When Networks Network - Science News

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Home / September 22nd, 2012; Vol.182 #6 / Feature
When Networks Network
Once studied solo, systems display surprising behavior when theyinteract
Elizabeth Quill
September 22nd, 2012; Vol.182 #6 (p. 18)
By

ENLARGE
Artwork by Bryan Christie Design
Half a dozen times each night, your slumbering body performs aremarkable feat of coordination.
During the deepest throes of sleep, the body’s support systems run
on their own timetables. Nerve cells hum along in your brain, theirchitchat generating slow waves that signal sleep’s nether stages. Yet,
like buses and trains with overlapping routes but unsynchronizedschedules, this neural conversation has little to say to your heart,which pumps blood to its own rhythm through the body’s arteries andveins. Air likewise skips into the nostrils and down the windpipe inseemingly random spits and spats. And muscle fluctuations that makethe legs twitch come and go as if in a vacuum. Networks of muscles,of brain cells, of airways and lungs, of heart and vessels operatelargely independently.
Every couple of hours, though, in as little as 30 seconds, the barriers
break down. Suddenly, there’s synchrony. All the disjointed activity of
deep sleep starts to connect with its surroundings. Each network —run via the group effort of its own muscular, cellular and molecular
players — joins the larger team.
This change, marking the transition from deep to light sleep, has only
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recently been understood in detail — thanks to a new look at whenand how the body’s myriad networks link up to form an übernetwork.
“As I go from one state to another, immediately the links between thephysiological systems change,” says Plamen Ivanov, a biophysicist at
Boston University. “It is quite surprising.”
And it’s not just in bodies. Similar syncing happens all the time ineveryday life. Systems of all sorts constantly connect. Bus stops pop
up near train stations, allowing commuters to hop from one transit
network to another. New friends join your social circle, linking yournetwork of friends to theirs. Telephones, banks, power plants all comeonline — and connect online.
A rich area of research has long been devoted to understanding how
players — whether bodily organs, people, bus stops, companies orcountries — connect and interact to create webs called networks. An
advance in the late 1990s led to a boom in network science, enabling
sophisticated analyses of how networks function and sometimes fail.
But more recently investigators have awakened to the idea that it’s
not enough to know how isolated networks work; studying how
networks interact with one another is just as important. Today, thefrontier field is not network science, but the science of networks of
networks.
“When we think about a single network in isolation, we are missing somuch of the context,” says Raissa D’Souza, a physicist and engineer
at the University of California, Davis. “We are going to makepredictions that don’t match real systems.”
Like their single-network counterparts, networks of networks show up
everywhere. By waking up in the morning, going to work and using
your brain, you are connecting networks. Same when you introduce afamily member to a friend or send a message on Facebook that you
also broadcast via Twitter. In fact, anytime you access the Internet,
which is supported by the power grid, which gets its instructions via
communications networks, you are relying on interdependent
systems. And if your 401(k) lost value during the recent recession,
you’re feeling the effects of such systems gone awry.
Findings so far suggest that networks of networks pose risks ofcatastrophic danger that can exceed the risks in isolated systems. A
seemingly benign disruption can generate rippling negative effects.
Those effects can cost millions of dollars, or even billions, when stock
markets crash, half of India loses power or an Icelandic volcano
spews ash into the sky, shutting down air travel and overwhelming
hotels and rental car companies. In other cases, failure within a
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network of networks can mean the difference between a minor
disease outbreak or a pandemic, a foiled terrorist attack or one thatkills thousands of people.
Understanding these life-and-death scenarios means abandoningsome well-established ideas developed from single-network studies.
Scientists now know that networks of networks don’t always behave
the way single networks do. In the wake of this insight, a revolution isunder way. Researchers from various fields are rushing to figure out
how networks link up and to identify the consequences of thoseconnections.

