A unified framework for traditional and agent-based social network modeling

In the last sixty years of research, several models have been proposed to explain (i) the formation and (ii) the evolution of networks. However, because of the specialization required for the problems, most of the agent-based models are not general. On the other hand, many of the traditional network models focus on elementary interactions that are often part of several different processes. This phenomenon is especially evident in the field of models for social networks. Therefore, this chapter presents a unified conceptual framework to express both novel agent-based and traditional social network models. This conceptual framework is essentially a meta-model that acts as a template for other models. To support this meta-model, the chapter proposes a different kind of agent-based modeling tool that we specifically created for developing social network models. The tool the authors propose does not aim at being a general-purpose agent-based modeling tool, thus remaining a relatively simple software system, while it is extensible where it really matters. Eventually, the authors apply this toolkit to a novel problem coming from the domain of P2P social networking platforms

A Comparison between Asynchronous Backtracking Pseudocode and its JADEL Implementation

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Proclivity or Popularity? Exploring Agent Heterogeneity in Network Formation

The Barabasi-Albert model (BA model) is the standard algorithm used to describe the emergent mechanism of a scale-free network. This dissertation argues that the BA model, and its variants, rarely take agent heterogeneity into account in the analysis of network formation. In social networks, however, people\u27s decisions to connect are strongly affected by the extent of similarity. In this dissertation, the author applies an agent-based modeling (ABM) approach to reassess the Barabasi-Albert model. This study proposes that, in forming social networks, agents are constantly balancing between instrumental and intrinsic preferences. After systematic simulation and subsequent analysis, this study finds that agents\u27 preference of popularity and proclivity strongly shapes various attributes of simulated social networks. Moreover, this analysis of simulated networks investigates potential ways to detect this balance within real-world networks. Particularly, the scale parameter of the power-distribution is found sensitive solely to agents\u27 preference popularity. Finally, this study employs the social media data (i.e., diffusion of different emotions) for Sina Weibo—a Chinese version Tweet—to valid the findings, and results suggest that diffusion of anger is more popularity-driven

Strategies to Manage Enterprise Information Technology Projects

Since 2005, most midsize company information technology (IT) projects had a 62.4% failure rate because of wrong project team communication skills or cost overruns. IT leaders expect negative IT project outcomes will cost over $2 billion by 2020. Using the actor-network theory, the purpose of this single case study was to explore strategies used by IT leaders from a midsize IT company in Washington, D.C. to plan and execute projects under budget and on time. Using purposeful sampling, 5 IT leaders were selected for this study because of their experience in implementing successful strategies for projects. Data were collected using face-to-face semistructured interviews, company documentation, and internal organizational risk reports. Yin\u27s 5-step process was used for data analysis to compile, disassemble, reassemble, interpret, and conclude the data. The interpretation of data, subjected to methodological triangulation and member checking to strengthen the dependability and credibility of the findings, yielded 3 themes of IT leader communication skills: IT leader strategy, IT leader knowledge, and implementation of cost savings. The findings indicated that IT leaders serve as the key actors in the IT project network, and leader communication skills are essential for implementing strategies for IT project completion and cost savings. With this knowledge, IT leaders can implement strategies to plan and execute projects under budget and on time. The implications for a positive social change includes the potential for IT leaders to reduce project production waste and contribute to economic expansion

Agent-based Interpretations of Classic Network Models

The paper is aimed at developing agent-based variants of traditional network models that make full use of concurrency. First, we review some classic models of the static structure of complex networks with the objective of developing agent-based models suited for simulating a large-scale, technology-enabled social network. Secondly, we outline the basic properties that characterize such networks. Then, we briefly discuss some classic network models and the properties of the networks they generate. Finally, we discuss how such models can be converted into agent-based models (i) to be executed more easily in heavily concurrent environments and (ii) to serve as basic blocks for more complex agent-based models. We evidence that many implicit assumptions made by traditional models regarding their execution environment are too expensive or outright impossible to maintain in concurrent environments. Consequently, we present the concurrency issues resulting from the violation of such assumptions. Then, we experimentally show that, under reasonable hypothesis, the agent-based variants maintain the main features of the classic models, notwithstanding the change of environment. Eventually, we present a meta-model that we singled out from the individual classic models and that we used to simplify the agent-oriented conversion of the traditional models. Finally, we discuss the software tools that we built to run the agent-based simulations