2,421 research outputs found
Link and subgraph likelihoods in random undirected networks with fixed and partially fixed degree sequence
The simplest null models for networks, used to distinguish significant
features of a particular network from {\it a priori} expected features, are
random ensembles with the degree sequence fixed by the specific network of
interest. These "fixed degree sequence" (FDS) ensembles are, however, famously
resistant to analytic attack. In this paper we introduce ensembles with
partially-fixed degree sequences (PFDS) and compare analytic results obtained
for them with Monte Carlo results for the FDS ensemble. These results include
link likelihoods, subgraph likelihoods, and degree correlations. We find that
local structural features in the FDS ensemble can be reasonably well estimated
by simultaneously fixing only the degrees of few nodes, in addition to the
total number of nodes and links. As test cases we use a food web, two protein
interaction networks (\textit{E. coli, S. cerevisiae}), the internet on the
autonomous system (AS) level, and the World Wide Web. Fixing just the degrees
of two nodes gives the mean neighbor degree as a function of node degree,
, in agreement with results explicitly obtained from rewiring. For
power law degree distributions, we derive the disassortativity analytically. In
the PFDS ensemble the partition function can be expanded diagrammatically. We
obtain an explicit expression for the link likelihood to lowest order, which
reduces in the limit of large, sparse undirected networks with links and
with to the simple formula . In a
similar limit, the probability for three nodes to be linked into a triangle
reduces to the factorized expression .Comment: 17 pages, includes 11 figures; first revision: shortened to 14 pages
(7 figures), added discussion of subgraph counts, deleted discussion of
directed network
Model-free reconstruction of neuronal network connectivity from calcium imaging signals
A systematic assessment of global neural network connectivity through direct
electrophysiological assays has remained technically unfeasible even in
dissociated neuronal cultures. We introduce an improved algorithmic approach
based on Transfer Entropy to reconstruct approximations to network structural
connectivities from network activity monitored through calcium fluorescence
imaging. Based on information theory, our method requires no prior assumptions
on the statistics of neuronal firing and neuronal connections. The performance
of our algorithm is benchmarked on surrogate time-series of calcium
fluorescence generated by the simulated dynamics of a network with known
ground-truth topology. We find that the effective network topology revealed by
Transfer Entropy depends qualitatively on the time-dependent dynamic state of
the network (e.g., bursting or non-bursting). We thus demonstrate how
conditioning with respect to the global mean activity improves the performance
of our method. [...] Compared to other reconstruction strategies such as
cross-correlation or Granger Causality methods, our method based on improved
Transfer Entropy is remarkably more accurate. In particular, it provides a good
reconstruction of the network clustering coefficient, allowing to discriminate
between weakly or strongly clustered topologies, whereas on the other hand an
approach based on cross-correlations would invariantly detect artificially high
levels of clustering. Finally, we present the applicability of our method to
real recordings of in vitro cortical cultures. We demonstrate that these
networks are characterized by an elevated level of clustering compared to a
random graph (although not extreme) and by a markedly non-local connectivity.Comment: 54 pages, 8 figures (+9 supplementary figures), 1 table; submitted
for publicatio
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