13,237 research outputs found
Local module identification in dynamic networks with correlated noise: the full input case
The identification of local modules in dynamic networks with known topology
has recently been addressed by formulating conditions for arriving at
consistent estimates of the module dynamics, typically under the assumption of
having disturbances that are uncorrelated over the different nodes. The
conditions typically reflect the selection of a set of node signals that are
taken as predictor inputs in a MISO identification setup. In this paper an
extension is made to arrive at an identification setup for the situation that
process noises on the different node signals can be correlated with each other.
In this situation the local module may need to be embedded in a MIMO
identification setup for arriving at a consistent estimate with maximum
likelihood properties. This requires the proper treatment of confounding
variables. The result is an algorithm that, based on the given network topology
and disturbance correlation structure, selects an appropriate set of node
signals as predictor inputs and outputs in a MISO or MIMO identification setup.
As a first step in the analysis, we restrict attention to the (slightly
conservative) situation where the selected output node signals are predicted
based on all of their in-neighbor node signals in the network.Comment: Extended version of paper submitted to the 58th IEEE Conf. Decision
and Control, Nice, 201
Immunization of networks with community structure
In this study, an efficient method to immunize modular networks (i.e.,
networks with community structure) is proposed. The immunization of networks
aims at fragmenting networks into small parts with a small number of removed
nodes. Its applications include prevention of epidemic spreading, intentional
attacks on networks, and conservation of ecosystems. Although preferential
immunization of hubs is efficient, good immunization strategies for modular
networks have not been established. On the basis of an immunization strategy
based on the eigenvector centrality, we develop an analytical framework for
immunizing modular networks. To this end, we quantify the contribution of each
node to the connectivity in a coarse-grained network among modules. We verify
the effectiveness of the proposed method by applying it to model and real
networks with modular structure.Comment: 3 figures, 1 tabl
A Minimal Architecture for General Cognition
A minimalistic cognitive architecture called MANIC is presented. The MANIC
architecture requires only three function approximating models, and one state
machine. Even with so few major components, it is theoretically sufficient to
achieve functional equivalence with all other cognitive architectures, and can
be practically trained. Instead of seeking to transfer architectural
inspiration from biology into artificial intelligence, MANIC seeks to minimize
novelty and follow the most well-established constructs that have evolved
within various sub-fields of data science. From this perspective, MANIC offers
an alternate approach to a long-standing objective of artificial intelligence.
This paper provides a theoretical analysis of the MANIC architecture.Comment: 8 pages, 8 figures, conference, Proceedings of the 2015 International
Joint Conference on Neural Network
Theories for influencer identification in complex networks
In social and biological systems, the structural heterogeneity of interaction
networks gives rise to the emergence of a small set of influential nodes, or
influencers, in a series of dynamical processes. Although much smaller than the
entire network, these influencers were observed to be able to shape the
collective dynamics of large populations in different contexts. As such, the
successful identification of influencers should have profound implications in
various real-world spreading dynamics such as viral marketing, epidemic
outbreaks and cascading failure. In this chapter, we first summarize the
centrality-based approach in finding single influencers in complex networks,
and then discuss the more complicated problem of locating multiple influencers
from a collective point of view. Progress rooted in collective influence
theory, belief-propagation and computer science will be presented. Finally, we
present some applications of influencer identification in diverse real-world
systems, including online social platforms, scientific publication, brain
networks and socioeconomic systems.Comment: 24 pages, 6 figure
Feedbacks from the metabolic network to the genetic network reveal regulatory modules in E. coli and B. subtilis
The genetic regulatory network (GRN) plays a key role in controlling the
response of the cell to changes in the environment. Although the structure of
GRNs has been the subject of many studies, their large scale structure in the
light of feedbacks from the metabolic network (MN) has received relatively
little attention. Here we study the causal structure of the GRNs, namely the
chain of influence of one component on the other, taking into account feedback
from the MN. First we consider the GRNs of E. coli and B. subtilis without
feedback from MN and illustrate their causal structure. Next we augment the
GRNs with feedback from their respective MNs by including (a) links from genes
coding for enzymes to metabolites produced or consumed in reactions catalyzed
by those enzymes and (b) links from metabolites to genes coding for
transcription factors whose transcriptional activity the metabolites alter by
binding to them. We find that the inclusion of feedback from MN into GRN
significantly affects its causal structure, in particular the number of levels
and relative positions of nodes in the hierarchy, and the number and size of
the strongly connected components (SCCs). We then study the functional
significance of the SCCs. For this we identify condition specific feedbacks
from the MN into the GRN by retaining only those enzymes that are essential for
growth in specific environmental conditions simulated via the technique of flux
balance analysis (FBA). We find that the SCCs of the GRN augmented by these
feedbacks can be ascribed specific functional roles in the organism. Our
algorithmic approach thus reveals relatively autonomous subsystems with
specific functionality, or regulatory modules in the organism. This automated
approach could be useful in identifying biologically relevant modules in other
organisms for which network data is available, but whose biology is less well
studied.Comment: 15 figure
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