A Perspective on Graph Theory and Network ScienceMarko Rodriguez
The graph/network domain has been driven by the creativity of numerous individuals from disparate areas of the academic and the commercial sector. Examples of contributing academic disciplines include mathematics, physics, sociology, and computer science. Given the interdisciplinary nature of the domain, it is difficult for any single individual to objectively realize and speak about the space as a whole. Any presentation of the ideas is ultimately biased by the formal training and expertise of the individual. For this reason, I will simply present on the domain from my perspective---from my personal experiences. More specifically, from my perspective biased by cognitive and computer science.
This is an autobiographical lecture on my life (so far) with graphs/networks.
Is network theory the best hope for regulating systemic risk?Kimmo Soramaki
The presentation is organised around three policy questions:
1. How can we measure the systemic importance of a bank?
2. Can regulators promote a safer financial system by affecting its topology?
3. Is it possible to devise early-warning indicators from real-time data?
A Perspective on Graph Theory and Network ScienceMarko Rodriguez
The graph/network domain has been driven by the creativity of numerous individuals from disparate areas of the academic and the commercial sector. Examples of contributing academic disciplines include mathematics, physics, sociology, and computer science. Given the interdisciplinary nature of the domain, it is difficult for any single individual to objectively realize and speak about the space as a whole. Any presentation of the ideas is ultimately biased by the formal training and expertise of the individual. For this reason, I will simply present on the domain from my perspective---from my personal experiences. More specifically, from my perspective biased by cognitive and computer science.
This is an autobiographical lecture on my life (so far) with graphs/networks.
Is network theory the best hope for regulating systemic risk?Kimmo Soramaki
The presentation is organised around three policy questions:
1. How can we measure the systemic importance of a bank?
2. Can regulators promote a safer financial system by affecting its topology?
3. Is it possible to devise early-warning indicators from real-time data?
Mapping Experiences with Actor Network TheoryLiza Potts
My presentation from ATTW's annual conference. I talk about how we can better design for experiences if we first understand the context in which we are building products and services. This simple mapping system helps visualize these contexts.
Want more? Check out my book on social media and disaster, filled with more information on how to map networks using actor-network theory http://www.amazon.com/dp/0415817412
2009 - Connected Action - Marc Smith - Social Media Network AnalysisMarc Smith
Review of social media network analysis of Internet social spaces like twitter, flickr, email, message boards, etc. Network analysis and visualization of social media collections of connections.
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.
An interactive presentation on social network theory and analysis. Content includes information on tie formation and social capital. Network relations are explained by using the example of The A Team. Granovetter's Strength of Weak Ties Theory (1973) is also covered and weak ties and strong ties are explained. Appropriate application of social network theory to individuals understanding how to best take advantage of social networking platforms to find jobs as well as companies taking advantage of social media platforms to find followers are introduced.
NetWorkShop: Boston Facilitators RoundtablePatti Anklam
The NetWorkShop offers a new perspective – a network lens – that sheds light on how human networks are structured and how technologies can enhance our ability to collaborate and co-create. For facilitators, it offers possibilities of new ways of thinking about client work as well as leadership coaching.
This workshop provides a clear presentation of basic network concepts, including:
· Reflective exercises in creating and interpreting network maps of relationships (organizational and personal) using network concepts
· An introduction to value networking analysis, with a focus on mapping roles and deliverables (gives and gets) in an organizational ecosystem
· A short overview of how social media (Twitter, Facebook, LinkedIn) is altering the landscape of how people create and work in networks.
Presentation by Dr Craig Hammond of University Centre Blackburn College (UCBC) which introduces some of the basic principles and ideas associated with Actor Network Theory
Mapping Experiences with Actor Network TheoryLiza Potts
My presentation from ATTW's annual conference. I talk about how we can better design for experiences if we first understand the context in which we are building products and services. This simple mapping system helps visualize these contexts.
Want more? Check out my book on social media and disaster, filled with more information on how to map networks using actor-network theory http://www.amazon.com/dp/0415817412
2009 - Connected Action - Marc Smith - Social Media Network AnalysisMarc Smith
Review of social media network analysis of Internet social spaces like twitter, flickr, email, message boards, etc. Network analysis and visualization of social media collections of connections.
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.
