Standards as interdependent artifacts : the case of the Internet

Thesis (Ph. D.)--Massachusetts Institute of Technology, Engineering Systems Division, 2008.Includes bibliographical references.This thesis has explored a new idea: viewing standards as interdependent artifacts and studying them with network analysis tools. Using the set of Internet standards as an example, the research of this thesis includes the citation network, the author affiliation network, and the co-author network of the Internet standards over the period of 1989 to 2004. The major network analysis tools used include cohesive subgroup decomposition (the algorithm by Newman and Girvan is used), regular equivalence class decomposition (the REGE algorithm and the method developed in this thesis is used), nodal prestige and acquaintance (both calculated from Kleinberg's technique), and some social network analysis tools. Qualitative analyses of the historical and technical context of the standards as well as statistical analyses of various kinds are also used in this research. A major finding of this thesis is that for the understanding of the Internet, it is beneficial to consider its standards as interdependent artifacts. Because the basic mission of the Internet (i.e. to be an interoperable system that enables various services and applications) is enabled, not by one or a few, but by a great number of standards developed upon each other, to study the standards only as stand-alone specifications cannot really produce meaningful understandings about a workable system. Therefore, the general approaches and methodologies introduced in this thesis which we label a systems approach is a necessary addition to the existing approaches. A key finding of this thesis is that the citation network of the Internet standards can be decomposed into functionally coherent subgroups by using the Newman-Girvan algorithm.(cont.) This result shows that the (normative) citations among the standards can meaningfully be used to help us better manage and monitor the standards system. The results in this thesis indicate that organizing the developing efforts of the Internet standards into (now) 121 Working Groups was done in a manner reasonably consistent with achieving a modular (and thus more evolvable) standards system. A second decomposition of the standards network was achieved by employing the REGE algorithm together with a new method developed in this thesis (see the Appendix) for identifying regular equivalence classes. Five meaningful subgroups of the Internet standards were identified, and each of them occupies a specific position and plays a specific role in the network. The five positions are reflected in the names we have assigned to them: the Foundations, the Established, the Transients, the Newcomers, and the Stand-alones. The life cycle among these positions was uncovered and is one of the insights that the systems approach on this standard system gives relative to the evolution of the overall standards system. Another insight concerning evolution of the standard system is the development of a predictive model for promotion of standards to a new status (i.e. Proposed, Draft and Internet Standards as the three ascending statuses). This model also has practical potential to managers of standards setting organizations and to firms (and individuals) interested in efficiently participating in standards setting processes. The model prediction is based on assessing the implicit social influence of the standards (based upon the social network metric, betweenness centrality, of the standards' authors) and the apparent importance of the standard to the network (based upon calculating the standard's prestige from the citation network).(cont.) A deeper understanding of the factors that go into this model was also developed through the analysis of the factors that can predict increased prestige over time for a standard. The overall systems approach and the tools developed and demonstrated in this thesis for the study of the Internet standards can be applied to other standards systems. Application (and extension) to the World Wide Web, electric power system, mobile communication, and others would we believe lead to important improvements in our practical and scholarly understanding of these systems.by Mo-Han Hsieh.Ph.D

Ontology-based knowledge management for technology intensive industries

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Link prediction and link detection in sequences of large social networks using temporal and local metrics

This dissertation builds upon the ideas introduced by Liben-Nowell and Kleinberg in The Link Prediction Problem for Social Networks [42]. Link prediction is the problem of predicting between which unconnected nodes in a graph a link will form next, based on the current structure of the graph. The following research contributions are made: • Highlighting the difference between the link prediction and link detection problems, which have been implicitly regarded as identical in current research. Despite hidden links and forming links having very highly significant differing metric values, they could not be distinguished from each other by a machine learning system using traditional metrics in an initial experiment. However, they could be distinguished from each other in a "simple" network (one where traditional metrics can be used for prediction successfully) using a combination of new graph analysis approaches. • Defining temporal metric statistics by combining traditional statistical measures with measures commonly employed in financial analysis and traditional social network analysis. These metrics are calculated over time for a sequence of sociograms. It is shown that some of the temporal extensions of traditional metrics increase the accuracy of link prediction. • Defining traditional metrics using different radii to those at which they are normally calculated. It is shown that this approach can increase the individual prediction accuracy of certain metrics, marginally increase the accuracy of a group of metrics, and greatly increase metric computation speed without sacrificing information content by computing metrics using smaller radii. It also solves the “distance-three task” (that common neighbour metrics cannot predict links between nodes at a distance greater than three). • Showing that the combination of local and temporal approaches to link prediction can lead to very high prediction accuracies. Furthermore in “complex” networks (ones where traditional metrics cannot be used for prediction successfully) local and temporal metrics become even more useful

Network analysis of shared interests represented by social bookmarking behaviors

Social bookmarking is a new phenomenon characterized by a number of features including active user participation, open and collective discovery of resources, and user-generated metadata. Among others, this study pays particular attention to its nature of being at the intersection of personal information space and social information space. While users of a social bookmarking site create and maintain their own bookmark collections, the users' personal information spaces, in aggregate, build up the information space of the site as a whole. The overall goal of this study is to understand how social information space may emerge when personal information spaces of users intersect and overlap with shared interests. The main purpose of the study is two-fold: first, to see whether and how we can identify shared interest space(s) within the general information space of a social bookmarking site; and second, to evaluate the applicability of social network analysis to this end. Delicious.com, one of the most successful instances of social bookmarking, was chosen as the case. The study was carried out in three phases asking separate yet interrelated questions concerning the overall level of interest overlap, the structural patterns in the network of users connected by shared interests, and the communities of interest within the network. The results indicate that, while individual users of delicious.com have a broad range of diverse interests, there is a considerable level of overlap and commonality, providing a ground for creating implicit networks of users with shared interests. The networks constructed based on common bookmarks revealed intriguing structural patterns commonly found in well-established social systems, including a core periphery structure with a high level of connectivity, which form a basis for efficient information sharing and knowledge transfer. Furthermore, an exploratory analysis of the network communities showed that each community has a distinct theme defining the shared interests of its members, at a high level of coherence. Overall, the results suggest that networks of people with shared interests can be induced from their social bookmarking behaviors and such networks can provide a venue for investigating social mechanisms of information sharing in this new information environment. Future research can be built upon the methods and findings of this study to further explore the implication of the emergent and implicit network of shared interests

A Theoretical Study of a Generalized Version of Kleinberg's HITS Algorithm

Kleinberg's HITS algorithm (Kleinberg 1999), which was originally developed in a Web context, tries to infer the authoritativeness of a Web page in relation to a specific query using the structure of a subgraph of the Web graph, which is obtained considering this specific query. Recent applications of this algorithm in contexts far removed from that of Web searching (Bacchin, Ferro and Melucci 2002, Ng et al. 2001) inspired us to study the algorithm in the abstract, independently of its particular applications, trying to mathematically illuminate its behaviour. In the present paper we detail this theoretical analysis. The original work starts from the definition of a revised and more general version of the algorithm, which includes the classic one as a particular case. We perform an analysis of the structure of two particular matrices, essential to studying the behaviour of the algorithm, and we prove the convergence of the algorithm in the most general case, finding the analytic expression of the vectors to which it converges. Then we study the symmetry of the algorithm and prove the equivalence between the existence of symmetry and the independence from the order of execution of some basic operations on initial vectors. Finally, we expound some interesting consequences of our theoretical results