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Networks

Patterns in Nature, Networks Intro

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Slides for our discussion of network components, topology and properties.

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Page 1: Patterns in Nature, Networks Intro

Networks

Page 2: Patterns in Nature, Networks Intro

Ewa

Balaji

Albert

Michele

Project M

Marc O'Brien

Ben Gaydos

Boston Progress

Arts Collective

Janet

Jonah

Alex

Becky

Karen

Karen Stein

A Better World by

Design

Design That

Matters

D-Lab

Nadia

Sandra

UC Berkeley

Rachel

Kopernik

Elizabeth

Donny

MIT

Jackie

Appropedia

Orgs TEL wants to

or could

work with

The TEL Team

Professional

Networking Hubs

Designers,

Coders, etc.

Waka

Nokero

SelcoEssmart

Sameer

Elise

Diana

JICA

Raiki

Translators Without

Borders

Engineering for

Change

Steve

Lesley University

John

Jo

Operation

Groundswell

PATH

The TEL Network

Page 3: Patterns in Nature, Networks Intro

Systems

Thinking

systems

elements

interrelations

purpose

feedback

Communications

simple

Qd the core

use analogies

concrete

unexpected

expose knowledge

gaps

Behavorial Change

direct the rider

send destination

postcards

Qd bright spots

script critical

moves

motivate the

elephant

shrink the change

Qd the feeling

appeal to idenity

engineer early

successes

help people grow

shape the path

create action

triggers

use checklists

make it social

Impact

know your

mission

measure the

right thing

measure it well

verb

target

outcome

impact from a thing

is it needed?

will it get to those

who need it?

will they use it

correctly?

leverage points

traps

opportunities

design

thinking

ask stupid

questionsmake hope visible

jump fences

smart

recombinations

abductive

reasoning

drift

fail early, fail often

harvest ideas, edit

applications compelling

experience

framework

generative

metaphors

combine familiar

and surprising

embrace

constraints

forced

prioritization

create mystery

credible

human-scale

principle

authority

antiauthority

the Sinatra test

testable

credentials

the Velcro theory of

memory

black & white

goals

emotion

violate schemas

solution focus

tweak the

environment

stories

build a culture

stocks

flows

H+E Toolkit

Page 4: Patterns in Nature, Networks Intro

Nodes

Page 5: Patterns in Nature, Networks Intro

B

A

C

D

network topology examples

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Links

Page 7: Patterns in Nature, Networks Intro

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D

network topology examples

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NetworkTopology

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network topology examples

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A

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D

network topology examples

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network topology examples

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C

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network topology examples

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network topology examples

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C

B

A

D

network topology examples

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B

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network topology examples

C

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A

D

network topology examples

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network topology examples

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network topology examples

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network topology examples

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network topology examples

Page 18: Patterns in Nature, Networks Intro

Degree:how connected a node is

Page 19: Patterns in Nature, Networks Intro
Page 20: Patterns in Nature, Networks Intro
Page 21: Patterns in Nature, Networks Intro

Chapter 1In the ick of It

Page 22: Patterns in Nature, Networks Intro

Linear

Mutual ties (flow in both directions)

e Bucket Brigade

Photo from FEMA

Page 23: Patterns in Nature, Networks Intro

Information is directional (inbound and outbound ties)

e Telephone Tree

Creates cascade effect

Reduces number of steps

Page 24: Patterns in Nature, Networks Intro

Squad is more intra-connected than inter-connected with other squads

e Military SquadTightly interconnected groups

Two-way tie between all members of squad

Page 25: Patterns in Nature, Networks Intro

Degree of Separation:how far away other nodes are

Page 26: Patterns in Nature, Networks Intro

G

S

N

K

O

T

E

first-degree

connection

second-degree

connection

third-degree

connection

degree of seapartion

Page 27: Patterns in Nature, Networks Intro

G

K

E

O

S

T

N

first-degree

connection

second-degree

connection

third-degree

connection

degree of separation

Page 28: Patterns in Nature, Networks Intro

Hubs

Page 29: Patterns in Nature, Networks Intro
Page 30: Patterns in Nature, Networks Intro
Page 31: Patterns in Nature, Networks Intro
Page 32: Patterns in Nature, Networks Intro
Page 33: Patterns in Nature, Networks Intro
Page 34: Patterns in Nature, Networks Intro

