Conceptualizing Social Capital as Access to Social Network and Mobilization of Network Resources: A Study of Workplace Literacy Programs and Low-income Somali Refugee Workers

There is a substantial body of literature on the economic benefits of workplace literacy programs, and much less empirical studies on the social or non-economic outcomes of workplace literacy programs, particularly in the context of low-income refugee workers. Adopting a social network approach, this study examines the impact of workplace literacy programs on the social capital development of Somali refugee workers. Social capital can be defined as the network of relationships possessed by an individual or social group that facilitates their access to emotional, instrumental, or informational resources, essential for their daily survival, stability, or upward mobility. This study takes the position that literacy development is a socially situated and contextualized set of practices which impact the structure of an individual’s social network. Thereby, creating access to certain types of social resources –emotional, instrumental and informational – that can be used for the good of the individual. Data were drawn using interviews with eighteen participants enrolled in a workplace literacy program and had attended classes for at least three months. The classes offered included ESL, GED and Citizenship. The interview protocol was designed using a hybrid (name and resource) generator instrument. First, we examined how participating in classes impacted the structure of participant’s social networks by measuring (i) the size of the social network, and (ii) strength of the ties in social network. Next, we examined the types of social capital resources that accrue to low income Somali refugee workers through their networks acquired as a result of participating in classes. The findings revealed that participation in classes had a positive impact on their network structure, through the acquisition of strong ties with co-workers, and weak ties with teachers and supervisors. This created access to emotional, instrumental and informational resources that participants previously did not have access to and consequently enhanced their social capital development. Moreover, mobilizing social capital resources through strong ties with co-workers would have been difficult or impossible in the absence of specific mechanisms, which we identified as motivation, trust and reciprocity

Exploring the Organizational Effects of Directors\u27 Embeddedness in Board Networks

In this dissertation, I explore how top executives’ and directors’ embeddedness in corporate elite networks within and between organizations’ boards of directors influence organizational strategy and policy. In the first study, I conduct a comprehensive review of the governance literature using both a traditional narrative approach as well as a bibliometric main path analysis, which traces the development and diffusion of scholarly knowledge on corporate elite networks. In the second study, drawing from network theory and behavioral governance research, I introduce a methodology that allows researchers to model intraboard networks by measuring the strength of ties among members of boards of directors based on objective formative indicators of the constructs of social similarity, social status, social exchange, and social history. Next, I use this technique to explore the antecedents and consequences of intraorganizational network characteristics of boards. Finally, in the third study, I examine the joint influence of interlocking directorates and intraorganizational networks of boards of directors on interorganizational imitation of corporate strategic activity. Results show that directors’ centrality within a focal organization’s board and those of its alters are important predictors of interorganizational imitation of corporate strategic activity. I contribute to the strategic management and organization theory literatures by advancing our understanding of the relationship of corporate elite networks with organizational strategy and policy, and by introducing a new approach to modeling directors’ networks in corporate governance research

Improving Link Prediction in Intermittently Connected Wireless Networks by Considering Link and Proximity Stabilities

Several works have outlined the fact that the mobility in intermittently connected wireless networks is strongly governed by human behaviors as they are basically human-centered. It has been shown that the users' moves can be correlated and that the social ties shared by the users highly impact their mobility patterns and hence the network structure. Tracking these correlations and measuring the strength of social ties have led us to propose an efficient distributed tensor-based link prediction technique. In fact, we are convinced that the feedback provided by such a prediction mechanism can enhance communication protocols such as opportunistic routing protocols. In this paper, we aim to bring out that measuring the stabilities of the link and the proximity at two hops can improve the efficiency of the proposed link prediction technique. To quantify these two parameters, we propose an entropy estimator in order to measure the two stability aspects over successive time periods. Then, we join these entropy estimations to the tensor-based link prediction framework by designing new prediction metrics. To assess the contribution of these entropy estimations in the enhancement of tensor-based link prediction efficiency, we perform prediction on two real traces. Our simulation results show that by exploiting the information corresponding to the link stability and/or to the proximity stability, the performance of the tensor-based link prediction technique is improved. Moreover, the results attest that our proposal's ability to outperform other well-known prediction metrics.Comment: Published in the proceedings of the 13th IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM), San Francisco, United States, 201

