218,497 research outputs found
Communities recognition in the Chesapeake Bay ecosystem by dynamical clustering algorithms based on different oscillators systems
We have recently introduced an efficient method for the detection and
identification of modules in complex networks, based on the de-synchronization
properties (dynamical clustering) of phase oscillators. In this paper we apply
the dynamical clustering tecnique to the identification of communities of
marine organisms living in the Chesapeake Bay food web. We show that our
algorithm is able to perform a very reliable classification of the real
communities existing in this ecosystem by using different kinds of dynamical
oscillators. We compare also our results with those of other methods for the
detection of community structures in complex networks.Comment: 8 pages, 7 figures, Proceedings of the International Workshop on
"Ecological Complex Systems: Stochastic Dynamics and Patterns", 22-26 July
2007 - Terrasini (Palermo), Ital
An active, ontology-driven network service for Internet collaboration
Web portals have emerged as an important means of collaboration on the WWW, and the integration of ontologies promises to make them more accurate in how they serve usersâ collaboration and information location requirements. However, web portals are essentially a centralised architecture resulting in difficulties supporting seamless roaming between portals and collaboration between groups supported on different portals. This paper proposes an alternative approach to collaboration over the web using ontologies that is de-centralised and exploits content-based networking. We argue that this approach promises a user-centric, timely, secure and location-independent mechanism, which is potentially more scaleable and universal than existing centralised portals
A schema-based P2P network to enable publish-subscribe for multimedia content in open hypermedia systems
Open Hypermedia Systems (OHS) aim to provide efficient dissemination, adaptation and integration of hyperlinked multimedia resources. Content available in Peer-to-Peer (P2P) networks could add significant value to OHS provided that challenges for efficient discovery and prompt delivery of rich and up-to-date content are successfully addressed. This paper proposes an architecture that enables the operation of OHS over a P2P overlay network of OHS servers based on semantic annotation of (a) peer OHS servers and of (b) multimedia resources that can be obtained through the link services of the OHS. The architecture provides efficient resource discovery. Semantic query-based subscriptions over this P2P network can enable access to up-to-date content, while caching at certain peers enables prompt delivery of multimedia content. Advanced query resolution techniques are employed to match different parts of subscription queries (subqueries). These subscriptions can be shared among different interested peers, thus increasing the efficiency of multimedia content dissemination
Detection of Complex Networks Modularity by Dynamical Clustering
Based on cluster de-synchronization properties of phase oscillators, we
introduce an efficient method for the detection and identification of modules
in complex networks. The performance of the algorithm is tested on computer
generated and real-world networks whose modular structure is already known or
has been studied by means of other methods. The algorithm attains a high level
of precision, especially when the modular units are very mixed and hardly
detectable by the other methods, with a computational effort on
a generic graph with nodes and links.Comment: 5 pages, 2 figures. Version accepted for publication on PRE Rapid
Communications: figures changed and text adde
Identifying network communities with a high resolution
Community structure is an important property of complex networks. An
automatic discovery of such structure is a fundamental task in many
disciplines, including sociology, biology, engineering, and computer science.
Recently, several community discovery algorithms have been proposed based on
the optimization of a quantity called modularity (Q). However, the problem of
modularity optimization is NP-hard, and the existing approaches often suffer
from prohibitively long running time or poor quality. Furthermore, it has been
recently pointed out that algorithms based on optimizing Q will have a
resolution limit, i.e., communities below a certain scale may not be detected.
In this research, we first propose an efficient heuristic algorithm, Qcut,
which combines spectral graph partitioning and local search to optimize Q.
Using both synthetic and real networks, we show that Qcut can find higher
modularities and is more scalable than the existing algorithms. Furthermore,
using Qcut as an essential component, we propose a recursive algorithm, HQcut,
to solve the resolution limit problem. We show that HQcut can successfully
detect communities at a much finer scale and with a higher accuracy than the
existing algorithms. Finally, we apply Qcut and HQcut to study a
protein-protein interaction network, and show that the combination of the two
algorithms can reveal interesting biological results that may be otherwise
undetectable.Comment: 14 pages, 5 figures. 1 supplemental file at
http://cic.cs.wustl.edu/qcut/supplemental.pd
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