An interactive presentation on social network theory and analysis. Content includes information on tie formation and social capital. Network relations are explained by using the example of The A Team. Granovetter's Strength of Weak Ties Theory (1973) is also covered and weak ties and strong ties are explained. Appropriate application of social network theory to individuals understanding how to best take advantage of social networking platforms to find jobs as well as companies taking advantage of social media platforms to find followers are introduced.
NetWorkShop: Boston Facilitators RoundtablePatti Anklam
The NetWorkShop offers a new perspective – a network lens – that sheds light on how human networks are structured and how technologies can enhance our ability to collaborate and co-create. For facilitators, it offers possibilities of new ways of thinking about client work as well as leadership coaching.
This workshop provides a clear presentation of basic network concepts, including:
· Reflective exercises in creating and interpreting network maps of relationships (organizational and personal) using network concepts
· An introduction to value networking analysis, with a focus on mapping roles and deliverables (gives and gets) in an organizational ecosystem
· A short overview of how social media (Twitter, Facebook, LinkedIn) is altering the landscape of how people create and work in networks.
Presentation by Dr Craig Hammond of University Centre Blackburn College (UCBC) which introduces some of the basic principles and ideas associated with Actor Network Theory
Scaling the API Economy - with Scale-Free Networks API Days Keynote from Laye...CA API Management
The Web exhibits a feature found in many complex systems known as "Scale-Free" or "Power-Law" networks, sometimes called the "long tail" Most people think of the "long tail" as an economic and/or social property. However, it also represents physical and informational properties fundamental to the way the Web works. But the steady increase in major service outages indicate that many current Web APIs, services, and even client applications ignore this basic "law of the Web."
This talk explores the "Scale-Free" rule of complex systems and offers clear and simple advice to those planning to build and/or consume APIs for the Web. Such as what to avoid, what to plan for, what to build, and how to identify & steer clear of clients and services that fail to abide by the rules and, in the process, are making it harder for all of us to scale the API Economy.
We present a new simulation tool for scale-free networks composed of a high number of nodes. The tool, based on discrete-event simulation, enables the definition of scale-free networks composed of heterogeneous nodes and complex application-level protocols. To satisfy the performance and scalability requirements, the simulator supports both sequential (i.e. monolithic) and parallel/distributed (i.e. PADS) approaches. Furthermore, appropriate mechanisms for the communication overhead-reduction are implemented. To demonstrate the efficiency of the tool, we experiment with gossip protocols on top of scale-free networks generated by our simulator. Results of the simulations demonstrate the feasibility of our approach. The proposed tool is able to generate and manage large scale-free networks composed of thousands of nodes interacting following real-world dissemination protocols.
Social Network Theory is the study of how people, organizations or groups interact with others inside their network understanding the easier when you examine the individual pieces starting with the largest element, when is networks, and working down to the smallest elements, which is the actors. The idea of social network and the notions of sociograms appeared over 50years ago Barnes (1954) is credited with coining the notion of social network, an outflow of his study of a Norwegian island parish in the early 1950s
This was a talk delivered to MA students of Cultural Policy and Management at City University in November 2011 - essentially an introductory summary of the phenomenon of 'internet of things'.
Social network analysis for modeling & tuning social media websiteEdward B. Rockower
Social Network Analysis of a Professional Online Social Media Collaboration Community. Tuning and optimizing based on observed social network dynamics and user behavior.
Social Network Analysis: What It Is, Why We Should Care, and What We Can Lear...Xiaohan Zeng
The advent of the social networks has completely changed our daily life. The deluge of data collected on Social Network Services (SNS) and recent developments in complex network theory have enabled many marvelous predictive analysis, which tells us many amazing stories.
Why do we often feel that "the world is so small?" Is the six-degree separation purely imagination or based on mathematical insights? Why are there just a few rockstars who enjoy extreme popularity while most of us stay unknown to the world? When science meets coffee shop knowledge, things are bound to be intriguing.
I will first briefly describe what social networks are, in the mathematical sense. Then I will introduce some ways to extract characteristics of networks, and how these analyses can explain many anecdotes in our life. Finally, I'll show an example of what we can learn from social network analysis, based on data from Groupon.
