14,471 research outputs found
Markov Network Structure Learning via Ensemble-of-Forests Models
Real world systems typically feature a variety of different dependency types
and topologies that complicate model selection for probabilistic graphical
models. We introduce the ensemble-of-forests model, a generalization of the
ensemble-of-trees model. Our model enables structure learning of Markov random
fields (MRF) with multiple connected components and arbitrary potentials. We
present two approximate inference techniques for this model and demonstrate
their performance on synthetic data. Our results suggest that the
ensemble-of-forests approach can accurately recover sparse, possibly
disconnected MRF topologies, even in presence of non-Gaussian dependencies
and/or low sample size. We applied the ensemble-of-forests model to learn the
structure of perturbed signaling networks of immune cells and found that these
frequently exhibit non-Gaussian dependencies with disconnected MRF topologies.
In summary, we expect that the ensemble-of-forests model will enable MRF
structure learning in other high dimensional real world settings that are
governed by non-trivial dependencies.Comment: 13 pages, 6 figure
Stability of graph communities across time scales
The complexity of biological, social and engineering networks makes it
desirable to find natural partitions into communities that can act as
simplified descriptions and provide insight into the structure and function of
the overall system. Although community detection methods abound, there is a
lack of consensus on how to quantify and rank the quality of partitions. We
show here that the quality of a partition can be measured in terms of its
stability, defined in terms of the clustered autocovariance of a Markov process
taking place on the graph. Because the stability has an intrinsic dependence on
time scales of the graph, it allows us to compare and rank partitions at each
time and also to establish the time spans over which partitions are optimal.
Hence the Markov time acts effectively as an intrinsic resolution parameter
that establishes a hierarchy of increasingly coarser clusterings. Within our
framework we can then provide a unifying view of several standard partitioning
measures: modularity and normalized cut size can be interpreted as one-step
time measures, whereas Fiedler's spectral clustering emerges at long times. We
apply our method to characterize the relevance and persistence of partitions
over time for constructive and real networks, including hierarchical graphs and
social networks. We also obtain reduced descriptions for atomic level protein
structures over different time scales.Comment: submitted; updated bibliography from v
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