Content-based networks: A pedagogical overview

Balcan, Duygu; Erzan, Ayşe

doi:10.1063/1.2743613

Cited by 11 publications

(8 citation statements)

References 36 publications

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“…Note that as N → ∞ we have f (x, y) = zxy in the sparse and subcritical regimes since zxy < z ≪ 1, which implies that we can neglect zxy in the denominator of eq. (7). Therefore the expression f (x, m) = f (x, m ) is exact.…”

Section: Fitness-dependent Complex Networkmentioning

confidence: 92%

Self-organized network evolution coupled to extremal dynamics

2007

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The interplay between topology and dynamics in complex networks is a fundamental but widely unexplored problem. Here, we study this phenomenon on a prototype model in which the network is shaped by a dynamical variable. We couple the dynamics of the Bak-Sneppen evolution model with the rules of the so-called fitness network model for establishing the topology of a network; each vertex is assigned a fitness, and the vertex with minimum fitness and its neighbours are updated in each iteration. At the same time, the links between the updated vertices and all other vertices are drawn anew with a fitness-dependent connection probability. We show analytically and numerically that the system self-organizes to a non-trivial state that differs from what is obtained when the two processes are decoupled. A power-law decay of dynamical and topological quantities above a threshold emerges spontaneously, as well as a feedback between different dynamical regimes and the underlying correlation and percolation properties of the network.Comment: Accepted version. Supplementary information at http://www.nature.com/nphys/journal/v3/n11/suppinfo/nphys729_S1.htm

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Section: Fitness-dependent Complex Networkmentioning

confidence: 92%

Self-organized network evolution coupled to extremal dynamics

2007

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“…Our formalism allows to extract niche values directly from empirical food webs, and not from ad hoc statistical distributions [19]. Another interesting application is to gene regulatory networks, where the length of regulatory sequences and promoter regions have been shown to determine the connection probability p ij [20]. Similarly, our approach allows to extract the vertex-specific quantities (such as expansiveness, actractiveness or mobility-related parameters) that are commonly assumed to determine the topology and community structure of social networks [12,13,21].…”

mentioning

confidence: 99%

Maximum likelihood: Extracting unbiased information from complex networks

2008

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The choice of free parameters in network models is subjective, since it depends on what topological properties are being monitored. However, we show that the maximum likelihood (ML) principle indicates a unique, statistically rigorous parameter choice, associated with a well-defined topological feature. We then find that, if the ML condition is incompatible with the built-in parameter choice, network models turn out to be intrinsically ill defined or biased. To overcome this problem, we construct a class of safely unbiased models. We also propose an extension of these results that leads to the fascinating possibility to extract, only from topological data, the "hidden variables" underlying network organization, making them "no longer hidden." We test our method on World Trade Web data, where we recover the empirical gross domestic product using only topological information.

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“…A more sophisticated approach that could be taken in the future is to impose the inferred I/O distribution as well (see e.g. [24,25]).…”

Section: Methodsmentioning

confidence: 99%

Information propagation within the Genetic Network of Saccharomyces cerevisiae

et al. 2010

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BackgroundA gene network's capacity to process information, so as to bind past events to future actions, depends on its structure and logic. From previous and new microarray measurements in Saccharomyces cerevisiae following gene deletions and overexpressions, we identify a core gene regulatory network (GRN) of functional interactions between 328 genes and the transfer functions of each gene. Inferred connections are verified by gene enrichment.ResultsWe find that this core network has a generalized clustering coefficient that is much higher than chance. The inferred Boolean transfer functions have a mean p-bias of 0.41, and thus similar amounts of activation and repression interactions. However, the distribution of p-biases differs significantly from what is expected by chance that, along with the high mean connectivity, is found to cause the core GRN of S. cerevisiae's to have an overall sensitivity similar to critical Boolean networks. In agreement, we find that the amount of information propagated between nodes in finite time series is much higher in the inferred core GRN of S. cerevisiae than what is expected by chance.ConclusionsWe suggest that S. cerevisiae is likely to have evolved a core GRN with enhanced information propagation among its genes.

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Content-based networks: A pedagogical overview

Cited by 11 publications

References 36 publications

Self-organized network evolution coupled to extremal dynamics

Self-organized network evolution coupled to extremal dynamics

Maximum likelihood: Extracting unbiased information from complex networks

Information propagation within the Genetic Network of Saccharomyces cerevisiae

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