34

NATURE NEUROSCIENCE VOLUME 16 | NUMBER 3 | MARCH 2013 371

R E S O U R C E

links connecting the ki neighbors of node i to each other, represents how nodes interconnect26. The Ci value of 0.24 measured for our net-work indicates strong clustering27. Nodes with the largest numbers of links were actin (33 interactions), PtdIns(4,5)P2 (20), SLC9A3R2 (12), CALM (9), RDX (8) and Ca2+ (8).

RDX and SLC9A3R2RDX and SLC9A3R2, identified as hubs in the hair-bundle protein network, were each detected in chick utricle bundles by immuno-blotting (Fig. 5a and Supplementary Fig. 8). We also found that SLC9A3R2 expressed in a human embryo kidney cell line (HEK293T) immunoprecipitated with RDX; SLC9A3R2 binds to activated

ERM proteins28, and binding was indeed stronger to RDX with a threonine-to-aspartate mutation that mimics the activating phos-phorylation (Fig. 5b and Supplementary Fig. 8). In bullfrog stereo-cilia, RDX is activated by PtdIns(4,5)P2 and phosphorylation is found in a narrow band above basal tapers, at the site of the ankle links5.

Ptdlns(4,5)P2

PLS1ANXA6

Known interactionBundle protein paralogHypothetical

Molecules perstereocilium

105

104

103

102

10

PIP4K2B

Figure 4 Interaction network for hair-bundle proteins. Symbols (nodes) represent bundle proteins or second messengers; only nodes with two or more interactions are plotted, with the exception of OCM and CALB2. Underlined labels indicate deafness-associated proteins. Node colors indicate functional classification (same key as in Fig. 2b); node symbol size represents protein abundance in bundles. Ca2+, PtdIns(4,5)P2 and cyclic AMP are indicated by diamond node symbols. Solid links represent interactions validated with literature citations; Supplementary Table 3 lists all interactions and evidence. Dotted links correspond to interactions involving paralogs of bundle proteins; dashed links represent hypothetical interactions (for example, SLC9A3R2 interactions from Table 3). The layout of the plot is controlled by the density of links between nearby nodes. The distribution of nodes and links in the plot is fit well by a power law, which indicates that the plot contains a few highly connected nodes (hubs) and many other less-connected nodes. Supplementary Figure 7 reproduces this figure with each link hyperlinked to a PubMed reference supporting the interaction.

