3 research outputs found
Exploring ordered patterns in the adjacency matrix for improving machine learning on complex networks
The use of complex networks as a modern approach to understanding the world
and its dynamics is well-established in literature. The adjacency matrix, which
provides a one-to-one representation of a complex network, can also yield
several metrics of the graph. However, it is not always clear that this
representation is unique, as the permutation of lines and rows in the matrix
can represent the same graph. To address this issue, the proposed methodology
employs a sorting algorithm to rearrange the elements of the adjacency matrix
of a complex graph in a specific order. The resulting sorted adjacency matrix
is then used as input for feature extraction and machine learning algorithms to
classify the networks. The results indicate that the proposed methodology
outperforms previous literature results on synthetic and real-world data.Comment: 12 pages, 10 figure
Social Interaction Layers in Complex Networks for the Dynamical Epidemic Modeling of COVID-19 in Brazil
We are currently living in a state of uncertainty due to the pandemic caused
by the Sars-CoV-2 virus. There are several factors involved in the epidemic
spreading such as the individual characteristics of each city/country. The true
shape of the epidemic dynamics is a large, complex system such as most of the
social systems. In this context, Complex networks are a great candidate to
analyze these systems due to their ability to tackle structural and dynamical
properties. Therefore this study presents a new approach to model the COVID-19
epidemic using a multi-layer complex network, where nodes represent people,
edges are social contacts, and layers represent different social activities.
The model improves the traditional SIR and it is applied to study the Brazilian
epidemic by analyzing possible future actions and their consequences. The
network is characterized using statistics of infection, death, and
hospitalization time. To simulate isolation, social distancing, or
precautionary measures we remove layers and/or reduce the intensity of social
contacts. Results show that even taking various optimistic assumptions, the
current isolation levels in Brazil still may lead to a critical scenario for
the healthcare system and a considerable death toll (average of 149,000). If
all activities return to normal, the epidemic growth may suffer a steep
increase, and the demand for ICU beds may surpass 3 times the country's
capacity. This would surely lead to a catastrophic scenario, as our estimation
reaches an average of 212,000 deaths even considering that all cases are
effectively treated. The increase of isolation (up to a lockdown) shows to be
the best option to keep the situation under the healthcare system capacity,
aside from ensuring a faster decrease of new case occurrences (months of
difference), and a significantly smaller death toll (average of 87,000).Comment: 16 pages, 7 figures, 2 table