This is a colloquium that I presented on 4/22/21: Stockholm University, Nordic Institute for Theoretical Physics (NORDITA), WINQ–AlbaNova Colloquium
Here is a video of my talk: http://video.albanova.se/ALBANOVA20210422/video.mp4
Network Visualization guest lecture at #DataVizQMSS at @Columbia / #SNA at PU...Denis Parra Santander
- First version was a guest lecture about Network Visualization in the class "Data Visualization" taught by Dr. Sharon Hsiao in the QMSS program at Columbia University http://www.columbia.edu/~ih2240/dataviz/index.htm
- This updated version was delivered in our class on SNA at PUC Chile in the MPGI master program.
Network Visualization guest lecture at #DataVizQMSS at @Columbia / #SNA at PU...Denis Parra Santander
- First version was a guest lecture about Network Visualization in the class "Data Visualization" taught by Dr. Sharon Hsiao in the QMSS program at Columbia University http://www.columbia.edu/~ih2240/dataviz/index.htm
- This updated version was delivered in our class on SNA at PUC Chile in the MPGI master program.
Presented "Random Walk on Graphs" in the reading group for Knoesis. Specifically for Recommendation Context.
Referred: Purnamrita Sarkar, Random Walks on Graphs: An Overview
Opinion Dynamics on Generalized NetworksMason Porter
This is a talk on opinion dynamics (especially bounded-confidence models) on generalized networks.
It is part of the MIX-NEXT III (Multiscale & Integrative compleX Networks: EXperiments & Theories) satellite at NetSci 2022.
(Thursday 14 July 2022)
Deeper Things: How Netflix Leverages Deep Learning in Recommendations and Se...Sudeep Das, Ph.D.
In this talk, we will provide an overview of Deep Learning methods applied to personalization and search at Netflix. We will set the stage by describing the unique challenges faced at Netflix in the areas of recommendations and information retrieval. Then we will delve into how we leverage a blend of traditional algorithms and emergent deep learning methods and new types of embeddings, especially hyperbolic space embeddings, to address these challenges.
This Presentation course will help you in understanding the Machine Learning model i.e. Generalized Linear Models for classification and regression with an intuitive approach of presenting the core concepts
A gentle introduction to growth curves using SPSSsmackinnon
A brief introduction on how to conduct growth curve statistical analyses using SPSS software, including some sample syntax. Originally presented at IWK Statistics Seminar Series at the IWK Health Center, Halifax, NS, May 1, 2013.
Prepared as a conference tutorial, MIC-Electrical, Athens, Greece, 5th April 2014, updated and delivered again in Beijing, China, 27 January 2015 to students from Complex Systems Group, CSRC and Dept. of Engineering Physics, Tsinghua University
A high-level overview of social network analysis using gephi with your exported Facebook friends network. See more network analysis at http://allthingsgraphed.com.
Centrality in Time- Dependent NetworksMason Porter
My slides for my keynote talk at the NetSci 2018 (#NetSci2018) conference in Paris, France (June 2018). This talk will take place on Thursday 13 June in the morning.
This is a presentation I gave in a workshop on "Language, concepts, history" organized by historian Joanna Innes. It took place on Friday 4/22/16 in Somerville College, Oxford.
I was one of the only people present who was not from the humanities, so it was a rather different-than-usual audience and set of participants for me.
I drew some of these slides from other presentations to rather different audiences. I emphasized rather different parts of some of those slides, so I am not sure if the slides on their own give an accurate reflection of the difference between this presentation and some of my other ones.
I thought the presentation went rather well.
Mathematical Models of the Spread of Diseases, Opinions, Information, and Mis...Mason Porter
This is my general-audience talk at DiscCon III (2021 WorldCon).
My talk overlapped with the Hugo Award ceremony, but the video will be posted later on the DisCon website for attendees who want to see it.
Presented "Random Walk on Graphs" in the reading group for Knoesis. Specifically for Recommendation Context.
Referred: Purnamrita Sarkar, Random Walks on Graphs: An Overview
Opinion Dynamics on Generalized NetworksMason Porter
This is a talk on opinion dynamics (especially bounded-confidence models) on generalized networks.
