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Social Network Analysis: Methods and Applications
25 Nov 1994-
TL;DR: This paper presents mathematical representation of social networks in the social and behavioral sciences through the lens of Dyadic and Triadic Interaction Models, which describes the relationships between actor and group measures and the structure of networks.
Abstract: Part I. Introduction: Networks, Relations, and Structure: 1. Relations and networks in the social and behavioral sciences 2. Social network data: collection and application Part II. Mathematical Representations of Social Networks: 3. Notation 4. Graphs and matrixes Part III. Structural and Locational Properties: 5. Centrality, prestige, and related actor and group measures 6. Structural balance, clusterability, and transitivity 7. Cohesive subgroups 8. Affiliations, co-memberships, and overlapping subgroups Part IV. Roles and Positions: 9. Structural equivalence 10. Blockmodels 11. Relational algebras 12. Network positions and roles Part V. Dyadic and Triadic Methods: 13. Dyads 14. Triads Part VI. Statistical Dyadic Interaction Models: 15. Statistical analysis of single relational networks 16. Stochastic blockmodels and goodness-of-fit indices Part VII. Epilogue: 17. Future directions.
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TL;DR: To identify regions of high connectivity in the human cerebral cortex, a computationally efficient approach was applied to map the degree of intrinsic functional connectivity across the brain and explored whether the topography of hubs could explain the pattern of vulnerability in Alzheimer's disease (AD).
Abstract: Recent evidence suggests that some brain areas act as hubs interconnecting distinct, functionally specialized systems. These nexuses are intriguing because of their potential role in integration and also because they may augment metabolic cascades relevant to brain disease. To identify regions of high connectivity in the human cerebral cortex, we applied a computationally efficient approach to map the degree of intrinsic functional connectivity across the brain. Analysis of two separate functional magnetic resonance imaging datasets (each n = 24) demonstrated hubs throughout heteromodal areas of association cortex. Prominent hubs were located within posterior cingulate, lateral temporal, lateral parietal, and medial/lateral prefrontal cortices. Network analysis revealed that many, but not all, hubs were located within regions previously implicated as components of the default network. A third dataset (n = 12) demonstrated that the locations of hubs were present across passive and active task states, suggesting that they reflect a stable property of cortical network architecture. To obtain an accurate reference map, data were combined across 127 participants to yield a consensus estimate of cortical hubs. Using this consensus estimate, we explored whether the topography of hubs could explain the pattern of vulnerability in Alzheimer's disease (AD) because some models suggest that regions of high activity and metabolism accelerate pathology. Positron emission tomography amyloid imaging in AD (n = 10) compared with older controls (n = 29) showed high amyloid-beta deposition in the locations of cortical hubs consistent with the possibility that hubs, while acting as critical way stations for information processing, may also augment the underlying pathological cascade in AD.
2,569 citations
TL;DR: The authors describe three distinctive types of diversity: separation, variety, and disparity, and present guidelines for conceptualization, measurement, and theory testing, highlighting the special case of demographic diversity.
Abstract: Research on organizational diversity, heterogeneity, and related concepts has proliferated in the past decade, but few consistent findings have emerged. We argue that the construct of diversity requires closer examination. We describe three distinctive types of diversity: separation, variety, and disparity. Failure to recognize the meaning, maximum shape, and assumptions underlying each type has held back theory development and yielded ambiguous research conclusions. We present guidelines for conceptualization, measurement, and theory testing, highlighting the special case of demographic diversity
2,541 citations
TL;DR: In this article, the authors discuss how social structures and social networks can affect economic outcomes like hiring, price, productivity, and innovation, focusing on Sociologists have developed core principles about the interactions of social structure, information, ability to punish or reward, and trust.
Abstract: This chapter begins by reviewing some of the principles. Building on these, the chapter then discusses how social structures and social networks can affect economic outcomes like hiring, price, productivity, and innovation. It focuses on Sociologists have developed core principles about the interactions of social structure, information, ability to punish or reward, and trust that frequently recur in their analyses of political, economic, and other institutions. Thus, network structure can be partially endogenized in labor market analysis. However, there are also a range of alternatives, not commonly included in economic analysis, that work through social groups and create compliance in less intrusive ways. Many studies, comprehensively reviewed in Roger Myersons, show the powerful impact of social structure and networks on the extent and source of innovation and its diffusion. When people trade with others they know, the impact of knowing each other on the price varies with their relationship, the cost of shifting to different partners, and the market situation.
2,493 citations
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TL;DR: This review presents the emergent field of temporal networks, and discusses methods for analyzing topological and temporal structure and models for elucidating their relation to the behavior of dynamical systems.
Abstract: A great variety of systems in nature, society and technology -- from the web of sexual contacts to the Internet, from the nervous system to power grids -- can be modeled as graphs of vertices coupled by edges The network structure, describing how the graph is wired, helps us understand, predict and optimize the behavior of dynamical systems In many cases, however, the edges are not continuously active As an example, in networks of communication via email, text messages, or phone calls, edges represent sequences of instantaneous or practically instantaneous contacts In some cases, edges are active for non-negligible periods of time: eg, the proximity patterns of inpatients at hospitals can be represented by a graph where an edge between two individuals is on throughout the time they are at the same ward Like network topology, the temporal structure of edge activations can affect dynamics of systems interacting through the network, from disease contagion on the network of patients to information diffusion over an e-mail network In this review, we present the emergent field of temporal networks, and discuss methods for analyzing topological and temporal structure and models for elucidating their relation to the behavior of dynamical systems In the light of traditional network theory, one can see this framework as moving the information of when things happen from the dynamical system on the network, to the network itself Since fundamental properties, such as the transitivity of edges, do not necessarily hold in temporal networks, many of these methods need to be quite different from those for static networks
2,452 citations
TL;DR: In this article, a theory of stakeholder influences is proposed, which accommodates multiple, interdependent stakeholder demands and predicts how organizations respond to the simultaneous influence of multiple stakeholders.
Abstract: Stakeholder theory development has increased in recent years, in part because of its emphasis on explaining and predicting how an organization functions with respect to the relationships and influences existing in its environment. Thus far. most researchers have concentrated on dyadic relationships between individual stakeholders and a focal organization. Using social network analysis, I construct in this article a theory of stakeholder influences, which accommodates multiple, interdependent stakeholder demands and predicts how organizations respond to the simultaneous influence of multiple stakeholders.
2,393 citations
Cites background from "Social Network Analysis: Methods an..."
...Wasserman and Faust (1994) provide a comprehensive list, including community elite decision making (Laumann & Pappi, 1973), social influence (Marsden...
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