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Comparing Social Networks: Size, Density, and Local Structure
"... This paper demonstrates limitations in usefulness of the triad census for studying similarities among local structural properties of social networks. A triad census succinctly summarizes the local structure of a network using the frequencies of sixteen isomorphism classes of triads (sub-graphs of th ..."
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Cited by 21 (0 self)
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This paper demonstrates limitations in usefulness of the triad census for studying similarities among local structural properties of social networks. A triad census succinctly summarizes the local structure of a network using the frequencies of sixteen isomorphism classes of triads (sub-graphs of three nodes). The empirical base for this study is a collection of 51 social networks measuring different relational contents (friendship, advice, agonistic encounters, victories in fights, dominance relations, and so on) among a variety of species (humans, chimpanzees, hyenas, monkeys, ponies, cows, and a number of bird species). Results show that, in aggregate, similarities among triad censuses of these empirical networks are largely explained by nodal and dyadic properties – the density of the network and distributions of mutual, asymmetric, and null dyads. These results remind us that the range of possible network-level properties is highly constrained by the size and density of the network and caution should be taken in interpreting higher order structural properties when they are largely explained by local network features. 1
Network Theory
"... in general usage can refer to several different kinds of ideas. For example, both a theory of tie formation and a theory of the advantages of social capital could be considered network theory. In the tie formation case, network properties serve as the dependent variable, and the theory concerns the ..."
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Cited by 20 (2 self)
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in general usage can refer to several different kinds of ideas. For example, both a theory of tie formation and a theory of the advantages of social capital could be considered network theory. In the tie formation case, network properties serve as the dependent variable, and the theory concerns the antecedents of network phenomena. In the social capital case, the network construct is the independent variable, and the theory considers the consequences of network phenomena. We distinguish between the two kinds of theory by referring to the first (on antecedents) as theory of networks and the second (on consequences) as network
Learning to Change
, 1995
"... Changes in observed social networks may signal an underlying change within an organization, and may even predict significant events or behaviors. The breakdown of a team’s effectiveness, the emergence of informal leaders, or the preparation of an attack by a clandestine network may all be associated ..."
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Changes in observed social networks may signal an underlying change within an organization, and may even predict significant events or behaviors. The breakdown of a team’s effectiveness, the emergence of informal leaders, or the preparation of an attack by a clandestine network may all be associated with changes in the patterns of interactions between group members. The ability to systematically, statistically, effectively and efficiently detect these changes has the potential to enable the anticipation, early warning, and faster response to both positive and negative organizational activities. By applying statistical process control techniques to social networks we can rapidly detect changes in these networks. Herein we describe this methodology and then illustrate it using four data sets, of which the first is the Newcomb fraternity data, the second set of data is collected on a group of mid-career U.S. Army officers in a week long training exercise, the third is the perceived connections among members of al Qaeda based on open source, and the fourth data set is simulated using multi-agent simulation. The results indicate that this approach is able to detect change even with the high levels of uncertainty inherent in these data.
Detecting Changes in a Dynamic Social Network
, 2009
"... Social network analysis (SNA) has become an important analytic tool for analyzing terrorist networks, friendly command and control structures, arms trade, biological warfare, the spread of diseases, among other applications. Detecting dynamic changes over time from an SNA perspective, may signal an ..."
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Social network analysis (SNA) has become an important analytic tool for analyzing terrorist networks, friendly command and control structures, arms trade, biological warfare, the spread of diseases, among other applications. Detecting dynamic changes over time from an SNA perspective, may signal an underlying change within an organization, and may even predict significant events or behaviors. The challenges in detecting network change includes the lack of underlying statistical distributions to quantify significant change, as well as high relational dependence affecting assumptions of independence and normality. Additional challenges involve determining an algorithm that maximizes the probability of detecting change, given a risk level for false alarm. A suite of computational and statistical approaches for detecting change are identified and compared. The Neyman-Pearson most powerful test of simple hypotheses is extended as a cumulative sum statistical process control chart to detect network change over time. Anomaly detection approaches using exponentially weighted moving average or scan statistics investigate performance under conditions of potential time-series dependence.
Longitudinal Dynamic Network Analysis Using the Over Time Viewer Feature in ORA
, 2009
"... networks, change detection, network evolution, longitudinal network analysis, dynamic network analysis. Analyzing network over time has become increasingly popular as longitudinal network data becomes more available. Longitudinal networks are studied by sociologists to understand network evolution, ..."
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networks, change detection, network evolution, longitudinal network analysis, dynamic network analysis. Analyzing network over time has become increasingly popular as longitudinal network data becomes more available. Longitudinal networks are studied by sociologists to understand network evolution, belief formation, friendship formation, diffusion of innovations, the spread of deviant behavior and more. Organizations are interested in studying longitudinal network in order to get inside the decision cycle of major events. Prior to important events occurring in an organization, there is likely to exist an earlier change in network dynamics. Being able to identify that a change in network dynamics has occurred can enable managers to respond to the change in network behavior prior to the event occurring and shape a favorable outcome. The Over Time Viewer is a software tool hosted by the CASOS software suite that enables the analysis of longitudinal dynamic network data. This report introduces the Over Time Viewer and provides instruction on how to effectively use its features.
Parallel versus sequential update and the evolution of cooperation with the assistance of emotional strategies, arXiv preprint arXiv:1401.4672
, 2014
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Utility Seeking in Complex Social Systems: An Applied Longitudinal Network Study on Command and Control
"... Abstract. Humans are autonomous, intelligent, and adaptive agents. By adopting social network analysis techniques, we submit a framework for the study of dynamic networks and demonstrate the use of actor-oriented specifications in longitudinal networks. Through the use of a unique command and contro ..."
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Abstract. Humans are autonomous, intelligent, and adaptive agents. By adopting social network analysis techniques, we submit a framework for the study of dynamic networks and demonstrate the use of actor-oriented specifications in longitudinal networks. Through the use of a unique command and control dataset from experiments run at the US Military Academy, we illustrate the power of testing hypotheses on actor utility profiles. We frame static, covariate factors onto communication networks, and find that statistical hypothesis testing indicates edge networks truly motivate soldiers to seek information, collaborate, and modify the social network around them into more comfortable configurations of triad closure and edge reciprocity, when compared to hierarchical networks: a finding with profound implications to the study of complex, adaptive social systems. 12 1