It is part of the MIX-NEXT III (Multiscale & Integrative compleX Networks: EXperiments & Theories) satellite at NetSci 2022.
(Thursday 14 July 2022)
Deeper Things: How Netflix Leverages Deep Learning in Recommendations and Se...Sudeep Das, Ph.D.
In this talk, we will provide an overview of Deep Learning methods applied to personalization and search at Netflix. We will set the stage by describing the unique challenges faced at Netflix in the areas of recommendations and information retrieval. Then we will delve into how we leverage a blend of traditional algorithms and emergent deep learning methods and new types of embeddings, especially hyperbolic space embeddings, to address these challenges.
This Presentation course will help you in understanding the Machine Learning model i.e. Generalized Linear Models for classification and regression with an intuitive approach of presenting the core concepts
A gentle introduction to growth curves using SPSSsmackinnon
A brief introduction on how to conduct growth curve statistical analyses using SPSS software, including some sample syntax. Originally presented at IWK Statistics Seminar Series at the IWK Health Center, Halifax, NS, May 1, 2013.
Prepared as a conference tutorial, MIC-Electrical, Athens, Greece, 5th April 2014, updated and delivered again in Beijing, China, 27 January 2015 to students from Complex Systems Group, CSRC and Dept. of Engineering Physics, Tsinghua University
A high-level overview of social network analysis using gephi with your exported Facebook friends network. See more network analysis at http://allthingsgraphed.com.
Centrality in Time- Dependent NetworksMason Porter
My slides for my keynote talk at the NetSci 2018 (#NetSci2018) conference in Paris, France (June 2018). This talk will take place on Thursday 13 June in the morning.
This is a presentation I gave in a workshop on "Language, concepts, history" organized by historian Joanna Innes. It took place on Friday 4/22/16 in Somerville College, Oxford.
I was one of the only people present who was not from the humanities, so it was a rather different-than-usual audience and set of participants for me.
I drew some of these slides from other presentations to rather different audiences. I emphasized rather different parts of some of those slides, so I am not sure if the slides on their own give an accurate reflection of the difference between this presentation and some of my other ones.
I thought the presentation went rather well.
Mathematical Models of the Spread of Diseases, Opinions, Information, and Mis...Mason Porter
This is my general-audience talk at DiscCon III (2021 WorldCon).
My talk overlapped with the Hugo Award ceremony, but the video will be posted later on the DisCon website for attendees who want to see it.
WSDM 2018 Tutorial on Influence Maximization in Online Social NetworksCigdem Aslay
In this tutorial, we extensively survey the research on social influence propagation and maximization, with a focus on the recent algorithmic and theoretical advances. To this end, we provide detailed reviews of the latest research effort devoted to (i) improving the efficiency and scalability of the influence maximization algorithms; (ii) context-aware modeling of the influence maximization problem to better capture real-world marketing scenarios; (iii) modeling and learning of real-world social influence; (iv) bridging the gap between social advertising and viral marketing.
Current trends of opinion mining and sentiment analysis in social networkseSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Presented at IZEAfest in Orlando, FL
Social network and sharing analysis including:
+Document analysis at scale: Meme tracking combined with other variables like sentiment and bias
+Social network at scale: Information cascades and virality, inference of social networks given meme-like information as contagions
+The node level perspective and its effects on what an individual sees and shares: Illusions, effort and overload, topics, personality and demographics
+Personas and segmentation: Grouping based on demographics and interests
Analyses of the structure of social-personality psychology as manifest in bibliometric couplings within the Journal of Personality and Social Psychology for selected years between 1981 and 2014
Introduction to Topological Data AnalysisMason Porter
Here are slides for my 3/14/21 talk on an introduction to topological data analysis.
This is the first talk in our Short Course on topological data analysis at the 2021 American Physical Society (APS) March Meeting: https://march.aps.org/program/dsoft/gsnp-short-course-introduction-to-topological-data-analysis/
Topological Data Analysis of Complex Spatial SystemsMason Porter
These are slides from a seminar I gave in "Cardiff" (for the mathematics department at University of Cardiff) on 4/15/20.
