257 research outputs found
Metric projection for dynamic multiplex networks
Evolving multiplex networks are a powerful model for representing the
dynamics along time of different phenomena, such as social networks, power
grids, biological pathways. However, exploring the structure of the multiplex
network time series is still an open problem. Here we propose a two-steps
strategy to tackle this problem based on the concept of distance (metric)
between networks. Given a multiplex graph, first a network of networks is built
for each time steps, and then a real valued time series is obtained by the
sequence of (simple) networks by evaluating the distance from the first element
of the series. The effectiveness of this approach in detecting the occurring
changes along the original time series is shown on a synthetic example first,
and then on the Gulf dataset of political events
An introduction to spectral distances in networks (extended version)
Many functions have been recently defined to assess the similarity among
networks as tools for quantitative comparison. They stem from very different
frameworks - and they are tuned for dealing with different situations. Here we
show an overview of the spectral distances, highlighting their behavior in some
basic cases of static and dynamic synthetic and real networks
A unifying view for performance measures in multi-class prediction
In the last few years, many different performance measures have been
introduced to overcome the weakness of the most natural metric, the Accuracy.
Among them, Matthews Correlation Coefficient has recently gained popularity
among researchers not only in machine learning but also in several application
fields such as bioinformatics. Nonetheless, further novel functions are being
proposed in literature. We show that Confusion Entropy, a recently introduced
classifier performance measure for multi-class problems, has a strong
(monotone) relation with the multi-class generalization of a classical metric,
the Matthews Correlation Coefficient. Computational evidence in support of the
claim is provided, together with an outline of the theoretical explanation
Sparse Predictive Structure of Deconvolved Functional Brain Networks
The functional and structural representation of the brain as a complex
network is marked by the fact that the comparison of noisy and intrinsically
correlated high-dimensional structures between experimental conditions or
groups shuns typical mass univariate methods. Furthermore most network
estimation methods cannot distinguish between real and spurious correlation
arising from the convolution due to nodes' interaction, which thus introduces
additional noise in the data. We propose a machine learning pipeline aimed at
identifying multivariate differences between brain networks associated to
different experimental conditions. The pipeline (1) leverages the deconvolved
individual contribution of each edge and (2) maps the task into a sparse
classification problem in order to construct the associated "sparse deconvolved
predictive network", i.e., a graph with the same nodes of those compared but
whose edge weights are defined by their relevance for out of sample predictions
in classification. We present an application of the proposed method by decoding
the covert attention direction (left or right) based on the single-trial
functional connectivity matrix extracted from high-frequency
magnetoencephalography (MEG) data. Our results demonstrate how network
deconvolution matched with sparse classification methods outperforms typical
approaches for MEG decoding
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