28,582 research outputs found
Entangled networks, synchronization, and optimal network topology
A new family of graphs, {\it entangled networks}, with optimal properties in
many respects, is introduced. By definition, their topology is such that
optimizes synchronizability for many dynamical processes. These networks are
shown to have an extremely homogeneous structure: degree, node-distance,
betweenness, and loop distributions are all very narrow. Also, they are
characterized by a very interwoven (entangled) structure with short average
distances, large loops, and no well-defined community-structure. This family of
nets exhibits an excellent performance with respect to other flow properties
such as robustness against errors and attacks, minimal first-passage time of
random walks, efficient communication, etc. These remarkable features convert
entangled networks in a useful concept, optimal or almost-optimal in many
senses, and with plenty of potential applications computer science or
neuroscience.Comment: Slightly modified version, as accepted in Phys. Rev. Let
Practical Attacks Against Graph-based Clustering
Graph modeling allows numerous security problems to be tackled in a general
way, however, little work has been done to understand their ability to
withstand adversarial attacks. We design and evaluate two novel graph attacks
against a state-of-the-art network-level, graph-based detection system. Our
work highlights areas in adversarial machine learning that have not yet been
addressed, specifically: graph-based clustering techniques, and a global
feature space where realistic attackers without perfect knowledge must be
accounted for (by the defenders) in order to be practical. Even though less
informed attackers can evade graph clustering with low cost, we show that some
practical defenses are possible.Comment: ACM CCS 201
Generalized Network Dismantling
Finding the set of nodes, which removed or (de)activated can stop the spread
of (dis)information, contain an epidemic or disrupt the functioning of a
corrupt/criminal organization is still one of the key challenges in network
science. In this paper, we introduce the generalized network dismantling
problem, which aims to find the set of nodes that, when removed from a network,
results in a network fragmentation into subcritical network components at
minimum cost. For unit costs, our formulation becomes equivalent to the
standard network dismantling problem. Our non-unit cost generalization allows
for the inclusion of topological cost functions related to node centrality and
non-topological features such as the price, protection level or even social
value of a node. In order to solve this optimization problem, we propose a
method, which is based on the spectral properties of a novel node-weighted
Laplacian operator. The proposed method is applicable to large-scale networks
with millions of nodes. It outperforms current state-of-the-art methods and
opens new directions in understanding the vulnerability and robustness of
complex systems.Comment: 6 pages, 5 figure
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