You can also find a recording of a similar talk that I gave in March 2020 for MBI (Mathematical Biosciences Institute): https://mbi.osu.edu/events/online-colloquium-mason-porter-spatial-systems-and-topological-data-analysis
Here are my slides (though the animated gifs on a couple of them are stills in this version) of my talk on an introduction to the science of "chaos" at WorldCon 77 in Dublin, Ireland.
This is my attempt to give a gentle introduction to the notion of chaos to a science-fiction audience.
Paper Writing in Applied Mathematics (slightly updated slides)Mason Porter
Here are my slides (which I have updated very slightly) in writing papers in applied mathematics.
There will be an accompanying oral presentation and discussion on Friday 20 April. I am recording the video for that and plan to post it along with these (or a further updated version of these) slides.
Tutorial on Paper-Writing in Applied Mathematics (Preliminary Draft of Slides)Mason Porter
These are preliminary slides for a tutorial and discussion on "Writing Papers in Applied Mathematics" that I'll be giving at UCLA, first for a few of my own PhD students on 4/6 and later (on 4/20 ?) in a recorded session to a larger UCLA group.
Several people have expressed interest, so I will post the recorded session online and circulate it.
These slides are for my talk for the Somerville College Mathematics Reunion ("Somerville Maths Reunion", 6/24/17): http://www.some.ox.ac.uk/event/somerville-maths-reunion/
My talk at the 2017 SIAM "Snowbird" conference on applications of dynamical systems (#SIAMDS17).
I spoke in a session on topological data analysis (TDA). My talk concerned persistent homology and its application to Brexit data (including voting data) and "functional networks" from coupled time series from both experiments and output of dynamical systems.
Eventually, a version of these slides that is synchronized with the audio of my talk is supposed to be posted online.
This is my attempt at an introduction to data ethics for mathematicians. Mathematicians increasingly need to deal with these kinds of issues, but we don't have the tradition of ethics training from other disciplines.
I welcome comments on how to improve these slides. Did I miss any salient points? Do you want to offer a different perspective on any of these? Do you want to offer any counterpoints? (Please e-mail me directly with comments and suggestions.)
Eventually, I hope to develop these slides further into an article for a venue aimed at mathematical scientists, and of course I would love to have knowledgeable coauthors who can offer a different perspective from mine.
My slides from my 3-hour tutorial on mesoscale structures in networks from the 2016 Lake Como School on Complex Networks (http://ntmb.lakecomoschool.org/).
After my talk, Tiago Peixoto gave a talk on statistical inference of large-scale mesoscale structures in networks. His presentation, which takes a complementary perspective from mine, is available at the following website: https://speakerdeck.com/count0/statisical-inference-of-generative-network-models
Networks in Space: Granular Force Networks and BeyondMason Porter
This is my talk for the Network Geometry Workshop (http://ginestra-bianconi-6flt.squarespace.com) at QMUL on 16 July 2015.
(A few of the slides are adapted from slides by my coauthors Dani Bassett and Karen Daniels.)
These are slides for my tutorial talk on network dynamics. (The colors are fine in the downloaded version, though there seem to be color issues if you view the slides directly in slideshare.)
Slides from my talk on a systems-level investigation of long-term human migration in Korea. Our paper is available at the following page: http://journals.aps.org/prx/abstract/10.1103/PhysRevX.4.041009
I adapted these slides from the ones created by my coauthor Sang Hoon Lee.
These are the slides for a tutorial talk about "multilayer networks" that I gave at NetSci 2014.
I walk people through a review article that I wrote with my PLEXMATH collaborators: http://comnet.oxfordjournals.org/content/2/3/203
Seminar of U.V. Spectroscopy by SAMIR PANDASAMIR PANDA
Spectroscopy is a branch of science dealing the study of interaction of electromagnetic radiation with matter.
Ultraviolet-visible spectroscopy refers to absorption spectroscopy or reflect spectroscopy in the UV-VIS spectral region.
Ultraviolet-visible spectroscopy is an analytical method that can measure the amount of light received by the analyte.