a

c

bHair

bund

les

Agaro

se

Epithe

lium

80HA-SLC9A3R2

Myc-T564D-RDX

SLC9A3R2

SLC9A3R2

RDX

RDX

11080

5060

Myc-RDX– + + +

5% load

5% load

HA IP

HA IP

Myc blot

HA

blot

–– – –

– ++–RDX

pERM

SLC9A3R2

RHOA

CDC42

MYO6

MYO7A

*

*

80

5040

20

20

260160

260

d RDX

RDX

SLC9A3R2

SLC9A3R2

Actin

RDX

RDX

pERM

pERM

Actin

MVSC

f ActinRHOAActinRHOAe Actin

MYO7AActinMYO7A

**

Figure 5 Identification of RDX and SLC9A3R2 in hair bundles. (a) Protein immunoblotting with purified hair bundles. Hair bundle lane represents bundles from 40 chick ears (~0.6 g total protein); agarose lane represents agarose equivalent to that in the bundles sample; epithelium lane represents utricle sensory epithelium from 4 chick ears (~10 g protein). Antibodies used are indicated at left. Both anti-RDX and anti-pERM (which recognizes phosphorylated ezrin, radixin and moesin) detected bands both at the expected size (~70 kDa) and at ~80 kDa (*). (b) RDX-SLC9A3R2 interaction. Myc or hemagglutinin (HA) epitope-tagged chick wild-type RDX, T564D-RDX and SLC9A3R2 were expressed in HEK cells in the indicated combinations. Tagged SLC9A3R2 was immunoprecipitated, and associated RDX was detected by immunoblotting. (c) RDX and pERM immunocytochemistry. RDX and pERM colocalize except in the taper region (double arrows in bottom panel). Inset, magnification of apical surfaces of supporting and hair cells. pERM immunoreactivity is absent from bases of microvilli (MV), as in stereocilia (SC). Scale bar in c is 10 m and applies to c–f. (d) RDX and SLC9A3R2 immunocytochemistry. RDX and SLC9A3R2 overlap throughout the bundle, but SLC9A3R2 is more concentrated toward stereociliary bases (double arrows in bottom panel), including the tapers, than is RDX. (e) MYO7A immunocytochemistry. MYO7A is concentrated toward stereociliary tips and in a band above the taper region (*). (f) RHOA immunocytochemistry. Staining is seen in stereocilia (arrow) and the kinocilium or the tallest stereocilia of the bundle (arrowheads). RHOA is also substantially enriched in hair cells over supporting cells.

Page 35: Patterns in Nature, Networks Intro

Clusters

Page 36: Patterns in Nature, Networks Intro

SocialNetworks

Page 37: Patterns in Nature, Networks Intro

TermsA Social Network: consists of all the connections and ties within a group or collection of groups

A group is a collection of individuals defined by a common attribute (it need not contain information about connections)

Page 38: Patterns in Nature, Networks Intro

Sociogramswho is connected to whom

Page 39: Patterns in Nature, Networks Intro

Ewa

Balaji

Albert

Michele

Project M

Marc O'Brien

Ben Gaydos

Boston Progress

Arts Collective

Janet

Jonah

Alex

Becky

Karen

Karen Stein

A Better World by

Design

Design That

Matters

D-Lab

Nadia

Sandra

UC Berkeley

Rachel

Kopernik

Elizabeth

Donny

MIT

Jackie

Appropedia

Orgs TEL wants to

or could

work with

The TEL Team

Professional

Networking Hubs

Designers,

Coders, etc.

Waka

Nokero

SelcoEssmart

Sameer

Elise

Diana

JICA

Raiki

Translators Without

Borders

Engineering for

Change

Steve

Lesley University

John

Jo

Operation

Groundswell

PATH

The TEL Network

Page 40: Patterns in Nature, Networks Intro

Small World Networks

Page 41: Patterns in Nature, Networks Intro
Page 42: Patterns in Nature, Networks Intro
Page 43: Patterns in Nature, Networks Intro

The Powerof Weak Ties

Page 44: Patterns in Nature, Networks Intro
Page 45: Patterns in Nature, Networks Intro

Networks and Epidemics

Page 46: Patterns in Nature, Networks Intro

To figure out how a disease is spreading, look for hubs.

Page 47: Patterns in Nature, Networks Intro

5/1/13 9:21 PMWith Bird Flu, 'Right Now, Anything Is Possible' : Shots - Health News : NPR

Page 1 of 6http://www.npr.org/blogs/health/2013/04/19/177793443/with-bird-flu-right-now-anything-is-possible

With Bird Flu, 'Right Now, Anything IsPossible'

April 19, 2013 5:36 AM

by RICHARD KNOX

Listen to the StoryMorning Edition 4 min 22 sec

ChinaFotoPress/Getty Images

i

An international dream team of flu experts assembled in Chinatoday.

Underscoring the urgency that public health agencies feel about theemergence of a new kind of bird flu, the team is headed by Dr. KeijiFukuda, the World Health Organization's top influenza scientist.

Before he left Geneva, Fukuda explained the wide-open nature of