimators of such graph parameters which can be given as dyad or triad counts. In particular, we give approximate formulae pertaining to large networks.     

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 support in social work

Following the recommendations of the Barclay Report for a move on the part of social work towards supporting helping networks in the community, it is necessary to look more closely at the components of ‘social support’ and to be clearer about which social networks social workers are being asked to support. It is suggested that sociology, although criticised for its remoteness from the daily experience of the social worker, could offer through social network analysis some useful measures of the support networks of social work clients.     

Beauty and social capital: Being attractive shapes social networks

It is a fact of life that being beautiful is advantageous (Hamermesh, 2011). Whether we consider incomes or the marriage market, the more attractive one is, the better one does. Drawing on psychological research that explains why beauty pays, we hypothesize that more attractive people will exploit social network opportunities differently than less attractive people and, consequently, their networks will comprise more beneficial features. As predicted, results of an experiment showed that more attractive people were more likely to select for themselves more profitable broker positions in networks relative to other positions and relative to less attractive people. Relying on network data supplied by young professionals, in a follow-up study we found that the networks of more attractive people were relatively less dense, as the findings of Study 1 would imply. We discuss the implications for our work as well as highlight the need for further research into an understudied, but potentially influential brokerage contingency factor—physical appeal.     

The motivational foundation of social networks

The origins of motivations for network behavior are found in modern theories of early childhood development. There are two types of early motivations: safety on the one hand and efficacy on the other. Safety corresponds to and is generated by networks of cohesion, while efficacy comes about from separation and corresponds to networks with structural holes and is typical of brokerage situations. Both cohesion and brokerage are necessarily involved in any network situation as a matter of fundamental theoretical necessity, but they occur under different circumstances in individual and organizational action. Studies are reviewed that show that effective relations both within and between organizations require both cohesive bases and brokerage between otherwise not well connected units.     

Biased networks and social structure theorems

It is shown that the part of Peter Blau's theory of social structure bearing on heterogeneity can be imbedded within the framework of biased networks, thereby clarifying conceptual aspects of the theory as well as providing a mathematical proof of the most central heterogeneity theorem for a class of networks. The key step is the postulation of a mathematical concept of salience, called an ‘inbreeding bias’ in prior work, which corresponds to Blau's fundamental axiom. In addition, there are derivations and discussions bearing on related topics in the analysis of social structure.     

Fuzzy duocentric community detection model in social networks

The main goal of this paper is to present a clustering model to identify duocentric communities in the complex networks. A duocentric community is built around two central nodes which are as close as possible to other nodes, while the central nodes are connected enough to each other to shape the center of the community. To detect such communities, we develop a new objective function based clustering model. The network's nodes are assigned to the duocentric communities by the type-2 fuzzy numbers which indicate the degrees of belonging to the communities by upper and lower membership values. Generated interval type-2 fuzzy membership values by our proposed model are able to determine how much each node belongs to the both central nodes and how it is shared among communities. Also, the compatible verification index with the proposed model is introduced to evaluate and compare the results of the proposed model with the existing approach in the literature. Finally, the performance of the proposed algorithm is validated by detecting duocentric communities in three artificial networks and two real social networks.     

Polyhedral dynamics and conflict mobilization in social networks

A representation of social networks in terms of polyhedral dynamics is used to study the mobilization of individuals in a specific social network. By making use of the distinction between backcloth and traffic, the analysis shows there are compelling structural reasons for the outcome of the mobilization process in the particular network studied.     

Predictors of accuracy in perceiving informal social networks

One's ability to accurately perceive the existence of informal social networks, referred to as cognitive accuracy, has recently been shown to play an important role in explaining various social phenomena. This paper addresses the research question of what predicts cognitive accuracy by developing a simple theory which links one's accuracy to the amount of information one exchanges with co-workers. To test the predictions of my theory, I analyze friendship and advice network data from a small entrepreneurial firm. The results of the analysis show that predictors of accuracy tend to be workers' degree centrality in the friendship or advice network and demographic and social distances between workers.