OKFN Greece meet-up
Friday, April 6, 2012, 5:00 PM
Aristotle University of Thessaloniki, Research Dissemination Center
Prof. I. Antoniou (Director of MSc Web Science, AUTH, Steering Committee OKFN Greece). The power of Openness. Open Data and Open Knowledge
Understanding the Big Picture of e-ScienceAndrew Sallans
A. Sallans. "Understanding the Big Picture of e-Science." Presented at the 2011 eScience Bootcamp at the University of Virginia's Claude Moore Health Sciences Library. 4 March 2011
Does networking really make a difference? The answer is a resounding yes! In a study performed by Partnering Resources, 93% of completely successful change initiatives were led by leaders with very strong or strong personal networks. Not one change initiatives described as less successful was led by a leader with strong or very strong personal networks. Furthermore, a recent study featured in Sloan Management Review showed that high performing project teams had almost twice as many non-core contributors affiliated with the team.
In this highly interactive session, we learned about the science behind networking. We drew on insights from researchers and practitioners in the social sciences and in business to learned about the networking practices of high performers. We dispelled the myth that people who want strong networks should never eat alone and, instead, we learned about the simple actions that significantly contribute to the health of your network. We merged the art and science by mapping individual participants’ networks, identifying gaps, and developing plans for filling those gaps.
Presented October 2, 2012 at The Commonwealth Institute.
Event information: http://partneringresources.com/event/art-science-networking-basics-commonwealth-institute/
First presented at http://www.e2conf.com/virtual/
The value of Social Analytics can be surfaced in many ways. Sometimes is quite visual like a leader board that helps motivate participation. Other times it's behind the scenes like the algorithms used to recommend groups to join or pages to read. Either way, social analytics can help you make better informed decisions, provide more relevancies to your interactions and ultimately help you get you and your company be more successful. This session will take a look at some of the real world implementations of social analytics available today from many of the social business vendors. We'll talk about the trends in this space and discuss some of the possible future directions.
All Things Open 2014 - Day 1
Wednesday, October 22nd, 2014
Arfon Smith
Chief Scientist for GitHub
Open Government/Open Data
What Academia Can Learn from Open Source
Find more by Arfon here: https://speakerdeck.com/arfon
I have provided a methodology for PR people to keep up-to-date with new things that are going to be important in the near future. I shall be adding content to it for the next couple of months.
The practice of PR has changed. This slideshow offers a view of the basics that every PR consultant should offer clients for 2012.
Probably the most exciting part of the presentation is the list of URL's at the end... Nice way to be very motivated.
Read| The latest issue of The Challenger is here! We are thrilled to announce that our school paper has qualified for the NATIONAL SCHOOLS PRESS CONFERENCE (NSPC) 2024. Thank you for your unwavering support and trust. Dive into the stories that made us stand out!
Synthetic Fiber Construction in lab .pptxPavel ( NSTU)
Synthetic fiber production is a fascinating and complex field that blends chemistry, engineering, and environmental science. By understanding these aspects, students can gain a comprehensive view of synthetic fiber production, its impact on society and the environment, and the potential for future innovations. Synthetic fibers play a crucial role in modern society, impacting various aspects of daily life, industry, and the environment. ynthetic fibers are integral to modern life, offering a range of benefits from cost-effectiveness and versatility to innovative applications and performance characteristics. While they pose environmental challenges, ongoing research and development aim to create more sustainable and eco-friendly alternatives. Understanding the importance of synthetic fibers helps in appreciating their role in the economy, industry, and daily life, while also emphasizing the need for sustainable practices and innovation.
Operation “Blue Star” is the only event in the history of Independent India where the state went into war with its own people. Even after about 40 years it is not clear if it was culmination of states anger over people of the region, a political game of power or start of dictatorial chapter in the democratic setup.
The people of Punjab felt alienated from main stream due to denial of their just demands during a long democratic struggle since independence. As it happen all over the word, it led to militant struggle with great loss of lives of military, police and civilian personnel. Killing of Indira Gandhi and massacre of innocent Sikhs in Delhi and other India cities was also associated with this movement.
Biological screening of herbal drugs: Introduction and Need for
Phyto-Pharmacological Screening, New Strategies for evaluating
Natural Products, In vitro evaluation techniques for Antioxidants, Antimicrobial and Anticancer drugs. In vivo evaluation techniques
for Anti-inflammatory, Antiulcer, Anticancer, Wound healing, Antidiabetic, Hepatoprotective, Cardio protective, Diuretics and
Antifertility, Toxicity studies as per OECD guidelines
Model Attribute Check Company Auto PropertyCeline George
In Odoo, the multi-company feature allows you to manage multiple companies within a single Odoo database instance. Each company can have its own configurations while still sharing common resources such as products, customers, and suppliers.
