207 research outputs found
Micro-Macro Analysis of Complex Networks
Complex systems have attracted considerable interest because of their wide range of applications, and are often studied via a \u201cclassic\u201d approach: study a specific system, find a complex network behind it, and analyze the corresponding properties. This simple methodology has produced a great deal of interesting results, but relies on an often implicit underlying assumption: the level of detail on which the system is observed. However, in many situations, physical or abstract, the level of detail can be one out of many, and might also depend on intrinsic limitations in viewing the data with a different level of abstraction or precision. So, a fundamental question arises: do properties of a network depend on its level of observability, or are they invariant? If there is a dependence, then an apparently correct network modeling could in fact just be a bad approximation of the true behavior of a complex system. In order to answer this question, we propose a novel micro-macro analysis of complex systems that quantitatively describes how the structure of complex networks varies as a function of the detail level. To this extent, we have developed a new telescopic algorithm that abstracts from the local properties of a system and reconstructs the original structure according to a fuzziness level. This way we can study what happens when passing from a fine level of detail (\u201cmicro\u201d) to a different scale level (\u201cmacro\u201d), and analyze the corresponding behavior in this transition, obtaining a deeper spectrum analysis. The obtained results show that many important properties are not universally invariant with respect to the level of detail, but instead strongly depend on the specific level on which a network is observed. Therefore, caution should be taken in every situation where a complex network is considered, if its context allows for different levels of observability
Towards a Rule Interchange Language for the Web
This articles discusses rule languages that are needed for a a
full deployment of the SemanticWeb. First, it motivates the need for such
languages. Then, it presents ten theses addressing (1) the rule and/or
logic languages needed on the Web, (2) data and data processing, (3)
semantics, and (4) engineering and rendering issues. Finally, it discusses
two options that might be chosen in designing a Rule Interchange Format
for the Web
Harmony in the Small-World
The Small-World phenomenon, popularly known as six degrees of separation, has
been mathematically formalized by Watts and Strogatz in a study of the
topological properties of a network. Small-worlds networks are defined in terms
of two quantities: they have a high clustering coefficient C like regular
lattices and a short characteristic path length L typical of random networks.
Physical distances are of fundamental importance in the applications to real
cases, nevertheless this basic ingredient is missing in the original
formulation. Here we introduce a new concept, the connectivity length D, that
gives harmony to the whole theory. D can be evaluated on a global and on a
local scale and plays in turn the role of L and 1/C. Moreover it can be
computed for any metrical network and not only for the topological cases. D has
a precise meaning in term of information propagation and describes in an
unified way both the structural and the dynamical aspects of a network:
small-worlds are defined by a small global and local D, i.e. by a high
efficiency in propagating information both on a local and on a global scale.
The neural system of the nematode C. elegans, the collaboration graph of film
actors, and the oldest U.S. subway system, can now be studied also as metrical
networks and are shown to be small-worlds.Comment: 16 pages, 3 figures, accepted for publication in Physica
The Architecture of Complex Systems
A short review of the recent results and models of complex networks.Comment: 8 pages, 1 figure. To appear on "Interdisciplinary Applications of
Ideas from Nonextensive Statistical Mechanics and Thermodynamics", Santa Fe
Institute for Studies of Complexity. Oxford University Pres
05371 Abstracts Collection -- Principles and Practices of Semantic Web Reasoning
From 11.09.05 to 16.09.05, the Dagstuhl Seminar 05371 ``Principles and Practices of Semantic Web Reasoning\u27\u27 % generate automaticall was held in the International Conference and Research Center (IBFI), Schloss Dagstuhl.
During the seminar, several participants presented their current
research, and ongoing work and open problems were discussed. Abstracts of
the presentations given during the seminar as well as abstracts of
seminar results and ideas are put together in this paper. The first section
describes the seminar topics and goals in general.
Links to extended abstracts or full papers are provided, if available
Economic Small-World Behavior in Weighted Networks
The small-world phenomenon has been already the subject of a huge variety of
papers, showing its appeareance in a variety of systems. However, some big
holes still remain to be filled, as the commonly adopted mathematical
formulation suffers from a variety of limitations, that make it unsuitable to
provide a general tool of analysis for real networks, and not just for
mathematical (topological) abstractions. In this paper we show where the major
problems arise, and how there is therefore the need for a new reformulation of
the small-world concept. Together with an analysis of the variables involved,
we then propose a new theory of small-world networks based on two leading
concepts: efficiency and cost. Efficiency measures how well information
propagates over the network, and cost measures how expensive it is to build a
network. The combination of these factors leads us to introduce the concept of
{\em economic small worlds}, that formalizes the idea of networks that are
"cheap" to build, and nevertheless efficient in propagating information, both
at global and local scale. This new concept is shown to overcome all the
limitations proper of the so-far commonly adopted formulation, and to provide
an adequate tool to quantitatively analyze the behaviour of complex networks in
the real world. Various complex systems are analyzed, ranging from the realm of
neural networks, to social sciences, to communication and transportation
networks. In each case, economic small worlds are found. Moreover, using the
economic small-world framework, the construction principles of these networks
can be quantitatively analyzed and compared, giving good insights on how
efficiency and economy principles combine up to shape all these systems.Comment: 17 pages, 10 figures, 4 table
Is the Boston subway a small-world network ?
The mathematical study of the small-world concept has fostered quite some
interest, showing that small-world features can be identified for some abstract
classes of networks. However, passing to real complex systems, as for instance
transportation networks, shows a number of new problems that make current
analysis impossible. In this paper we show how a more refined kind of analysis,
relying on transportation efficiency, can in fact be used to overcome such
problems, and to give precious insights on the general characteristics of real
transportation networks, eventually providing a picture where the small-world
comes back as underlying construction principle.Comment: 5 pages, 1 figure. To appear in Physica A, in a special issue devoted
to the Proceedings of the Conference "Horizons in Complex Systems", Messina
(Italy), Dec.5-Dec.8 200
Neural Network Models of Learning and Categorization in Multigame Experiments
Previous research has shown that regret-driven neural networks predict behavior in repeated completely mixed games remarkably well, substantially equating the performance of the most accurate established models of learning. This result prompts the question of what is the added value of modeling learning through neural networks. We submit that this modeling approach allows for models that are able to distinguish among and respond differently to different payoff structures. Moreover, the process of categorization of a game is implicitly carried out by these models, thus without the need of any external explicit theory of similarity between games. To validate our claims, we designed and ran two multigame experiments in which subjects faced, in random sequence, different instances of two completely mixed 2 × 2 games. Then, we tested on our experimental data two regret-driven neural network models, and compared their performance with that of other established models of learning and Nash equilibrium
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