17,237 research outputs found
Exploring the assortativity-clustering space of a network's degree sequence
Nowadays there is a multitude of measures designed to capture different
aspects of network structure. To be able to say if the structure of certain
network is expected or not, one needs a reference model (null model). One
frequently used null model is the ensemble of graphs with the same set of
degrees as the original network. In this paper we argue that this ensemble can
be more than just a null model -- it also carries information about the
original network and factors that affect its evolution. By mapping out this
ensemble in the space of some low-level network structure -- in our case those
measured by the assortativity and clustering coefficients -- one can for
example study how close to the valid region of the parameter space the observed
networks are. Such analysis suggests which quantities are actively optimized
during the evolution of the network. We use four very different biological
networks to exemplify our method. Among other things, we find that high
clustering might be a force in the evolution of protein interaction networks.
We also find that all four networks are conspicuously robust to both random
errors and targeted attacks
Effectiveness of dismantling strategies on moderated vs. unmoderated online social platforms
Online social networks are the perfect test bed to better understand
large-scale human behavior in interacting contexts. Although they are broadly
used and studied, little is known about how their terms of service and posting
rules affect the way users interact and information spreads. Acknowledging the
relation between network connectivity and functionality, we compare the
robustness of two different online social platforms, Twitter and Gab, with
respect to dismantling strategies based on the recursive censor of users
characterized by social prominence (degree) or intensity of inflammatory
content (sentiment). We find that the moderated (Twitter) vs unmoderated (Gab)
character of the network is not a discriminating factor for intervention
effectiveness. We find, however, that more complex strategies based upon the
combination of topological and content features may be effective for network
dismantling. Our results provide useful indications to design better strategies
for countervailing the production and dissemination of anti-social content in
online social platforms
ME-Net: Towards Effective Adversarial Robustness with Matrix Estimation
Deep neural networks are vulnerable to adversarial attacks. The literature is
rich with algorithms that can easily craft successful adversarial examples. In
contrast, the performance of defense techniques still lags behind. This paper
proposes ME-Net, a defense method that leverages matrix estimation (ME). In
ME-Net, images are preprocessed using two steps: first pixels are randomly
dropped from the image; then, the image is reconstructed using ME. We show that
this process destroys the adversarial structure of the noise, while
re-enforcing the global structure in the original image. Since humans typically
rely on such global structures in classifying images, the process makes the
network mode compatible with human perception. We conduct comprehensive
experiments on prevailing benchmarks such as MNIST, CIFAR-10, SVHN, and
Tiny-ImageNet. Comparing ME-Net with state-of-the-art defense mechanisms shows
that ME-Net consistently outperforms prior techniques, improving robustness
against both black-box and white-box attacks.Comment: ICML 201
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