The Roman Empire A Historical Colossus.pdfkaushalkr1407
The Roman Empire, a vast and enduring power, stands as one of history's most remarkable civilizations, leaving an indelible imprint on the world. It emerged from the Roman Republic, transitioning into an imperial powerhouse under the leadership of Augustus Caesar in 27 BCE. This transformation marked the beginning of an era defined by unprecedented territorial expansion, architectural marvels, and profound cultural influence.
The empire's roots lie in the city of Rome, founded, according to legend, by Romulus in 753 BCE. Over centuries, Rome evolved from a small settlement to a formidable republic, characterized by a complex political system with elected officials and checks on power. However, internal strife, class conflicts, and military ambitions paved the way for the end of the Republic. Julius Caesar’s dictatorship and subsequent assassination in 44 BCE created a power vacuum, leading to a civil war. Octavian, later Augustus, emerged victorious, heralding the Roman Empire’s birth.
Under Augustus, the empire experienced the Pax Romana, a 200-year period of relative peace and stability. Augustus reformed the military, established efficient administrative systems, and initiated grand construction projects. The empire's borders expanded, encompassing territories from Britain to Egypt and from Spain to the Euphrates. Roman legions, renowned for their discipline and engineering prowess, secured and maintained these vast territories, building roads, fortifications, and cities that facilitated control and integration.
The Roman Empire’s society was hierarchical, with a rigid class system. At the top were the patricians, wealthy elites who held significant political power. Below them were the plebeians, free citizens with limited political influence, and the vast numbers of slaves who formed the backbone of the economy. The family unit was central, governed by the paterfamilias, the male head who held absolute authority.
Culturally, the Romans were eclectic, absorbing and adapting elements from the civilizations they encountered, particularly the Greeks. Roman art, literature, and philosophy reflected this synthesis, creating a rich cultural tapestry. Latin, the Roman language, became the lingua franca of the Western world, influencing numerous modern languages.
Roman architecture and engineering achievements were monumental. They perfected the arch, vault, and dome, constructing enduring structures like the Colosseum, Pantheon, and aqueducts. These engineering marvels not only showcased Roman ingenuity but also served practical purposes, from public entertainment to water supply.
1. An Introduction to
Network Theory
Kyle Findlay
Kyle.Findlay@tns-global.com
R&D Executive
TNS Global Brand Equity Centre
Presented at the
SAMRA 2010 Conference
Mount Grace Country House and Spa, Magaliesburg, South Africa, from 2| to 5 June 2010
An Introduction to Network Theory | Kyle Findlay SAMRA 2010
2. An agent/object's actions are affected
by the actions of others around it.
What is a network?
Actions, choices, etc. are not made in isolation
i.e. they are contingent on others' actions, choices, etc.
An Introduction to Network Theory | Kyle Findlay | SAMRA 2010
3. “A collection of objects connected to
each other in some fashion”
[Watts, 2002]
4. Social groups Diseases Stem cells
The internet Neural networks (computer & human) Other cells
Cities Proteins & genes Plants
Quaking Aspen (one of the largest organisms in the world –
these trees represent a single organism with a shared root
system)
The blogosphere
Source: Six Degrees, Duncan Watts, 2002
Proliferation of landlines in London
What is a network?
Human genome
Rabbit cell
An Introduction to Network Theory | Kyle Findlay | SAMRA 2010
5. ▫ New paradigm: “real networks represent populations of individual components
that are actually doing something” [Watts, 2002]
In other words, networks are dynamic objects that are continually changing
Understanding a network is important because its structure affects the individual
components’ behaviour and the behaviour of the system as a whole
Networks used to be thought of as systems… structures
▫ Networks are key to understanding non-linear, dynamic
fixed
…just like those represented by almost every facet of the universe…
…from the atomic level right through to the cosmic level
▫ The important part is that the components are not acting independently – they
are affected by the components around them!
