Adam M. Kleinbaum

Discretion Within Constraint: Homophily and Structure in a Formal Organization

Homophily in social relations results from both individual preferences and selective opportunities for interaction, but how these two mechanisms interact in large, contemporary organizations is not well understood. We argue that organizational structures and geography delimit opportunities for interaction such that actors have a greater level of discretion to choose their interaction partners within business units, job functions, offices, and quasi-formal structures. This leads us to expect to find a higher proportion of homophilous interactions within these organizational structures than across their boundaries. We test our theory in an analysis of the rate of dyadic communication in an email data set comprising thousands of employees in a large information technology firm. These findings have implications for research on homophily, gender relations in organizations, and formal and informal organizational structure.

Organizational Misfits and the Origins of Brokerage in Intra-Firm Networks

We have ample empirical evidence of the effect of brokerage in intraorganizational social networks on career outcomes, but we know less about how network structure originates as a result of career processes. In this paper, I argue that brokerage results from two distinct mechanisms – links with former co-workers and with friends of friends accumulated as careers unfold. Furthermore, I hypothesize that “organizational misfits�? – individuals who follow career trajectories that are atypical in their organization – will have access to more valuable brokerage opportunities than individuals whose careers followed more conventional paths. Career history is recorded longitudinally for 30,000 employees in a large information technology firm over six years and sequence-analyzed to measure individual-level fit with typical career paths in the organization. Network position is measured using a unique data set of over 250 million electronic mail messages. Empirical results support the hypotheses that diverse – and especially atypical – careers have a causal effect on brokerage through mechanisms rooted in social capital, even when accounting for endogeneity between networks and mobility.

Inside the black box of the corporate staff: Social networks and the implementation of corporate strategy

In multidivisional firms, the corporate staff is central to the implementation of corporate-level strategy, but empirical evidence on its function is limited. We examine one corporate staff through e-mail analysis. We find sharp cross-sectional differences in communication patterns: staff members have networks that are larger, more integrative, and richer in structural holes. However, much of this difference is attributed to sorting processes, rather than being caused by employment in the corporate staff per se. Further, once people receive the ‘corporate imprimatur,’ they retain aspects of it even when they move back to the line organization. These results imply that the literatures emphasis on structure as a means to achieve coordination undervalues a selection process in which individuals with broad networks match to coordination-focused jobs in the corporate staff. Copyright

Popularity, Similarity, and the Network Extraversion Bias

Using the emergent friendship network of an incoming cohort of students in an M.B.A. program, we examined the role of extraversion in shaping social networks. Extraversion has two important implications for the emergence of network ties: a popularity effect, in which extraverts accumulate more friends than introverts do, and a homophily effect, in which the more similar are two people’s levels of extraversion, the more likely they are to become friends. These effects result in a systematic network extraversion bias, in which people’s social networks will tend to be overpopulated with extraverts and underpopulated with introverts. Moreover, the most extraverted people have the greatest network extraversion bias, and the most introverted people have the least network extraversion bias. Our finding that social networks were systematically misrepresentative of the broader social environment raises questions about whether there is a societal bias toward believing other people are more extraverted than they actually are and whether introverts are better socially calibrated than extraverts.

Similar neural responses predict friendship

Human social networks are overwhelmingly homophilous: individuals tend to befriend others who are similar to them in terms of a range of physical attributes (e.g., age, gender). Do similarities among friends reflect deeper similarities in how we perceive, interpret, and respond to the world? To test whether friendship, and more generally, social network proximity, is associated with increased similarity of real-time mental responding, we used functional magnetic resonance imaging to scan subjects’ brains during free viewing of naturalistic movies. Here we show evidence for neural homophily: neural responses when viewing audiovisual movies are exceptionally similar among friends, and that similarity decreases with increasing distance in a real-world social network. These results suggest that we are exceptionally similar to our friends in how we perceive and respond to the world around us, which has implications for interpersonal influence and attraction.Though we are often friends with people similar to ourselves, it is unclear if neural responses to perceptual stimuli are also similar. Here, authors show that the similarity of neural responses evoked by a range of videos was highestxa0for close friends and decreased with increasing social distance.

An Altercentric Perspective on the Origins of Brokerage in Social Networks: How Perceived Empathy Moderates the Self-Monitoring Effect

Social structure matters in organizational life, but our understanding of the origins of social network structure remains limited. In this paper, we observe that the literature on individual differences and social networks focuses almost exclusively on egos views of herself and of her network. Our approach complements this egocentric perspective with a more altercentric view, in which others perceptions of and reactions to egos personality and relational behavior shape the structure of egos network. Our altercentric perspective builds on earlier evidence that the construct of self-monitoring is associated with brokerage, but it suggests that the effect of self-monitoring on brokerage is amplified in those perceived as highly empathic and attenuated in those perceived as lower in empathy. A mechanism that underlies this effect is the greater propensity of others to reciprocate the social interactions of high-empathy, high self-monitors than those low in empathy. We find support for these predictions in a study of the dynamic emergence of a social network among a complete cohort of MBA students and conclude that alters are active agents in the formation of egos network.

Hyperlink Prediction in Hypernetworks Using Latent Social Features

Predicting the existence of links between pairwise objects in networks is a key problem in the study of social networks. However, relationships among objects are often more complex than simple pairwise relations. By restricting attention to dyads, it is possible that information valuable for many learning tasks can be lost. The hypernetwork relaxes the assumption that only two nodes can participate in a link, permitting instead an arbitrary number of nodes to participate in so-called hyperlinks or hyperedges, which is a more natural representation for complex, multi-party relations. However, the hyperlink prediction problem has yet to be studied. In this paper, we propose HPLSF (Hyperlink Prediction using Latent Social Features), a hyperlink prediction algorithm for hypernetworks. By exploiting the homophily property of social networks, HPLSF explores social features for hyperlink prediction. To handle the problem that social features are not always observable, a latent social feature learning scheme is developed. To cope with the arbitrary cardinality hyperlink issue in hypernetworks, we design a feature-embedding scheme to map the a priori arbitrarily-sized feature set associated with each hyperlink into a uniformly-sized auxiliary space. To address the fact that observed features and latent features may be not independent, we generalize a structural SVM to learn using both observed features and latent features. In experiments, we evaluate the proposed HPLSF framework on three large-scale hypernetwork datasets. Our results on the three diverse datasets demonstrate the effectiveness of the HPLSF algorithm. Although developed in the context of social networks, HPLSF is a general methodology and applies to arbitrary hypernetworks.

Reorganization and Tie Decay Choices

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