Attention on Weak Ties in Social and Communication Networks

Granovetter's weak tie theory of social networks is built around two central hypotheses. The first states that strong social ties carry the large majority of interaction events; the second maintains that weak social ties, although less active, are often relevant for the exchange of especially important information (e.g., about potential new jobs in Granovetter's work). While several empirical studies have provided support for the first hypothesis, the second has been the object of far less scrutiny. A possible reason is that it involves notions relative to the nature and importance of the information that are hard to quantify and measure, especially in large scale studies. Here, we search for empirical validation of both Granovetter's hypotheses. We find clear empirical support for the first. We also provide empirical evidence and a quantitative interpretation for the second. We show that attention, measured as the fraction of interactions devoted to a particular social connection, is high on weak ties --- possibly reflecting the postulated informational purposes of such ties --- but also on very strong ties. Data from online social media and mobile communication reveal network-dependent mixtures of these two effects on the basis of a platform's typical usage. Our results establish a clear relationships between attention, importance, and strength of social links, and could lead to improved algorithms to prioritize social media content

Measuring Tie Strength in Implicit Social Networks

Given a set of people and a set of events they attend, we address the problem of measuring connectedness or tie strength between each pair of persons given that attendance at mutual events gives an implicit social network between people. We take an axiomatic approach to this problem. Starting from a list of axioms that a measure of tie strength must satisfy, we characterize functions that satisfy all the axioms and show that there is a range of measures that satisfy this characterization. A measure of tie strength induces a ranking on the edges (and on the set of neighbors for every person). We show that for applications where the ranking, and not the absolute value of the tie strength, is the important thing about the measure, the axioms are equivalent to a natural partial order. Also, to settle on a particular measure, we must make a non-obvious decision about extending this partial order to a total order, and that this decision is best left to particular applications. We classify measures found in prior literature according to the axioms that they satisfy. In our experiments, we measure tie strength and the coverage of our axioms in several datasets. Also, for each dataset, we bound the maximum Kendall's Tau divergence (which measures the number of pairwise disagreements between two lists) between all measures that satisfy the axioms using the partial order. This informs us if particular datasets are well behaved where we do not have to worry about which measure to choose, or we have to be careful about the exact choice of measure we make.Comment: 10 page

Research Collaboration Analysis Using Text and Graph Features

Patterns of scientific collaboration and their effect on scientific production have been the subject of many studies. In this paper we analyze the nature of ties between co-authors and study collaboration patterns in science from the perspective of semantic similarity of authors who wrote a paper together and the strength of ties between these authors (i.e. how much have they previously collaborated together). These two views of scientific collaboration are used to analyze publications in the TrueImpactDataset [11], a new dataset containing two types of publications - publications regarded as seminal and publications regarded as literature reviews by field experts. We show there are distinct differences between seminal publications and literature reviews in terms of author similarity and the strength of ties between their authors. In particular, we find that seminal publications tend to be written by authors who have previously worked on dissimilar problems (i.e. authors from different fields or even disciplines), and by authors who are not frequent collaborators. On the other hand, literature reviews in our dataset tend to be the result of an established collaboration within a discipline. This demonstrates that our method provides meaningful information about potential future impacts of a publication which does not require citation information

On Facebook, most ties are weak

Pervasive socio-technical networks bring new conceptual and technological challenges to developers and users alike. A central research theme is evaluation of the intensity of relations linking users and how they facilitate communication and the spread of information. These aspects of human relationships have been studied extensively in the social sciences under the framework of the "strength of weak ties" theory proposed by Mark Granovetter.13 Some research has considered whether that theory can be extended to online social networks like Facebook, suggesting interaction data can be used to predict the strength of ties. The approaches being used require handling user-generated data that is often not publicly available due to privacy concerns. Here, we propose an alternative definition of weak and strong ties that requires knowledge of only the topology of the social network (such as who is a friend of whom on Facebook), relying on the fact that online social networks, or OSNs, tend to fragment into communities. We thus suggest classifying as weak ties those edges linking individuals belonging to different communities and strong ties as those connecting users in the same community. We tested this definition on a large network representing part of the Facebook social graph and studied how weak and strong ties affect the information-diffusion process. Our findings suggest individuals in OSNs self-organize to create well-connected communities, while weak ties yield cohesion and optimize the coverage of information spread.Comment: Accepted version of the manuscript before ACM editorial work. Check http://cacm.acm.org/magazines/2014/11/179820-on-facebook-most-ties-are-weak/ for the final versio

Quantifying Triadic Closure in Multi-Edge Social Networks

Multi-edge networks capture repeated interactions between individuals. In social networks, such edges often form closed triangles, or triads. Standard approaches to measure this triadic closure, however, fail for multi-edge networks, because they do not consider that triads can be formed by edges of different multiplicity. We propose a novel measure of triadic closure for multi-edge networks of social interactions based on a shared partner statistic. We demonstrate that our operalization is able to detect meaningful closure in synthetic and empirical multi-edge networks, where common approaches fail. This is a cornerstone in driving inferential network analyses from the analysis of binary networks towards the analyses of multi-edge and weighted networks, which offer a more realistic representation of social interactions and relations.Comment: 19 pages, 5 figures, 6 table