87 research outputs found
Search in Complex Networks : a New Method of Naming
We suggest a method for routing when the source does not posses full
information about the shortest path to the destination. The method is
particularly useful for scale-free networks, and exploits its unique
characteristics. By assigning new (short) names to nodes (aka labelling) we are
able to reduce significantly the memory requirement at the routers, yet we
succeed in routing with high probability through paths very close in distance
to the shortest ones.Comment: 5 pages, 4 figure
Heat Exchanger Tube to Tube Sheet Joints Corrosion Behavior
Paper presents the studies made by the authors above the tube to tube sheet fittings of heat exchanger with fixed covers from hydrofining oil reforming unit. Tube fittings are critical zones for heat exchangers failures. On a device made from material tube and tube sheet at real joints dimensions were establish axial compression force and traction force at which tube is extracted from expanded joint. Were used two shapes joints with two types of fittings surfaces, one with smooth hole of tube sheet and other in which on boring surface we made a groove. From extracted expanded tube zones were made samples for corrosion tests in order to establish the corrosion rate, corrosion potential and corrosion current in working mediums such as hydrofining oil and industrial water at different temperatures. The corrosion rate values and the temperature influence are important to evaluate joints durability and also the results obtained shows that the boring tube sheet shape with a groove on hole tube shape presents a better corrosion behavior then the shape with smooth hole tube sheet
The Directed Dominating Set Problem: Generalized Leaf Removal and Belief Propagation
A minimum dominating set for a digraph (directed graph) is a smallest set of
vertices such that each vertex either belongs to this set or has at least one
parent vertex in this set. We solve this hard combinatorial optimization
problem approximately by a local algorithm of generalized leaf removal and by a
message-passing algorithm of belief propagation. These algorithms can construct
near-optimal dominating sets or even exact minimum dominating sets for random
digraphs and also for real-world digraph instances. We further develop a core
percolation theory and a replica-symmetric spin glass theory for this problem.
Our algorithmic and theoretical results may facilitate applications of
dominating sets to various network problems involving directed interactions.Comment: 11 pages, 3 figures in EPS forma
The structure and function of complex networks
Inspired by empirical studies of networked systems such as the Internet,
social networks, and biological networks, researchers have in recent years
developed a variety of techniques and models to help us understand or predict
the behavior of these systems. Here we review developments in this field,
including such concepts as the small-world effect, degree distributions,
clustering, network correlations, random graph models, models of network growth
and preferential attachment, and dynamical processes taking place on networks.Comment: Review article, 58 pages, 16 figures, 3 tables, 429 references,
published in SIAM Review (2003
A shadowing problem in the detection of overlapping communities: lifting the resolution limit through a cascading procedure
Community detection is the process of assigning nodes and links in
significant communities (e.g. clusters, function modules) and its development
has led to a better understanding of complex networks. When applied to sizable
networks, we argue that most detection algorithms correctly identify prominent
communities, but fail to do so across multiple scales. As a result, a
significant fraction of the network is left uncharted. We show that this
problem stems from larger or denser communities overshadowing smaller or
sparser ones, and that this effect accounts for most of the undetected
communities and unassigned links. We propose a generic cascading approach to
community detection that circumvents the problem. Using real and artificial
network datasets with three widely used community detection algorithms, we show
how a simple cascading procedure allows for the detection of the missing
communities. This work highlights a new detection limit of community structure,
and we hope that our approach can inspire better community detection
algorithms.Comment: 14 pages, 12 figures + supporting information (5 pages, 6 tables, 3
figures
Folksonomies and clustering in the collaborative system CiteULike
We analyze CiteULike, an online collaborative tagging system where users
bookmark and annotate scientific papers. Such a system can be naturally
represented as a tripartite graph whose nodes represent papers, users and tags
connected by individual tag assignments. The semantics of tags is studied here,
in order to uncover the hidden relationships between tags. We find that the
clustering coefficient reflects the semantical patterns among tags, providing
useful ideas for the designing of more efficient methods of data classification
and spam detection.Comment: 9 pages, 5 figures, iop style; corrected typo
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
A distributed collaborative platform for personal health profiles in patient-driven health social network
Health social networks (HSNs) have become an integral part of healthcare to augment the ability of people to communicate, collaborate, and share information in the healthcare domain despite obstacles of geography and time. Doctors disseminate relevant medical updates in these platforms and patients take into account opinions of strangers when making medical decisions. This paper introduces our efforts to develop a core platform called Distributed Platform for Health Profiles (DPHP) that enables individuals or groups to control their personal health profiles. DPHP stores user's personal health profiles in a non-proprietary manner which will enable healthcare providers and pharmaceutical companies to reuse these profiles in parallel in order to maximize the effort where users benefit from each usage for their personal health profiles. DPHP also facilitates the selection of appropriate data aggregators and assessing their offered datasets in an autonomous way. Experimental results were described to demonstrate the proposed search model in DPHP. Multiple advantages might arise when healthcare providers utilize DPHP to collect data for various data analysis techniques in order to improve the clinical diagnosis and the efficiency measurement for some medications in treating certain diseases
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