Social Network Analysis

In most natural and engineered systems, a set of entities interact with each other in complicated patterns that can encompass multiple types of relationships, change in time, and include other types of complications Such systems include multiple subsystems and layers of connectivity, and it is important to take such "multilayer" features into account to try to improve our understanding of complex systems Consequently, it is necessary to generalize "traditional" network theory by developing (and validating) a framework and associated tools to study multilayer systems in a comprehensive fashion The origins of such efforts date back several decades and arose in multiple disciplines, and now the study of multilayer networks has become one of the most important directions in network science In this paper, we discuss the history of multilayer networks (and related concepts) and review the exploding body of work on such networks To unify the disparate terminology in the large body of recent work, we discuss a general framework for multilayer networks, construct a dictionary of terminology to relate the numerous existing concepts to each other, and provide a thorough discussion that compares, contrasts, and translates between related notions such as multilayer networks, multiplex networks, interdependent networks, networks of networks, and many others We also survey and discuss existing data sets that can be represented as multilayer networks We review attempts to generalize single-layer-network diagnostics to multilayer networks We also discuss the rapidly expanding research on multilayer-network models and notions like community structure, connected components, tensor decompositions, and various types of dynamical processes on multilayer networks We conclude with a summary and an outlook

Multilayer Networks

Models and Methods in Social Network Analysis presents the most important developments in quantitative models and methods for analyzing social network data that have appeared during the 1990s. Intended as a complement to Wasserman and Faust’s Social Network Analysis: Methods and Applications, it is a collection of original articles by leading methodologists reviewing recent advances in their particular areas of network methods. Reviewed are advances in network measurement, network sampling, the analysis of centrality, positional analysis or blockmodeling, the analysis of diffusion through networks, the analysis of affiliation or “two-mode” networks, the theory of random graphs, dependence graphs, exponential families of random graphs, the analysis of longitudinal network data, graphic techniques for exploring network data, and software for the analysis of social networks.

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Models and methods in social network analysis

Social computing represents a new computing paradigm and an interdisciplinary research and application field. Undoubtedly, it strongly influences system and software developments in the years to come. We expect that social computing's scope continues to expand and its applications multiply. From both theoretical and technological perspectives, social computing technologies moves beyond social information processing towards emphasizing social intelligence. As we've discussed, the move from social informatics to social intelligence is achieved by modeling and analyzing social behavior, by capturing human social dynamics, and by creating artificial social agents and generating and managing actionable social knowledge

Social Computing: From Social Informatics to Social Intelligence

The Enron email corpus is appealing to researchers because it represents a rich temporal record of internal communication within a large, real-world organization facing a severe and survival-threatening crisis We describe how we enhanced the original corpus database and present findings from our investigation undertaken with a social network analytic perspective We explore the dynamics of the structure and properties of the organizational communication network, as well as the characteristics and patterns of communicative behavior of the employees from different organizational levels We found that during the crisis period, communication among employees became more diverse with respect to established contacts and formal roles Also during the crisis period, previously disconnected employees began to engage in mutual communication, so that interpersonal communication was intensified and spread through the network, bypassing formal chains of communication The findings of this study provide valuable insight into a real-world organizational crisis, which may be further used for validating or developing theories and dynamic models of organizational crises; thereby leading to a better understanding of the underlying causes of, and response to, organization failure

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Communication Networks from the Enron Email Corpus It's Always About the People. Enron is no Different

To facilitate the analysis of real and simulated data on groups, organizations and societies, tools and measures are needed that can handle relational or network data that is multi-mode, multi-link and multi-time period in which nodes and edges have attributes with possible data errors and missing data. The integrated CASOS dynamic network analysis toolkit described in this paper is an interoperable set of scalable software tools. These tools form a toolchain that facilitate the dynamic extraction, analysis, visualization and reasoning about key actors, hidden groups, vulnerabilities and changes in such data at varying levels of fidelity. We present these tools and illustrate their capabilities using data collected from a series of 368 texts on an organizational system interfaced with covert networks in the Middle East.

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Toward an interoperable dynamic network analysis toolkit

Most people have at least an intuitive understanding of hierarchies, how they work, and how to affect their behavior However, covert organizations, such as terrorist organizations, have network structures that are distinct from those in typical hierarchical organizations Their structure is distinct from the organizations that most people in western culture are used to dealing with In particular, they tend to be more cellular and distributed As such, most people do not have an intuitive understanding of how they work and instead seek to think of them as hierarchies However, analysis reveals that trying to destabilize a cellular distributed network using tactics designed for hierarchies is likely to be ineffective A secondary problem is that
despite the vast quantities of information on the size, shape and structure of these networks, such,
information is incomplete and possible erroneous What is needed is a set of tools and an
approach to assessing destabilization strategies in a decision context that takes these difficulties
in to account and provides analysts with guidance in assessing alternative destabilization tactics
Such an approach is forwarded in this paper In addition, initial lessons learned are discussed
The particular approach is extensible and scales well to groups composed of 1000’s of members

Destabilizing Terrorist Networks

Using ORA to explore the relationship of nursing unit communication to patient safety and quality outcomes

: When Linton C. Freeman made his conceptual clarifications about centrality measures in social network analysis in 1979 he exclusively focused on unweighted, symmetric, and connected networks without the possibility of self-loops. Even though a lot of articles have been published in the last years discussing network measures for weighted, asymmetric or unconnected networks, the vast majority of researchers dealing with social network data simplify their networks based on Freeman's 1979 definitions before they calculate centrality measures. When dealing with weighted and/or asymmetric networks which can have self links and consist of multiple components, researchers are confronted with a lack of standardization. Different tools for social network analysis treat specific cases differently. In this article we describe and discuss the ways the software ORA (developed by CASOS at Carnegie Mellon University) handles the most important network measures in case of weighted, asymmetric, self-looped, and disconnected networks. In the center of our attention are the following measures, degree centrality, closeness centrality, betweenness centrality, eigenvector centrality, and clustering coefficient.

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Handling Weighted, Asymmetric, Self-Looped, and Disconnected Networks in ORA

Managing and controlling knowledge intensive dynamic systems requires being able to estimate, analyze, and evaluate, under conditions of uncertainty, the existing system and the impact of actions, such as changes in personnel or resources on this system. Our approach to this problem is dynamic network analysis. Using a combination of statistical and simulation tools, we analyze the robustness under uncertainty of a series of metrics for identifying key entities whose removal from the network destabilizes the network by degrading performance on one or more dimensions. We examine multiple types of uncertainty, including cases of over and underestimation of the presence of relations among entities. We find that higher levels of information assurance are needed for nodes than edges, and that destabilization even under uncertainty can still be disruptive. A potential application is to identify individuals in a covert network to isolate in order to decrease the network's ability to take action.

Jeffrey Reminga

Papers

Toward an interoperable dynamic network analysis toolkit

Destabilizing Terrorist Networks

Using ORA to explore the relationship of nursing unit communication to patient safety and quality outcomes

Handling Weighted, Asymmetric, Self-Looped, and Disconnected Networks in ORA

Destabilizing dynamic networks under conditions of uncertainty