▫ Note: links between component may be physical (e.g. power cable, magnetism)
or conceptual (e.g. social connections)
What is a network? An Introduction to Network Theory | Kyle Findlay | SAMRA 2010
6. CAUTION: Gratuitous network shots
Data networks Air traffic network
Telecommunications networks Shipping (sea) networks
Source: Britain From Above (http://www.bbc.co.uk/britainfromabove) An Introduction to Network Theory | Kyle Findlay | SAMRA 2010
7. Network thinking can be applied
almost anywhere!
An Introduction to Network Theory | Kyle Findlay | SAMRA 2010
8. URL: http://www.youtube.com/watch?v=PufTeIBNRJ4
Epidemiology (i.e. spread of diseases)
e.g. spread of foot & mouth disease in the UK in 2001 over 75 days
Where’s it applied? An Introduction to Network Theory | Kyle Findlay | SAMRA 2010
9. URL: http://www.youtube.com/watch?v=8C_dnP2fvxk
Physics
e.g. particle interactions, the structure of the universe
Where’s it applied? An Introduction to Network Theory | Kyle Findlay | SAMRA 2010
10. URL: http://www.youtube.com/watch?v=lRZ2iEHFgGo URL: http://www.youtube.com/watch?v=AEoP-XtJueo
Engineering
e.g. creation of robust infrastructure (e.g. electricity, telecoms), rust formation (natural growth
processes similar to diffusion limited aggregation)
Where’s it applied? An Introduction to Network Theory | Kyle Findlay | SAMRA 2010 |
11. Vid not working
URL: http://www.youtube.com/watch?v=l-RoDv7c5ok URL: http://www.youtube.com/watch?v=o4g930pm8Ms
Technology
e.g. mapping the online world, making networks resilient in the face of cyber-terrorism,
optimising cellular networks, controlling air traffic
Where’s it applied? An Introduction to Network Theory | Kyle Findlay | SAMRA 2010 |
12. URL: http://www.youtube.com/watch?v=YadP3w7vkJA URL: http://www.youtube.com/watch?v=Sp8tLPDMUyg
Biology
e.g. fish swimming in schools, ant colonies, birds flying in formation, crickets chirping in
unison, giant honeybees shimmering
Where’s it applied? An Introduction to Network Theory | Kyle Findlay | SAMRA 2010 |
13. Source: The Human Brain Book by Rita Carter
Medicine
e.g. cell formation, nervous system, neural networks
Where’s it applied? An Introduction to Network Theory | Kyle Findlay | SAMRA 2010 |
14. URL: http://www.youtube.com/watch?v=9n9irapdON4 URL: http://www.youtube.com/watch?v=sD2yosZ9qDw
And, most interestingly…society
e.g. interactions between people (e.g. Facebook; group behaviour)
Where’s it applied? An Introduction to Network Theory | Kyle Findlay | SAMRA 2010 |
15. Some terminology…
▫ Node = individual
components of a
network e.g.
people, power
stations, neurons,
etc.
▫ Edge = direct link
between
components
(referred to as a
dyad in context of
social networking
An Introduction to Network Theory | Kyle Findlay | SAMRA 2010 |
16. No connections Some nodes All relevant nodes
between nodes connected connected
c=0 c = 1/3 c=1
▫ Tells you how likely a node is to be connected to its neighbours…
…and, importantly, how likely that its neighbours are connected to each other
▫ Put another way, it tells you how close a node and its neighbors are to being a clique where
“everybody knows everyone else”
Important network features:
Clustering co-efficient
An Introduction to Network Theory | Kyle Findlay | SAMRA 2010 |
17. “Unclustered” network “Clustered” network
None of Ego’s friends know each other* All of Ego’s friends know each other
Important network features:
Clustering co-efficient
*Source: Six Degrees, Duncan Watts, 2002 An Introduction to Network Theory | Kyle Findlay | SAMRA 2010 |
18. ▫ A real-world example: CEOs of Fortune 500 companies
Which companies share directors? Clusters are
colour-coded
Important network features:
Clustering co-efficient
Source: http://flickr.com/photos/11242012@N07/1363558436 An Introduction to Network Theory | Kyle Findlay | SAMRA 2010 |
19. ▫ Average path length = the average number of
‘hops’ required to reach any other node in the
network
▫ “Six degrees of separation” average path length =
6
Important network features:
Average path length
An Introduction to Network Theory | Kyle Findlay | SAMRA 2010 |
20. ▫ The degree of a node is the number of connections
(or edges) it has coming in from, and going out to,
other nodes 1
2
10
3
9
Node 4
8
7
5
10 connections 6
or “edges”
Important network features:
Degree distribution
An Introduction to Network Theory | Kyle Findlay | SAMRA 2010 |