Cancer cell metabolism: special Reference to Lactate PathwayAADYARAJPANDEY1
Normal Cell Metabolism:
Cellular respiration describes the series of steps that cells use to break down sugar and other chemicals to get the energy we need to function.
Energy is stored in the bonds of glucose and when glucose is broken down, much of that energy is released.
Cell utilize energy in the form of ATP.
The first step of respiration is called glycolysis. In a series of steps, glycolysis breaks glucose into two smaller molecules - a chemical called pyruvate. A small amount of ATP is formed during this process.
Most healthy cells continue the breakdown in a second process, called the Kreb's cycle. The Kreb's cycle allows cells to “burn” the pyruvates made in glycolysis to get more ATP.
The last step in the breakdown of glucose is called oxidative phosphorylation (Ox-Phos).
It takes place in specialized cell structures called mitochondria. This process produces a large amount of ATP. Importantly, cells need oxygen to complete oxidative phosphorylation.
If a cell completes only glycolysis, only 2 molecules of ATP are made per glucose. However, if the cell completes the entire respiration process (glycolysis - Kreb's - oxidative phosphorylation), about 36 molecules of ATP are created, giving it much more energy to use.
IN CANCER CELL:
Unlike healthy cells that "burn" the entire molecule of sugar to capture a large amount of energy as ATP, cancer cells are wasteful.
Cancer cells only partially break down sugar molecules. They overuse the first step of respiration, glycolysis. They frequently do not complete the second step, oxidative phosphorylation.
This results in only 2 molecules of ATP per each glucose molecule instead of the 36 or so ATPs healthy cells gain. As a result, cancer cells need to use a lot more sugar molecules to get enough energy to survive.
Unlike healthy cells that "burn" the entire molecule of sugar to capture a large amount of energy as ATP, cancer cells are wasteful.
Cancer cells only partially break down sugar molecules. They overuse the first step of respiration, glycolysis. They frequently do not complete the second step, oxidative phosphorylation.
This results in only 2 molecules of ATP per each glucose molecule instead of the 36 or so ATPs healthy cells gain. As a result, cancer cells need to use a lot more sugar molecules to get enough energy to survive.
introduction to WARBERG PHENOMENA:
WARBURG EFFECT Usually, cancer cells are highly glycolytic (glucose addiction) and take up more glucose than do normal cells from outside.
Otto Heinrich Warburg (; 8 October 1883 – 1 August 1970) In 1931 was awarded the Nobel Prize in Physiology for his "discovery of the nature and mode of action of the respiratory enzyme.
WARNBURG EFFECT : cancer cells under aerobic (well-oxygenated) conditions to metabolize glucose to lactate (aerobic glycolysis) is known as the Warburg effect. Warburg made the observation that tumor slices consume glucose and secrete lactate at a higher rate than normal tissues.
This pdf is about the Schizophrenia.
For more details visit on YouTube; @SELF-EXPLANATORY;
https://www.youtube.com/channel/UCAiarMZDNhe1A3Rnpr_WkzA/videos
Thanks...!
Introduction:
RNA interference (RNAi) or Post-Transcriptional Gene Silencing (PTGS) is an important biological process for modulating eukaryotic gene expression.
It is highly conserved process of posttranscriptional gene silencing by which double stranded RNA (dsRNA) causes sequence-specific degradation of mRNA sequences.
dsRNA-induced gene silencing (RNAi) is reported in a wide range of eukaryotes ranging from worms, insects, mammals and plants.
This process mediates resistance to both endogenous parasitic and exogenous pathogenic nucleic acids, and regulates the expression of protein-coding genes.
What are small ncRNAs?
micro RNA (miRNA)
short interfering RNA (siRNA)
Properties of small non-coding RNA:
Involved in silencing mRNA transcripts.
Called “small” because they are usually only about 21-24 nucleotides long.
Synthesized by first cutting up longer precursor sequences (like the 61nt one that Lee discovered).
Silence an mRNA by base pairing with some sequence on the mRNA.
Discovery of siRNA?
