5 research outputs found
Detecting Core-Periphery Structures by Surprise
Detecting the presence of mesoscale structures in complex networks is of
primary importance. This is especially true for financial networks, whose
structural organization deeply affects their resilience to events like default
cascades, shocks propagation, etc. Several methods have been proposed, so far,
to detect communities, i.e. groups of nodes whose connectivity is significantly
large. Communities, however do not represent the only kind of mesoscale
structures characterizing real-world networks: other examples are provided by
bow-tie structures, core-periphery structures and bipartite structures. Here we
propose a novel method to detect statistically-signifcant bimodular structures,
i.e. either bipartite or core-periphery ones. It is based on a modification of
the surprise, recently proposed for detecting communities. Our variant allows
for bimodular nodes partitions to be revealed, by letting links to be placed
either 1) within the core part and between the core and the periphery parts or
2) just between the (empty) layers of a bipartite network. From a technical
point of view, this is achieved by employing a multinomial hypergeometric
distribution instead of the traditional (binomial) hypergeometric one; as in
the latter case, this allows a p-value to be assigned to any given
(bi)partition of the nodes. To illustrate the performance of our method, we
report the results of its application to several real-world networks, including
social, economic and financial ones.Comment: 11 pages, 10 figures. Python code freely available at
https://github.com/jeroenvldj/bimodular_surpris
The Multilayer Structure of Corporate Networks
Various company interactions can be described by networks, for instance the
ownership networks and the board membership networks. To understand the
ecosystem of companies, these interactions cannot be seen in isolation. For
this purpose we construct a new multilayer network of interactions between
companies in Germany and in the United Kingdom, combining ownership links,
social ties through joint board directors, R\&D collaborations and stock
correlations in one linked multiplex dataset. We describe the features of this
network and show there exists a non-trivial overlap between these different
types of networks, where the different types of connections complement each
other and make the overall structure more complex. This highlights that
corporate control, boardroom influence and other connections have different
structures and together make an even smaller corporate world than previously
reported. We have a first look at the relation between company performance and
location in the network structure.Comment: 14 pages, 5 figures, 3 table
Reconstructing mesoscale network structures
When facing complex mesoscale network structures, it is generally believed
that (null) models encoding the modular organization of nodes must be employed.
The present paper focuses on two block structures that characterize the
mesoscale organization of many real-world networks, i.e. the bow-tie and the
core-periphery ones. Our analysis shows that constraining the network degree
sequence is often enough to reproduce such structures, as confirmed by model
selection criteria as AIC or BIC. As a byproduct, our paper enriches the
toolbox for the analysis of bipartite networks - still far from being complete.
The aforementioned structures, in fact, partition the networks into asymmetric
blocks characterized by binary, directed connections, thus calling for the
extension of a recently-proposed method to randomize undirected, bipartite
networks to the directed case.Comment: 13 pages, 7 figures, accepted by the journal Complexit
A new lot sizing and scheduling heuristic for multi-site biopharmaceutical production
Biopharmaceutical manufacturing requires high investments and long-term production planning. For large biopharmaceutical companies, planning typically involves multiple products and several production facilities. Production is usually done in batches with a substantial set-up cost and time for switching between products. The goal is to satisfy demand while minimising manufacturing, set-up and inventory costs. The resulting production planning problem is thus a variant of the capacitated lot-sizing and scheduling problem, and a complex combinatorial optimisation problem. Inspired by genetic algorithm approaches to job shop scheduling, this paper proposes a tailored construction heuristic that schedules demands of multiple products sequentially across several facilities to build a multi-year production plan (solution). The sequence in which the construction heuristic schedules the different demands is optimised by a genetic algorithm. We demonstrate the effectiveness of the approach on a biopharmaceutical lot sizing problem and compare it with a mathematical programming model from the literature. We show that the genetic algorithm can outperform the mathematical programming model for certain scenarios because the discretisation of time in mathematical programming artificially restricts the solution space