49,013 research outputs found
Evolutionary Events in a Mathematical Sciences Research Collaboration Network
This study examines long-term trends and shifting behavior in the
collaboration network of mathematics literature, using a subset of data from
Mathematical Reviews spanning 1985-2009. Rather than modeling the network
cumulatively, this study traces the evolution of the "here and now" using
fixed-duration sliding windows. The analysis uses a suite of common network
diagnostics, including the distributions of degrees, distances, and clustering,
to track network structure. Several random models that call these diagnostics
as parameters help tease them apart as factors from the values of others. Some
behaviors are consistent over the entire interval, but most diagnostics
indicate that the network's structural evolution is dominated by occasional
dramatic shifts in otherwise steady trends. These behaviors are not distributed
evenly across the network; stark differences in evolution can be observed
between two major subnetworks, loosely thought of as "pure" and "applied",
which approximately partition the aggregate. The paper characterizes two major
events along the mathematics network trajectory and discusses possible
explanatory factors.Comment: 30 pages, 14 figures, 1 table; supporting information: 5 pages, 5
figures; published in Scientometric
Pattern vectors from algebraic graph theory
Graphstructures have proven computationally cumbersome for pattern analysis. The reason for this is that, before graphs can be converted to pattern vectors, correspondences must be established between the nodes of structures which are potentially of different size. To overcome this problem, in this paper, we turn to the spectral decomposition of the Laplacian matrix. We show how the elements of the spectral matrix for the Laplacian can be used to construct symmetric polynomials that are permutation invariants. The coefficients of these polynomials can be used as graph features which can be encoded in a vectorial manner. We extend this representation to graphs in which there are unary attributes on the nodes and binary attributes on the edges by using the spectral decomposition of a Hermitian property matrix that can be viewed as a complex analogue of the Laplacian. To embed the graphs in a pattern space, we explore whether the vectors of invariants can be embedded in a low- dimensional space using a number of alternative strategies, including principal components analysis ( PCA), multidimensional scaling ( MDS), and locality preserving projection ( LPP). Experimentally, we demonstrate that the embeddings result in well- defined graph clusters. Our experiments with the spectral representation involve both synthetic and real- world data. The experiments with synthetic data demonstrate that the distances between spectral feature vectors can be used to discriminate between graphs on the basis of their structure. The real- world experiments show that the method can be used to locate clusters of graphs
C2MS: Dynamic Monitoring and Management of Cloud Infrastructures
Server clustering is a common design principle employed by many organisations
who require high availability, scalability and easier management of their
infrastructure. Servers are typically clustered according to the service they
provide whether it be the application(s) installed, the role of the server or
server accessibility for example. In order to optimize performance, manage load
and maintain availability, servers may migrate from one cluster group to
another making it difficult for server monitoring tools to continuously monitor
these dynamically changing groups. Server monitoring tools are usually
statically configured and with any change of group membership requires manual
reconfiguration; an unreasonable task to undertake on large-scale cloud
infrastructures.
In this paper we present the Cloudlet Control and Management System (C2MS); a
system for monitoring and controlling dynamic groups of physical or virtual
servers within cloud infrastructures. The C2MS extends Ganglia - an open source
scalable system performance monitoring tool - by allowing system administrators
to define, monitor and modify server groups without the need for server
reconfiguration. In turn administrators can easily monitor group and individual
server metrics on large-scale dynamic cloud infrastructures where roles of
servers may change frequently. Furthermore, we complement group monitoring with
a control element allowing administrator-specified actions to be performed over
servers within service groups as well as introduce further customized
monitoring metrics. This paper outlines the design, implementation and
evaluation of the C2MS.Comment: Proceedings of the The 5th IEEE International Conference on Cloud
Computing Technology and Science (CloudCom 2013), 8 page
Information Flow Structure in Large-Scale Product Development Organizational Networks
In recent years, understanding the structure and function of complex networks has become the foundation for explaining many different real- world complex social, information, biological and technological phenomena. Techniques from statistical physics have been successfully applied to the analysis of these networks, and have uncovered surprising statistical structural properties that have also been shown to have a major effect on their functionality, dynamics, robustness, and fragility. This paper examines, for the first time, the statistical properties of strategically important complex organizational information-based networks -- networks of people engaged in distributed product development -- and discusses the significance of these properties in providing insight into ways of improving the strategic and operational decision-making of the organization. We show that the patterns of information flows that are at the heart of large-scale product development networks have properties that are like those displayed by information, biological and technological networks. We believe that our new analysis methodology and empirical results are also relevant to other organizational information-based human or nonhuman networks.Large-scale product development, socio-technical systems, information systems, social networks, Innovation, complex engineering systems, distributed problem solving
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