The first small RNA:
In 1993 Rosalind Lee (Victor Ambros lab) was studying a non- coding gene in C. elegans, lin-4, that was involved in silencing of another gene, lin-14, at the appropriate time in the
development of the worm C. elegans.
Two small transcripts of lin-4 (22nt and 61nt) were found to be complementary to a sequence in the 3' UTR of lin-14.
Because lin-4 encoded no protein, she deduced that it must be these transcripts that are causing the silencing by RNA-RNA interactions.
Types of RNAi ( non coding RNA)
MiRNA
Length (23-25 nt)
Trans acting
Binds with target MRNA in mismatch
Translation inhibition
Si RNA
Length 21 nt.
Cis acting
Bind with target Mrna in perfect complementary sequence
Piwi-RNA
Length ; 25 to 36 nt.
Expressed in Germ Cells
Regulates trnasposomes activity
MECHANISM OF RNAI:
First the double-stranded RNA teams up with a protein complex named Dicer, which cuts the long RNA into short pieces.
Then another protein complex called RISC (RNA-induced silencing complex) discards one of the two RNA strands.
The RISC-docked, single-stranded RNA then pairs with the homologous mRNA and destroys it.
THE RISC COMPLEX:
RISC is large(>500kD) RNA multi- protein Binding complex which triggers MRNA degradation in response to MRNA
Unwinding of double stranded Si RNA by ATP independent Helicase
Active component of RISC is Ago proteins( ENDONUCLEASE) which cleave target MRNA.
DICER: endonuclease (RNase Family III)
Argonaute: Central Component of the RNA-Induced Silencing Complex (RISC)
One strand of the dsRNA produced by Dicer is retained in the RISC complex in association with Argonaute
ARGONAUTE PROTEIN :
1.PAZ(PIWI/Argonaute/ Zwille)- Recognition of target MRNA
2.PIWI (p-element induced wimpy Testis)- breaks Phosphodiester bond of mRNA.)RNAse H activity.
MiRNA:
The Double-stranded RNAs are naturally produced in eukaryotic cells during development, and they have a key role in regulating gene expression .
Observation of Io’s Resurfacing via Plume Deposition Using Ground-based Adapt...Sérgio Sacani
Since volcanic activity was first discovered on Io from Voyager images in 1979, changes
on Io’s surface have been monitored from both spacecraft and ground-based telescopes.
Here, we present the highest spatial resolution images of Io ever obtained from a groundbased telescope. These images, acquired by the SHARK-VIS instrument on the Large
Binocular Telescope, show evidence of a major resurfacing event on Io’s trailing hemisphere. When compared to the most recent spacecraft images, the SHARK-VIS images
show that a plume deposit from a powerful eruption at Pillan Patera has covered part
of the long-lived Pele plume deposit. Although this type of resurfacing event may be common on Io, few have been detected due to the rarity of spacecraft visits and the previously low spatial resolution available from Earth-based telescopes. The SHARK-VIS instrument ushers in a new era of high resolution imaging of Io’s surface using adaptive
optics at visible wavelengths.
Earliest Galaxies in the JADES Origins Field: Luminosity Function and Cosmic ...Sérgio Sacani
We characterize the earliest galaxy population in the JADES Origins Field (JOF), the deepest
imaging field observed with JWST. We make use of the ancillary Hubble optical images (5 filters
spanning 0.4−0.9µm) and novel JWST images with 14 filters spanning 0.8−5µm, including 7 mediumband filters, and reaching total exposure times of up to 46 hours per filter. We combine all our data
at > 2.3µm to construct an ultradeep image, reaching as deep as ≈ 31.4 AB mag in the stack and
30.3-31.0 AB mag (5σ, r = 0.1” circular aperture) in individual filters. We measure photometric
redshifts and use robust selection criteria to identify a sample of eight galaxy candidates at redshifts
z = 11.5 − 15. These objects show compact half-light radii of R1/2 ∼ 50 − 200pc, stellar masses of
M⋆ ∼ 107−108M⊙, and star-formation rates of SFR ∼ 0.1−1 M⊙ yr−1
. Our search finds no candidates
at 15 < z < 20, placing upper limits at these redshifts. We develop a forward modeling approach to
infer the properties of the evolving luminosity function without binning in redshift or luminosity that
marginalizes over the photometric redshift uncertainty of our candidate galaxies and incorporates the
impact of non-detections. We find a z = 12 luminosity function in good agreement with prior results,
and that the luminosity function normalization and UV luminosity density decline by a factor of ∼ 2.5
from z = 12 to z = 14. We discuss the possible implications of our results in the context of theoretical
models for evolution of the dark matter halo mass function.
