Self-Organising Networks in Complex Infrastructure Projects: The Case of London Bank Station Capacity Upgrade Project

Managing large infrastructure projects remains a thorny issue in theory and practice. This is mainly due to their increasingly interconnected, interdependent, multilateral, nonlinear, unpredictable, uncontrollable, and rapidly changing nature. This study is an attempt to demystify the key issues to the management of large construction projects, arguing that these projects are delivered through networks that evolve in ways that we do not sufficiently understand as yet. The theoretical framework of this study is grounded in Complexity Theory; a theory resulted in a paradigm shift when it was first introduced to project management post-2000 but is yet to be unpacked in its full potential. The original contribution of the study is predicated on perceiving large construction projects as evolving complex systems that involves a high degree of self‐organisation. This is a process that transitions contractually static prescribed roles to dynamic network roles, comprising individuals exchanging information. Furthermore, by placing great emphasis upon informal communications, this study demonstrates how self-organising networks can be married with Complexity Theory. This approach has the potential to make bedfellows around the concept of managing networks within a context of managing projects; a concept that is not always recognised, especially in project management. With the help of social network analysis, two snapshots from Bank Station Capacity Upgrade Project Network were analysed as a case study. Findings suggest that relationships and hence network structures in large construction projects exhibit small-world topology, underlined by a high degree of sparseness and clustering. These are distinct structural properties of self-organising networks. Evidence challenges the theorisation about self-organisation which largely assumes positive outcomes and suggests that self-organising could open up opportunities yet also create constraints. This helps to provide further insights into complexity and the treatment of uncertainty in large projects. The study concludes with detailed recommendations for research and practice

Hierarchy measure for complex networks

Nature, technology and society are full of complexity arising from the intricate web of the interactions among the units of the related systems (e.g., proteins, computers, people). Consequently, one of the most successful recent approaches to capturing the fundamental features of the structure and dynamics of complex systems has been the investigation of the networks associated with the above units (nodes) together with their relations (edges). Most complex systems have an inherently hierarchical organization and, correspondingly, the networks behind them also exhibit hierarchical features. Indeed, several papers have been devoted to describing this essential aspect of networks, however, without resulting in a widely accepted, converging concept concerning the quantitative characterization of the level of their hierarchy. Here we develop an approach and propose a quantity (measure) which is simple enough to be widely applicable, reveals a number of universal features of the organization of real-world networks and, as we demonstrate, is capable of capturing the essential features of the structure and the degree of hierarchy in a complex network. The measure we introduce is based on a generalization of the m-reach centrality, which we first extend to directed/partially directed graphs. Then, we define the global reaching centrality (GRC), which is the difference between the maximum and the average value of the generalized reach centralities over the network. We investigate the behavior of the GRC considering both a synthetic model with an adjustable level of hierarchy and real networks. Results for real networks show that our hierarchy measure is related to the controllability of the given system. We also propose a visualization procedure for large complex networks that can be used to obtain an overall qualitative picture about the nature of their hierarchical structure.Comment: 29 pages, 9 figures, 4 table

Spacelab system analysis: A study of the Marshall Avionics System Testbed (MAST)

An analysis of the Marshall Avionics Systems Testbed (MAST) communications requirements is presented. The average offered load for typical nodes is estimated. Suitable local area networks are determined

Analysis of Layered Social Networks

Prevention of near-term terrorist attacks requires an understanding of current terrorist organizations to include their composition, the actors involved, and how they operate to achieve their objectives. To aid this understanding, operations research, sociological, and behavioral theory relevant to the study of social networks are applied, thereby providing theoretical foundations for new methodologies to analyze non-cooperative organizations, defined as those trying to hide their structure or are unwilling to provide information regarding their operations. Techniques applying information regarding multiple dimensions of interpersonal relationships, inferring from them the strengths of interpersonal ties, are explored. A layered network construct is offered that provides new analytic opportunities and insights generally unaccounted for in traditional social network analyses. These provide decision makers improved courses of action designed to impute influence upon an adversarial network, thereby achieving a desired influence, perception, or outcome to one or more actors within the target network. This knowledge may also be used to identify key individuals, relationships, and organizational practices. Subsequently, such analysis may lead to the identification of exploitable weaknesses to either eliminate the network as a whole, cause it to become operationally ineffective, or influence it to directly or indirectly support National Security Strategy

