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\chapter{Introduction}

\input{abstract}

A common usage of bipartite graphs in ecology is the representation of ecological
interactions like
plant-pollinator \parencite{ramos-jilibertoTopologicalChangeAndean2010},
birds-seed dispersion, prey-predator or host-parasite
\parencite{kaszewska-gilasGlobalStudiesHostParasite2021}. For plant-pollinator
interactions, the rows are pollinator species and the columns are plant
species,
and the intersection is a value, binary if it is a presence/absence or a value
if it is an abundance count.

Some interesting results can arise when applying a tool widely used on a
particular kind of interactions is used on another kind of interactions. In
~\cite{desjardins-proulxEcologicalInteractionsNetflix2017} the authors use the
\emph{K-nearest neighbour} (KNN) algorithm as a Recommender to predict missing
preys for predators in a predator-prey network.\\

Bipartite graphs are widely used in biology in general, in various fields, among
which the previously cited ecological networks, but also in medicine with
biomedical networks, biomolecular networks or epidemiological networks.
\parencite{pavlopoulosBipartiteGraphsSystems2018}

There is a need for comparison methods of bipartite networks in literature, and
it is being actively developed,
e.g.~\cite{pichonTellingMutualisticAntagonistic2024} use structures at multiple
scales (species degree, motif frequency, nestedness \dots) to tell apart
mutualistic and antagonistic networks.

This motivates us to propose a model for structure detection in bipartite
collections.

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