70 research outputs found
Polynomial algorithms for p-dispersion problems in a 2d Pareto Front
Having many best compromise solutions for bi-objective optimization problems,
this paper studies p-dispersion problems to select
representative points in the Pareto Front(PF). Four standard variants of
p-dispersion are considered. A novel variant, denoted Max-Sum-Neighbor
p-dispersion, is introduced for the specific case of a 2d PF. Firstly, it is
proven that -dispersion and -dispersion problems are solvable in
time in a 2d PF. Secondly, dynamic programming algorithms are designed for
three p-dispersion variants, proving polynomial complexities in a 2d PF. The
Max-Min p-dispersion problem is proven solvable in time and
memory space. The Max-Sum-Min p-dispersion problem is proven solvable in
time and space. The Max-Sum-Neighbor p-dispersion problem
is proven solvable in time and space. Complexity results and
parallelization issues are discussed in regards to practical implementation
An Optimization-based Approach To Node Role Discovery in Networks: Approximating Equitable Partitions
Similar to community detection, partitioning the nodes of a network according
to their structural roles aims to identify fundamental building blocks of a
network. The found partitions can be used, e.g., to simplify descriptions of
the network connectivity, to derive reduced order models for dynamical
processes unfolding on processes, or as ingredients for various graph mining
tasks. In this work, we offer a fresh look on the problem of role extraction
and its differences to community detection and present a definition of node
roles related to graph-isomorphism tests, the Weisfeiler-Leman algorithm and
equitable partitions. We study two associated optimization problems (cost
functions) grounded in ideas from graph isomorphism testing, and present
theoretical guarantees associated to the solutions of these problems. Finally,
we validate our approach via a novel "role-infused partition benchmark", a
network model from which we can sample networks in which nodes are endowed with
different roles in a stochastic way
Information Geometry
This Special Issue of the journal Entropy, titled āInformation Geometry Iā, contains a collection of 17 papers concerning the foundations and applications of information geometry. Based on a geometrical interpretation of probability, information geometry has become a rich mathematical field employing the methods of differential geometry. It has numerous applications to data science, physics, and neuroscience. Presenting original research, yet written in an accessible, tutorial style, this collection of papers will be useful for scientists who are new to the field, while providing an excellent reference for the more experienced researcher. Several papers are written by authorities in the field, and topics cover the foundations of information geometry, as well as applications to statistics, Bayesian inference, machine learning, complex systems, physics, and neuroscience
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