7. Social Networks
• Typically (but not always), nodes represent individuals
• Depending on the application, edges can represent one (or more) of various types
of social connections: offline interactions, phone calls, Facebook ‘friendships’,
Twitter followership, etc.
• Notions of actual social ties, but also notions of communication
• Different things propagate on different types of networks
• For example: information spreading versus disease spreading
• Complicated mixture of regular and ‘random’ structures
• Good random-graph models provide baselines for comparison
8. Dynamical Processes on Networks
•Incorporate which individuals (nodes) interact with which other
individuals via their ties (edges).
•This yields a dynamical system on a network.
•Basic question: How does network structure affect dynamics (and
vice versa)?
•MAP & J. P Gleeson [2016], “Dynamical Systems on Networks: A
Tutorial”, Frontiers in Applied Dynamical Systems: Reviews and
Tutorials, Vol. 4
9. A General Note About Time Scales and Modeling
Dynamical Systems on Dynamical Networks
• Relative time scales of evolution of states versus evolution of network structure
• States change much faster than structure?
• Faster: Dynamical systems on static networks (“quenched”)
• MUCH faster (too rapidly): Can only trust statistical properties of states
• Structure changes much faster than states?
• Faster: Temporal networks
• MUCH faster (too rapidly): Can only trust statistical properties of network structure (“annealed”)
• Comparable time scales?
• “Adaptive” networks (aka “coevolving” networks)
• Dynamics of states of network nodes (or edges) coupled to dynamics of network structure
10. Spreading and Opinion Models
•There are many types of models, and the goal of my talk is to introduce
three types of them.
• Threshold models
• A type of model with discrete states (usually two of them) that models social
reinforcement in contagious spreading processes in a minimalist way
• Voter models
• Discrete-valued opinions, although not really a model for “voters”
• Bounded-confidence models
• Continuous-valued opinions
11. Threshold Models
Example: Watts Threshold Model
• D. J.Watts, PNAS, 2002
• Each node j has a (frozen) threshold Rj drawn from some distribution and can be in one of two states (0 or 1)
• Choose a seed fraction ρ(0) of nodes (e.g. uniformly at random) to initially be in state 1 (“infected”,“active”,
etc.)
• Updating can be either:
• Synchronous: discrete time; update all nodes at once
• Asynchronous:“continuous” time; update some fraction of nodes in time step dt (e.g., using a Gillespie
algorithm)
• Update rule: Compare fraction of infected neighbors (m/kj) to Rj. Node j becomes infected if m/kj ≥ Rj.
Otherwise no change.
• Variant (Centola–Macy): Look at number of active neighbors (m) rather than fraction of active neighbors
• Monotonicity: Nodes in state 1 stay there forever.
J. P. Gleeson, PRX,Vol. 3, 021004 (2013): has a table of more than 20 binary-state models (WTM, percolation models, etc.)
13. A Threshold Model with Hipsters
• J. S. Juul & MAP [2019], “ Hipsters on Networks: How a Minority Group of Individuals Can Lead to an
Antiestablishment Majority”, Physical Review E, Vol. 99: 022313
• WTM rules to adopt some product (A or B)
• Conformist node: Adopts majority opinion from local neighborhood
• Hipster node: Adopts minority opinion (from full network, like a best-seller list) from ! times ago
16. “The” Voter Model
• S. Redner [2019], “Reality Inspired Voter Models: A Mini-Review”, Comptes
Rendus Physique, Vol. 20:275–292
• In an update step, an individual updates their opinion based on the opinion of a
neighbor
• One choice: asynchronous versus synchronous updating
• Select a random node (e.g., uniformly at random) and then a random neighbor
• Another choice: node-based models versus edge-based models
• Select a random edge (or perhaps a random “discordant” edge)
• In Kureh & Porter (2020), we use asynchronous, edge-based updates.