21. 3 main types of networks
1. Grid/lattice network 2. Small-world network 3. Random network
(structure, order) (a mix of order and randomness) (randomness)
β=0 << Level of randomness of links >> β=1
They sit on a continuum based on a few factors:
1
Randomness 2
Clustering 3
Ave path length
*Source: Six Degrees, Duncan Watts, 2002 An Introduction to Network Theory | Kyle Findlay | SAMRA 2010 |
22. 3 main types of networks:
Grid/Lattice network
▫ Simplest form of network with nodes ranged
geometrically
▫ Low degree (nodes only connected to closest
neighbours)
▫ High clustering
▫ Long average path length (no shortcuts – have to
go through all nodes)
▫ Pros: methodical, easy to visualise
▫ Cons: not very good at modeling most real-
1D lattice
world networks
Molecule Diamond (crystal) lattice Bismuth crystal
An Introduction to Network Theory | Kyle Findlay | SAMRA 2010 |
23. 3 main types of networks:
Small world network
▫ Most nodes aren’t neighbours, but they can be
reached
from every other node by a small number of
hops or steps
Higher clustering co-efficient than to a few random
i.e. small average path length dueone would expect if
connections were made by pure random chance
re-wirings
− “A small world network, where nodes represent people and edges
connect people that know each other, captures the small world
phenomenon of strangers being linked by a mutual acquaintance”
Common in nature, including everything from the internet
to gene regulatory networks to ecosystems
Source: http://en.wikipedia.org/wiki/Small-world_network An Introduction to Network Theory | Kyle Findlay | SAMRA 2010 |
24. 3 main types of networks:
Random network
▫ Lower clustering than small-world
networks generally
▫ No “force” or “bias” influencing how
links are created between nodes
i.e. probability of creating an edge/link is
independent of previous connections
An Introduction to Network Theory | Kyle Findlay | SAMRA 2010 |
25. The big picture
Nature
▫ Networks are evident
everywhere in nature
▫ In fact, most natural growth
Natural growth = evolutionary, iterative growth, where future growth is
constrained by previous growth patterns (referred to as path dependence)
processes come about due to
− i.e. growth follows the network structure
network behaviour
An Introduction to Network Theory | Kyle Findlay | SAMRA 2010 |
26. And market research?
▫ Networks better reflect reality and capture complexity i.e. non-linear dynamics
▫ Network theory helps us to better understand:
?How will word of my
brand permeate through
? How will negative publicity
about my brand spread and
? Who are the gatekeepers
in a community that
my target market? be interpreted? most affect the
flow of information?
?How is the market likely to
fall out in terms of
? What will the non-linear
market share impact be of a specific
(Double Jeopardy)? change in the market
e.g. change in market share,
perceptions, etc.
An Introduction to Network Theory | Kyle Findlay | SAMRA 2010 |
27. And market research?
▫ Network theory has been used to understand imagery and market barriers
Adjusting attributes and seeing knock-on effect in network
Using agent-based modeling to model this effect
Useful for word-of-mouth/viral approaches
− Watts and Peretti use network theory to
increase reach of WOM campaigns
Helps us avoid thinking about things in a vacuum
as it takes account of inter-related variables…
− … and provides us with counter-intuitive outcomes
that we may not have reached on our own
An Introduction to Network Theory | Kyle Findlay | SAMRA 2010 |
29. ▫ This is a very simple 2D
representation of how I
roughly visualise
information
propagating through a
network
It is very simple and
doesn’t take into
account many
concepts
But it is a visual aid
that helps one to start
thinking about interesting bits:
Some how
A network in action
information might
spread from person to
Source: http://www.funny-games.biz/reaction-effect.html
person
An Introduction to Network Theory | Kyle Findlay | SAMRA 2010 |
30. What does a highly “spreadable” idea look like?
• 1,636,967 views in two months (as at 25 April 2010)
• Performed in front of 75,000 people at Coachella Music Festival (California, April 2010)
URL: http://www.youtube.com/watch?v=Q77YBmtd2Rw
Some interesting bits:
How ideas spread
31. Who spreads ideas?
− Watts vs. Gladwell
vs.