COREnet: the fusion of social network analysis and target audience analysis

The purpose of this capstone is to highlight and explain how the target audience analysis (TAA) process can be enhanced by incorporating aspects of influence theory, social movement theory (SMT) and social network analysis (SNA). While a large body of literature addresses influence theory, SMT and SNA, little has been written within military information support operations (MISO) doctrine recognizing SNA in the analytical process. This capstone creates a method to apply SNA, SMT, and influence theory to existing MISO doctrine while also developing a scalable web-based application that assists with visualizing and analyzing open source data to draw meaningful conclusions and assist decision making on given operational problem sets. The web-based interface, COREnet, is a high fidelity prototype derived completely from open- source technology. The examples utilized are from a 2006 data set of an Indonesian terrorist network to demonstrate how SNA can be fully integrated into the TAA process.http://archive.org/details/corenetfusionofs1094544638Major, United States ArmyApproved for public release; distribution is unlimited

An Application of Social Network Analysis on Military Strategy, System Networks and the Phases of War

The research developed in this study will utilize Social Network and Graph Theory terminology and methodology applied to groups of systems, rather than individuals within a given system, in order to shape strategic level goals. With regard to military operations, Social Network Analysis has been used to show that enemy networks and relationships can be accurately represented using weighted layers with weighted relationships in order to identify the key player(s) that must be influenced and/or removed so that a particular effect on the enemy might be realized. Social Network Analysis is therefore a significant tool concerning tactical level of operations that aids in developing a targeting methodology which aids tactical commanders in mission planning, however has never been applied to strategic levels of Command. Like previous key player problems, this research will utilize system attributes and global relational strengths as inputs. The output results will rank order representative systems of interest that satisfy the constraints and desired objectives within a particular Phase of War. This work will apply and extend the tools of Social Network Analysis structure and techniques to a theater level mission

Survey of Routing Algorithms for Computer Networks

This thesis gives a general discussion of routing for computer networks, followed by an overview of a number of typical routing algorithms used or reported in the past few years. Attention is mainly focused on distributed adaptive routing algorithms for packet switching (or message switching) networks. Algorithms for major commercial networks (or network architectures) are reviewed as well, for the convenience of comparison

Key aspects of covert networks data collection:Problems, challenges, and opportunities

Data quality is considered to be among the greatest challenges in research on covert networks. This study identifies six aspects of network data collection, namely nodes, ties, attributes, levels, dynamics, and context. Addressing these aspects presents challenges, but also opens theoretical and methodological opportunities. Furthermore, specific issues arise in this research context, stemming from the use of secondary data and the problem of missing data. While each of the issues and challenges has some specific solution in the literature on organized crime and social networks, the main argument of this paper is to try and follow a more systematic and general solution to deal with these issues. To this end, three potentially synergistic and combinable techniques for data collection are proposed for each stage of data collection – biographies for data extraction, graph databases for data storage, and checklists for data reporting. The paper concludes with discussing the use of statistical models to analyse covert networks and the cultivation of relations within the research community and between researchers and practitioners

Social Capital in the ICT Sector – A Network Perspective on Executive Turnover and Startup Performance

Recently, The Wall Street Journal proclaimed the “War for Internet Talent” among companies in the Information and Communication Technology (ICT) sector. At the same time, talented employees become entrepreneurial and establish their own startups. We aim to provide evidence that startup performance is not based exclusively on access to talent, in the sense of individual human capital, but is also determined by a social capital aspect resulting from their executives’ turnover history. We apply social network analysis (SNA) combined with logistical regression on a large dataset of companies and executives in the ICT sector. Our study contributes to turnover and entrepreneurship in information systems research, as well as to social capital and multilevel systems research. Furthermore, we shed a light on turnover patterns in the ICT sector, contribute to a better understanding of success factors for startups, and provide a practical measure to help identify and differentiate key employees