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Nonlinear opinion models and other networked systems
Networks play a critical role in many physical, biological, and social systems. In this thesis, we investigate tools to model and analyze networked systems. We first examine some of the ways in which we can model social dynamics that take place on networks. We then study two recently developed data-analysis methods that employ a network framework and explore new ways in which they can be used to find meaningful signals in large data sets. In the first half of the thesis, we study opinion dynamics on networks. We begin by examining a class of opinion models, known as coevolving voter models (CVM), that couple the mechanisms of opinion formation and changing social connections. We then propose a version of CVMs that incorporates nonlinearity. In our models, we assume that individuals strive to achieve harmony and avoid disagreement, both by changing their social connections to reflect their opinions and by changing their opinions to reflect their social connections. By taking a minimalist approach to modeling social dynamics, we hope to gain a deeper understanding of how these two mechanisms can give rise to social phenomena such as the ``majority illusion''. Comparing several versions of CVMs, we find that seemingly small changes in update rules can lead to strikingly different behaviors. A particularly interesting feature of our nonlinear CVMs is that, under certain conditions, the opinion state that is held initially by a minority of the nodes can effectively spread to almost every node in a network if the minority nodes view themselves as the majority. We then discuss an ongoing project that involves another class of opinion models called bounded-confidence models. Specifically, we examine extensions of bounded-confidence models on hypergraphs and discuss some preliminary findings. In the second half of the thesis, we study problems in data analysis. We begin by considering topological structures as a tool to study integrated circuit (IC) devices. In particular, we examine a problem in the design and manufacturing of IC devices using topological data analysis (TDA), which is based on network structures called simplicial complexes. Failures in IC devices generally occur near the tolerance limits of photolithography systems, such as at the minimum separation distance between adjacent electronic components. However, for complex arrangements of electronic components, simply ensuring minimal separation is insufficient to guarantee that one can manufacture an IC design accurately and reliably. We apply tools from TDA to compare data from IC designs. Without inputting domain knowledge, we are able to infer several results about the IC design-manufacturing process. Finally, we discuss an ongoing project in the analysis of network data. Specifically, we explore applications of a recently developed algorithm called network dictionary learning (NDL) and discuss problems of network reconstruction and denoising using NDL on both synthetic and real-world networks
Predicting Emerging Trends on Social Media by Modeling it as Temporal Bipartite Networks
The behavior of peoples' request for a post on online social media is a stochastic process that makes post's ranking highly skewed in nature. We mean peoples interest for a post can grow/decay exponentially or linearly. Considering this nature of the evolutionary peoples' interest, this paper presents a Growth-based Popularity Predictor (GPP) model for predicting and ranking the web-contents. Three different kinds of web-based real datasets namely Movielens, Facebook-wall-post and Digg are used to evaluate the performance of the proposed model. This performance is measured based on four information-retrieval metrics Area Under receiving operating Characteristic (AUC), Novelty, Precision, and Kendal's Tau. The obtained results show that the prediction performance can be further improved if the score is mapped onto a cumulative predicted item's ranking.https://doi.org/10.1109/ACCESS.2020.297613
On Dynamic Consensus Processes in Group Decision Making Problems
The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Consensus in group decision making requires discussion and deliberation between the group members with the aim to reach a decision that reflects the opinions of every group member in order for it to be acceptable by everyone. Traditionally, the consensus reaching problem is theoretically modelled as a multi stage negotiation process, i.e. an iterative process with a number of negotiation rounds, which ends when the consensus level achieved reaches a minimum required threshold value. In real world decision situations, both the consensus process environment and specific parameters of the theoretical model can change during the negotiation period. Consequently, there is a need for developing dynamic consensus process models to represent effectively and realistically the dynamic nature of the group decision making problem. Indeed, over the past few years, static consensus models have given way to new dynamic approaches in order to manage parameter variability or to adapt to environment changes. This paper presents a systematic literature review on the recent evolution of consensus reaching models under dynamic environments and critically analyse their advantages and limitations
Dynamics and Social Clustering on Coevolving Networks
Complex networks offer a powerful conceptual framework for the description and analysis of many real world systems. Many processes have been formed into networks in the area of random graphs, and the dynamics of networks have been studied. These two mechanisms combined creates an adaptive or coevolving network -- a network whose edges change adaptively with respect to its states, bringing a dynamical interaction between the state of nodes and the topology of the network. We study three binary-state dynamics in the context of opinion formation, disease propagation and evolutionary games of networks. We try to understand how the network structure affects the status of individuals, and how the behavior of individuals, in turn, affects the overall network structure. We focus our investigation on social clustering, since this is one of the central properties of social networks, arising due to the ubiquitous tendency among individuals to connect to friends of a friend, and can significantly impact a coevolving network system. Introducing rewiring models with transitivity reinforcement, we investigate how the mechanism affects network dynamics and the clustering structure of the networks. We perform Monte Carlo simulations to explore the parameter space of each model. By applying improved compartmental formalism methods, including approximate master equations, our semi-analytical approximation generally provide accurate predictions of the final states of the networks, degree distributions, and evolution of fundamental quantities. Different levels of semi-analytical estimation are compared.Doctor of Philosoph
Supervised Preference Models: Data and Storage, Methods, and Tools for Application
In this thesis, we present a variety of models commonly known as pairwise comparisons, discrete choice and learning to rank under one paradigm that we call preference models. We discuss these approaches together with the intention to show that these belong to the same family and show a unified notation to express these. We focus on supervised machine learning approaches to predict preferences, present existing approaches and identify gaps in the literature. We discuss reduction and aggregation, a key technique used in this field and identify that there are no existing guidelines for how to create probabilistic aggregations, which is a topic we begin exploring. We also identify that there are no machine learning interfaces in Python that can account well for hosting a variety of types of preference models and giving a seamless user experience when it comes to using commonly recurring concepts in preference models, specifically, reduction, aggregation and compositions of sequential decision making. Therefore, we present our idea of what such software should look like in Python and show the current state of the development of this package which we call skpref
Data analysis methods for copy number discovery and interpretation
Copy
number
variation
(CNV)
is
an
important
type
of
genetic
variation
that
can
give
rise
to
a
wide
variety
of
phenotypic
traits.