− Mavens/influencers vs. forest fire
− Self-organised criticality
− K-shell decomposition?
Some interesting bits:
How ideas spread
32. Which ideas spread?
− Unpredictable
− Ideas that “fit”
Some interesting bits:
How ideas spread
33. ▫ Refers to systems in which many individual agents with
limited intelligence and information are able to pool
resources to accomplish a goal beyond the capabilities
of the individuals… while no single ant knows how toself-interest
− e.g.
only focused on build an ant colony
− e.g. in mind
without the bigger picturethe internet has grown organically over time
with no single person directing its growth
−
i.e. no grand designer
This is known as self-organisation and/or emergence, and is a property of
complex networks and non-linear, dynamic systems
Some interesting bits:
Distributed intelligence
An Introduction to Network Theory | Kyle Findlay | SAMRA 2010 |
34. ▫ Existence of such behaviour in organisms has many
implications for social, military and management
applications and is one of the most active areas of
research today!
Some interesting bits:
Distributed intelligence diffusion, memes,
Works best in small-world networks
Implications for knowledge
An Introduction to Network Theory | Kyle Findlay | SAMRA 2010 |
35. URL: http://www.youtube.com/watch?v=ozkBd2p2piU
Ant colony
Some interesting bits:
Distributed intelligence
Source: http://www.youtube.com/watch?v=ozkBd2p2piU An Introduction to Network Theory | Kyle Findlay | SAMRA 2010 |
36. ▫ “On average, the first 5 random re-wirings
reduce the average path length of the network by
one-half, regardless of the size of the network”
[Watts, 2002]*
Random re-wirings
“8” “3”
Long average path length Dramatically reduced average path length
Some interesting bits:
Random re-wirings
*Source: Six Degrees, Duncan Watts, 2002, p.89 An Introduction to Network Theory | Kyle Findlay | SAMRA 2010 |
37. Some interesting bits:
Triadic closure
A B
C
▫ People are more likely to become acquainted over time when they have something in
common
i.e. we have a bias towards the familiar, thus reducing the pure randomness of
connections
Known as “homophily” - “birds of a feather flock together”
▫ Network connections don’t arise independently of each other…
…they are influenced by previous connections
▫ If A knows B…
…and B knows C…
…then A is much more likely to know C
*Source: Six Degrees, Duncan Watts, 2002, p.58-61 An Introduction to Network Theory | Kyle Findlay | SAMRA 2010 |
38. Some interesting bits:
Triadic closure
A B
C
This is why random re-wirings are so effective at reducing the ave. path length…
− …they help connect clusters, or ‘cliques’, that might otherwise exist in isolation
This is the strength of the small-world network:
− High clustering and a relatively small amount of random re-wirings allows for a
dramatically reduced average path length…
− …allowing everyone to connect to everyone else in relatively few steps e.g. “six degrees
of separation”
*Source: Six Degrees, Duncan Watts, 2002, p.58-61 An Introduction to Network Theory | Kyle Findlay | SAMRA 2010 |
39. Some interesting bits:
Triadic closure
•This “birds of a feather flock together” effect was modeled by Watts and Strogatz*
• They used α (alpha) to represent level of preference to only connect with friends of
friends
• Low α = strong preference to only connect with friends of friends (triadic closures occur,
independent clusters)
• High α = connections chosen at random
• Small-world networks exist somewhere around the peak (which represents a phase transition)
i.e. where clustering is high but average path length is low
• To the left of the peak, clusters are just starting to join together
• At the peak, everyone is connected
• To the right of the peak, connections are lost as wirings become more random
*Source: Six Degrees, Duncan Watts, 2002, p.78-79 An Introduction to Network Theory | Kyle Findlay | SAMRA 2010 |
40. ▫ Studies conducted by Stanley Milgram beginning
in 1967 at Harvard University
▫ Sent packages to randomly selected people in
Omaha, Nebraska & Wichita
▫ Asked that they bedelivered to individuals in
Milgram repeated other similar
experiments which also received low
Boston, Massachusettscompletion rates
▫ Could only forward package to people they knew
However, experiments on the internet
have since confirmed the number at 6:
on a first-name basis − Facebook application:
▫ Only 64 of 296 letters reached path–=4.5destination
Six Degrees
average
their million users;
5.73
▫ Average path length of these was around 5.5 or 6
▫ Milgram never used the phrase “six degrees of
Some interesting bits: separation” himself
Six degrees of separation
Source: Wikipedia, Small world experiment
Wikipedia, Six degrees of separation An Introduction to Network Theory | Kyle Findlay | SAMRA 2010 |
41. ▫ The Kevin Bacon game
Aim is to connect all other actors back to Kevin
Bacon
Choice of Kevin Bacon is arbitrary – can be applied
to most actors
Some interesting bits:
Six degrees of separation
*Source: Six Degrees, Duncan Watts, 2002 An Introduction to Network Theory | Kyle Findlay | SAMRA 2010 |
