Results 1  10
of
162
Community detection in graphs
, 2009
"... The modern science of networks has brought significant advances to our understanding of complex systems. One of the most relevant features of graphs representing real systems is community structure, or clustering, i. e. the organization of vertices in clusters, with many edges joining vertices of th ..."
Abstract

Cited by 801 (1 self)
 Add to MetaCart
The modern science of networks has brought significant advances to our understanding of complex systems. One of the most relevant features of graphs representing real systems is community structure, or clustering, i. e. the organization of vertices in clusters, with many edges joining vertices of the same cluster and comparatively few edges joining vertices of different clusters. Such
Transmission of olfactory information between three populations of neurons in the antennal lobe of the fly
 Neuron
"... Three classes of neurons form synapses in the antennal lobe of Drosophila, the insect counterpart of the vertebrate olfactory bulb: olfactory receptor neurons, projection neurons, and inhibitory local interneurons. We have targeted a genetically encoded optical reporter of synaptic transmission to ..."
Abstract

Cited by 45 (0 self)
 Add to MetaCart
Three classes of neurons form synapses in the antennal lobe of Drosophila, the insect counterpart of the vertebrate olfactory bulb: olfactory receptor neurons, projection neurons, and inhibitory local interneurons. We have targeted a genetically encoded optical reporter of synaptic transmission to each of these classes of neurons and visualized population responses to natural odors. The activation of an odorspecific ensemble of olfactory receptor neurons leads to the activation of a symmetric ensemble of projection neurons across the glomerular synaptic relay. Virtually all excited glomeruli receive inhibitory input from local interneurons. The extent, odor specificity, and partly interglomerular origin of this input suggest that inhibitory circuits assemble combinatorially during odor presentations. These circuits may serve as dynamic templates that extract higher order features from afferent activity patterns.
Information Consensus of Asynchronous Discretetime Multiagent Systems
, 2005
"... This paper studies the consensus problem of multiagent systems in an asynchronous framework. Under certain assumptions, the consensus protocol leads to stable behaviors even if the updating instants and sets of the agents are asynchronously determined. The model of asynchronous multiagent systems ..."
Abstract

Cited by 25 (1 self)
 Add to MetaCart
This paper studies the consensus problem of multiagent systems in an asynchronous framework. Under certain assumptions, the consensus protocol leads to stable behaviors even if the updating instants and sets of the agents are asynchronously determined. The model of asynchronous multiagent systems encompasses those synchronous ones with various communication patterns, i.e., issues of directional, delayed, or failed communication can be addressed in the same framework. The asynchronous results in this paper thus shed new light on the synchronous results reported in the literature. In particular, synchronous protocols under dynamically changing interaction topologies can be seen as a special case of the asynchronous protocol where all communication delays are zero.
Network synchronization: Spectral versus statistical properties
 Physica D
"... We consider synchronization of weighted networks, possibly with asymmetrical connections. We show that the synchronizability of the networks cannot be directly inferred from their statistical properties. Small local changes in the network structure can sensitively affect the eigenvalues relevant for ..."
Abstract

Cited by 17 (2 self)
 Add to MetaCart
(Show Context)
We consider synchronization of weighted networks, possibly with asymmetrical connections. We show that the synchronizability of the networks cannot be directly inferred from their statistical properties. Small local changes in the network structure can sensitively affect the eigenvalues relevant for synchronization, while the gross statistical network properties remain essentially unchanged. Consequently, commonly used statistical properties, including the degree distribution, degree homogeneity, average degree, average distance, degree correlation, and clustering coefficient, can fail to characterize the synchronizability of networks.
Synchronization of networks with prescribed degree distributions
, 2005
"... We show that the degree distributions of graphs do not suffice to characterize the synchronization of systems evolving on them. We prove that, for any given degree sequence satisfying certain conditions, there exists a connected graph having that degree sequence for which the first nontrivial eigenv ..."
Abstract

Cited by 16 (2 self)
 Add to MetaCart
We show that the degree distributions of graphs do not suffice to characterize the synchronization of systems evolving on them. We prove that, for any given degree sequence satisfying certain conditions, there exists a connected graph having that degree sequence for which the first nontrivial eigenvalue of the graph Laplacian is arbitrarily close to zero. Consequently, complex dynamical systems defined on such graphs have poor synchronization properties. The result holds under quite mild assumptions, and shows that there exists classes of random, scalefree, regular, smallworld, and other common network architectures which impede synchronization. The proof is based on a construction that also serves as an algorithm for building nonsynchronizing networks having a prescribed degree distribution.
Eigenvalue decomposition as a generalized synchronization cluster analysis
 Int. J. Bifur. Chaos
, 2007
"... Motivated by the recent demonstration of its use as a tool for the detection and characterization of phaseshape correlations in multivariate time series, we show that eigenvalue decomposition can also be applied to a matrix of indices of bivariate phase synchronization strength. The resulting metho ..."
Abstract

Cited by 10 (0 self)
 Add to MetaCart
(Show Context)
Motivated by the recent demonstration of its use as a tool for the detection and characterization of phaseshape correlations in multivariate time series, we show that eigenvalue decomposition can also be applied to a matrix of indices of bivariate phase synchronization strength. The resulting method is able to identify clusters of synchronized oscillators, and to quantify their strength as well as the degree of involvement of an oscillator in a cluster. Since for the case of a single cluster the method gives similar results as our previous approach, it can be seen as a generalized Synchronization Cluster Analysis, extending its field of application to more complex situations. The performance of the method is tested by applying it to simulation data.