Differences
in
copy
number
are
thought
to
play
major
roles
in
processes
that
involve
dosage
sensitive
genes,
providing
beneficial,
deleterious
or
neutral
modifications
to
individual
phenotypes.
Copy
number
analysis
has
long
been
a
standard
in
clinical
cytogenetic
laboratories.
Gene
deletions
and
duplications
can
often
be
linked
with
genetic
Syndromes
such
as:
the
7q11.23
deletion
of
Williams-‐Bueren
Syndrome,
the
22q11
deletion
of
DiGeorge
syndrome
and
the
17q11.2
duplication
of
Potocki-‐Lupski
syndrome.
Interestingly,
copy
number
based
genomic
disorders
often
display
reciprocal
deletion
/
duplication
syndromes,
with
the
latter
frequently
exhibiting
milder
symptoms.
Moreover,
the
study
of
chromosomal
imbalances
plays
a
key
role
in
cancer
research.
The
datasets
used
for
the
development
of
analysis
methods
during
this
project
are
generated
as
part
of
the
cutting-‐edge
translational
project,
Deciphering
Developmental
Disorders
(DDD).
This
project,
the
DDD,
is
the
first
of
its
kind
and
will
directly
apply
state
of
the
art
technologies,
in
the
form
of
ultra-‐high
resolution
microarray
and
next
generation
sequencing
(NGS),
to
real-‐time
genetic
clinical
practice.
It
is
collaboration
between
the
Wellcome
Trust
Sanger
Institute
(WTSI)
and
the
National
Health
Service
(NHS)
involving
the
24
regional
genetic
services
across
the
UK
and
Ireland.
Although
the
application
of
DNA
microarrays
for
the
detection
of
CNVs
is
well
established,
individual
change
point
detection
algorithms
often
display
variable
performances.
The
definition
of
an
optimal
set
of
parameters
for
achieving
a
certain
level
of
performance
is
rarely
straightforward,
especially
where
data
qualities
vary ... [cont.]
Using Raman Spectroscopy for Intraoperative Margin Analysis in Breast Conserving Surgery
Breast Conserving Surgery (BCS) in the treatment of breast cancer aims to provide optimal oncological results, with minimal tissue excision to optimise cosmetic outcome. Positive margins due to an inadequate resection occurs in 17% of UK patients undergoing BCS and prompts recommendation for further tissue re-excision to reduce recurrence risk. A second operation causes patient anxiety and significant healthcare costs. This issue could be resolved with accurate intra-operative margin analysis (IMA) to enable excision of all cancerous tissue at the index procedure. High wavenumber Raman Spectroscopy (HWN RS) is a vibrational spectroscopy highly sensitive to changes in protein/lipid environment and water content –biochemical differences found between tumour and normal breast tissue. We proposed that HWN RS could be used to differentiate between tumour and non-tumour breast tissue with a view to future IMA. This thesis presents the development of a Raman system to measure the HWN region capable of accurately detecting changes in protein, lipid and water content, in the presence of highly fluorescent surgical pigments such as blue dye that are present in surgically excised specimens. We investigate the relationship between changes in the HWN spectra with changes in water content in constructed breast phantoms to mimic protein and lipid rich environments and biological tissue. Human breast tissue of paired tumour and non-tumour samples were then measured and analysed. We found that breast tumour tissue is a protein rich, high water, low fat environment and that non-tumour is a low protein, fat rich environment with a low water content, and this can be used to identify breast cancer using HWN RS with excellent accuracy of over 90%. This thesis demonstrates a HWN RS Raman system capable of differentiating between tumour and non-tumour tissue in human breast tissue, and this has the potential to provide IMA in BCS
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