42. What’s your Erdős number?
(Scientific equivalent of The Kevin Bacon Game)
“Apocalypse” by XKCD
Alt text for this comic:
"I wonder if I still have time to go shoot a short film with
Kevin Bacon?"
URL: http://xkcd.com/599/
Some interesting bits:
Six degrees of separation
43. ▫ A network is considered “scale-free” if its degree
distribution follows a power law
i.e. nodes can have an unlimited number of links to
them e.g. the internet This is what a power law
distribution looks like*
A few nodes have many links, while the majority
have few links
If you take the log of both
axes, you should get a
straight line*
Some interesting bits:
Scale-free networks & power laws
*Image source: Six Degrees, Duncan Watts, 2002 An Introduction to Network Theory | Kyle Findlay | SAMRA 2010 |
44. ▫ However, very few, if any, networks can display
scale-free properties indefinitely
At some point, limited resources force a cut-off e.g.
limited number of computers in the world
▫ Therefore, generally, scale-free networks only
Taking the log-log of a
display a power law distributionlaw distribution line* area of
power
for some
should show a straight
the graph
However, in practice, the
line is generally only
straight for some area of
the graph*
Some interesting bits:
Scale-free networks & power laws
*Image source: Six Degrees, Duncan Watts, 2002 An Introduction to Network Theory | Kyle Findlay | SAMRA 2010 |
45. ▫ Power law distributions help us understand
natural growth (e.g. popularity of brands, trends,
ideas, politics, religion, etc.)
Growth in an environment where social influence
occurs tends to result in a power law distribution
(think cumulative advantage)
This comes about due to network behaviour
e.g. nodes with more connections are more likely to
have even more connections (sounds a lot like…
Double Jeopardy!)
▫ This ‘skewing’ of growth patterns is characteristic
Some interesting bits:
Scale-free networks & power laws
of small world networks and results in a few large
components and many small components
An Introduction to Network Theory | Kyle Findlay | SAMRA 2010 |
50. •Thanks!
It’s a small world after all
http://www.youtube.com/watch?v=tYQovmtO06k&feature=related
Editor's Notes
New paradigm: “real networks represent populations of individual components that are actually doing something” [Watts, 2002] In other words, networks are dynamic objects that are continually changing Understanding a network is important because its structure affects the individual components’ behaviour and/or the behaviour of the system as a whole Networks are key to understanding non-linear, dynamic systems… … just like those represented by almost every facet of the universe… … from the atomic level right through to the cosmic level The important part is that the components are not acting independently – they are affected by the components around them! Note: links between component may be physical (e.g. power cable, magnetism) or conceptual (e.g. social connections)
Duncan Watts and Steven Strogatz introduced the measure in 1998 Tells you how likely a node is to be connected to its neighbours… … and, importantly, how likely that its neighbours are connected to each other Put another way, it tells you how close a node and its neighbors are to being a clique where “everybody knows everyone else”
Project Description: In 2006, FAS analyzed the director interlock relationships between Fortune 500 companies in California. We looked at how companies are connected through their board of directors, i.e. Apple and Disney are connected through Steve Jobs since he serves on both boards. Companies that share a lot of directors create denser zones in the network and form clusters. We measured which companies exert the largest influence overall and within each cluster. This reveals compelling new insights into key account management. Legend: The triangles represent Fortune 500 companies in CA. The larger the triangle, the more influential the company is. Companies of the same color belong to the same network cluster. If company A and company B share a director, they are linked by a line. The more directors shared, the thicker the line.