The emergence of social conventions in multi-agent systems has been analyzed mainly in settings where every agent may interact either with every other agent or with nearest neighbours, according to some regular underlying topology. In this note we argue that these topologies are too simple if we take into account recent discoveries on real networks. These networks, one of the main examples being the Internet, are what is called complex, that is, either graphs with the small-world property or scale-free graphs. In this note we study the efficiency of the emergence of social conventions in complex networks, that is, how fast conventions are reached. Our main result is that complex graphs make the system much more efficient than regular graphs with the same average number of links per node. Furthermore, we find out that scale-free graphs make the system as efficient as fully connected graphs.

The identification and management of work dependencies is a fundamental challenge in software development organizations. This paper argues that modularization, the traditional technique intended to reduce interdependencies among components of a system, has serious limitations in the context of software development. We build on the idea of congruence, proposed in our prior work, to examine the relationship between the structure of technical and work dependencies and the impact of dependencies on software development productivity. Our empirical evaluation of the congruence framework showed that when developers’ coordination patterns are congruent with their coordination needs, the resolution time of modification requests was significantly reduced. Furthermore, our analysis highlights the importance of identifying the “right” set of technical dependencies that drive the coordination requirements among software developers. Call and data dependencies appear to have far less impact than logical dependencies. Categories and Subject Descriptors

In a time of information glut, observations about complex systems and phenomena of interest are available in several applications areas, such as biology and text. As a consequence, scientists have started searching for patterns that involve interactions among the objects of analysis, to the effect that research on models and algorithms for network analysis has become a central theme for knowledge discovery and data mining (KDD). The intuitions behind the plethora of approaches rely upon few basic types of networks, identified by specific local and global topological properties, which we term “pure ” topology types. In this paper, (1) we survey pure topology types along with existing sampling algorithms that generate them, (2) we introduce novel algorithms that enhance the diversity of samples, and address the case of cellular topologies, (3) we perform statistical studies of the stability of the properties of pure types to alternative generative algorithms, and a joint study of the separability of pure types, in terms of their embedding in a space of metrics for network analysis, widely adopted in the social and physical sciences. We conclude with a word of caution to the practitioners, who sample pure topology types to assess the “statistical significance” of their findings, e.g., the p-value of the clustering coefficient is sensitive to the sampling algorithm used. We find that different pure types share similar topological properties. Further, real world networks hardly present the variability profile of a single pure type. We suggest the assumption of “mixtures of types ” as an alternative starting point for developing models and algorithms for network analysis.

We present precise and explicit, definitions and descriptions of pertinent terms and constructs related to a specific type of network topology – the cellular network. First, we clarify the construct of a cell and establish the concept of a cell-core and cell-periphery; next, we present details of the broader, cellular network. Further, we introduce the notion of a k-cell subgraph construct. Throughout the report, we introduce several supplementary terms related to these concepts. The terminology and formalization we present can be effectively utilized when conversing about real-world social networks, but may be essential as guidelines when constructing cellular social-networks for virtual experiments. Increasingly, the network form referred to as “cellular ” is appearing in empirical social-network studies and is being applied in virtual experiments of social-networks. The presence of a cellular network is particularly prominent in research pertaining to covert and terrorist organizations; although, the form can be found in other less saturnine situations. While the structure of a cellular network is often rather intuitive, to avoid confusion researchers, analysts and especially experimenters have need for formal definitions and precise a description of the topology. To date, no such formalization of

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There is a pressing need to automatically collect data on social systems as rich network data, analyze such systems to find hidden relations and groups, prune the datasets to locate regions of interest, locate key actors, characterize the structure, locate points of vulnerability, and simulate change in a system as it evolves naturally or in response to strategic interventions over time or under certain impacts, including modification of data. To meet this challenge, we need to develop a new mechanism for storing and manipulating social structure data. Social structure data will be stored in a relational database capable of manipulating large quantities of data. The database is structured to preserve the character and integrity of the data in an extensible manner, and is extended with a number of functions specifically designed for manipulating graph-based

__________________________________________________________________ The purpose of this study is two-fold. First, a validation study on Construct-TM is conducted to show that modeling the actual and cognitive knowledge networks of a group can produce agent interactions within the model that correlate significantly with the communication network obtained from empirical data. Second, empirically grounded theory is produced by combining empirical data with simulation experiments run on empirically validated models.

Over the past couple of decades, geographically distributed work has become pervasive and software development organizations are no exception. Factors such as access to talent, acquisitions and the need to reduce the time-to-market of new products

Foundation, or the U.S. government. The study of complex social and technological systems, such as organizations, requires a sophisticated approach that accounts for the underlying psychological and sociological principles, communication patterns and the technologies within these systems. Social Network Analysis and link analysis have since inception operated on the cutting edge bringing together mathematical analysis of social structures and qualitative reasoning and interpretation. As available computing power grew, social network-based models have become not only an analysis tool, but also a methodology for building new theories of social behaviour and organizational evolution, frequently through the creation of simulation models. This work examines the past approaches of creating Social Network-based semantically consistent and interpretable models of social structure and social

simulation, model validation, organization theory, network organization, organizational Network organizations offer learning, adaptive and resilient capabilities that are particularly useful in high velocity environments as these capabilities allow the organization to effectively respond to change. The dynamic, evolutionary nature of network organizations affords such advantageous capabilities. Although the advantages of network organizations are well-studied, the risks associated with them are not. Of interest is the study of critical personnel. Understanding criticality within an organization can help improve performance and protect against the risk of loss. But the study of critical personnel has traditionally used static structural representations that do not represent the dynamic nature of network organizations. This thesis advances the study of critical personnel risks in network organizations