17. A Nonlinear Coevolving Voter Model
• Y. Kureh & MAP [2020], “Fitting In
and Breaking Up: A Nonlinear Version
of Coevolving Voter Models”, Physical
Review E, Vol. 101, No. 6: 062303
• We consider versions of the model with
three types of changes in network
structure.
• Each step: probability !q of rewiring
step and complementary probability 1 –
!q of opinion update
• q = nonlinearity parameter
21. Majority Illusion and Echo Chambers
• “Liberal Facebook” versus
“Conservative Facebook”:
http://graphics.wsj.com/blue-feed-
red-feed/
• “Majority illusion”: K. Lerman, X.
Yan, & X.-Z. Wu, PLoS ONE, Vol.
11, No. 2: e0147617 2016
• Such network structures form
naturally from homophily and are
exacerbated further by heated
arguments in contentious times.
24. Bounded-Confidence Models
• Continuous-valued opinions on some space, such as [–1,1]
• When two agents interact:
• If their opinions are sufficiently close, they compromise by some amount
• Otherwise, their opinions don’t change
• Two best-known variants
• Deffuant et al. model: asynchronous updating of node states
• Hegselmann–Krause model: synchronous updating of node states
• Most traditionally studied without network structure (i.e., all-to-all coupling of agents) and with a
view towards studying consensus
• By contrast, original motivation — but barely explored in practice — of bounded-confidence models
was to examine how extremist ideas, even when seeded in a small proportion of a population, can take
root in a population
25. Bounded-Confidence Model on Networks
• X. Flora Meng, Robert A. Van Gorder, & MAP [2018], “Opinion Formation and Distribution in a Bounded-
Confidence Model on Various Networks”, Physical Review E, Vol. 97, No. 2: 022312
• Network structure has a major effect on the dynamics, including how many opinion groups form and how long they take to form
• At each discrete time, randomly select a pair of agents who are adjacent in a network
• If their opinions are close enough, they compromise their opinion by an amount proportional to the difference
• If their opinions are too far apart, they don’t change
• Complicated dynamics
• Does consensus occur? How many opinion groups are there at steady state? How long does it take to converge to steady state?
How does this depend on parameters and network structure?
• Example: Convergence time seems to undergo a critical transition with respect to opinion confidence bound (indicating
compromise range) on some types of networks
28. Influence of Media
• Heather Z. Brooks & MAP [2020], “A Model for the Influence of Media on the Ideology of
Content in Online Social Networks”, Physical Review Research, Vol. 2, No. 2: 023041)
• Discrete events (sharing stories), but the probability to share them (and thereby influence
opinions of neighboring nodes) is based on a bounded-confidence mechanism
• Distance based both on location in ideology space and on the level of quality of the content that is
being spread
• Include “media nodes” that have only out-edges
• How easily can media nodes with extreme ideological positions influence opinions in a network?
• Future considerations: can also incorporate bots, sockpuppet accounts, etc.
31. Conclusions
• Lots of cool stuff to study in opinion and spreading models on networks
• Flavors of models include threshold models, voter models, bounded-confidence models, and others.
• How does network structure affect dynamics?
• Is there a consensus? How many opinion groups? How long does it take to converge to a steady state? Etc.
• Some very recent and upcoming papers
• A. Hickok, Y. H. Kureh, H. Z. Brooks, M. Feng, & MAP: “A Bounded-Confidence Model of Opinion
Dynamics on Hypergraphs”, arXiv:2104.00720
• H. Z. Brooks & MAP, “Spreading Cascades in Bounded-Confidence Dynamics on Networks”, in preparation
• M. Feng, H. Z. Brooks, Y. H. Kureh, A. Hickok, & MAP: “A Bounded-Confidence Model of Opinion
Dynamics on Multilayer Networks”
• U. Kanjanasaratool, M. Feng, & MAP: “An Adaptive Bounded-Confidence Model”, in preparation