ENLARGENETWORK MILESTONES
View larger image | A major breakthrough in the study of networks
occurred when researchers discovered that a lot of real-world
networks take a similar form. Dubbed “small-world,” these single
networks are characterized by clustering and shortcuts. Anotherrefinement in thinking is now taking place as attention turns tointeracting networks.
T. DubéInvestigators including Ivanov are analyzing a deluge of data tounderstand how networks cooperate to make bodies function. Other
researchers are probing the Earth around them to identify the links
that keep the planet in balance. But it’s not all rainbows andbutterflies. Much of the recent focus has been on the potential
dangers that come with connection. In one landmark study,
researchers at Boston University and elsewhere have developed mathfor explaining the way networks of networks can suddenly break
down. Studying the bad along with the good may lead to a sort of“how to” for designing integrated systems that not only perform wellin normal times, but also keep working when things go wrong.
Cascades of failure
A series of CNN news clips posted on YouTube highlight thevulnerability of interdependent systems. In what Wolf Blitzer
repeatedly reminds the viewer is only an “exercise,” former U.S.
government officials convene to respond to a simulated cyberattack.
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The War of the Worlds–esque report begins with a Russian computer
infecting a smartphone with a virus. After jumping to othersmartphones, the bug makes its way into U.S. computers. From there
it crashes communication networks, which in turn take out power
stations. The ensuing blackout shuts down transportation networks.
Each failure leads to yet more failures as the effects of a single
infection bounce back and forth between systems. Having no control
over the Russian computer system and no authority to shut down
smartphones, the U.S. government is powerless.
Shlomo Havlin of Bar-Ilan University in Israel sometimes shows
portions of these clips during talks he gives on networks of networks.
“If you have damage in one system, it can lead to damage in anothersystem,” Havlin says. But he points out that concerns about suchrippling damages are not entirely new. Several reports — such as the
CNN coverage — have highlighted worries about how fragileinterdependent systems might be. “What was not known was asystematic way to study this, a framework,” Havlin says.
He first became interested in the problem when a program reviewer
from the U.S. Defense Threat Reduction Agency visited the Boston
University physics department in 2009. The agency was funding
Havlin and H. Eugene Stanley, along with Boston colleagues Gerald
Paul and Sergey Buldyrev, to work on questions plaguing single
networks. The reviewer mentioned a new topic that interested theagency: How resilient are interacting networks when something goesamiss? Proposals were due in a couple of weeks. Despite the shorttime frame, the team, later joined by Bar-Ilan colleague Roni
Parshani, decided to tackle the issue.
Overnight Havlin came up with a way of thinking about it. Single
networks are typically represented by dots joined by lines. The dots,
called nodes, are the players in the network. The lines, called edges
or links, represent connections between those players. Havlin’s insightwas to connect some of the nodes in one network with nodes inanother via a new type of line. His new lines, called dependency links,
signal places where a node in one network relies on a node in theother to function — say, a computer that can’t get by without its sole
power source. These key dependencies could allow a failure topropagate between systems.
Once Havlin outlined a way of thinking about the problem, Buldyrev
worked through the math. It wasn’t simple. He had to use equations
to explain each state of each network as the random removal of one
node triggered the removal of other nodes. Buldyrev, whom Paul calls“a mathematical genius,” cracked it. Answering the program
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reviewer’s initial question took only about a week.
“One morning, I came in and Shlomo was — not quite dancing on thetable — but he was very, very excited,” Paul says.
In their analysis of connected networks, the researchers found a typeof mathematical behavior that couldn’t have been predicted fromknowledge of single networks. When a node is removed from a single
network, the failure tends to propagate gradually, the network coming
apart bit by bit by bit. But removing nodes in a network of networks
means the breakdown can occur abruptly. As nodes go offline, thesystem initially appears to be working properly. But all of a sudden, athreshold is reached. Lose one more node and — poof — the wholething falls to pieces.
“Even if one more node fails, the network collapses completely,”
Havlin says. “It makes the network a much more risky place.”
Stanley likens the single-network scenario to a drunken prisoner
trying to escape with a pair of wire clippers. As the prisoner makes
random cuts along a fence, a hole develops that gradually gets bigger
and bigger. After a little while, maybe, the prisoner can stick an arm
through, and with a few more snips, a head. Eventually enough snips
may allow the prisoner’s whole body to fit through. But in the case of
networks of networks, the prisoner cuts just one or two wires andthen appears to hit on a magical one that makes the whole fence
disintegrate. The prisoner can walk to freedom.

ENLARGEBACK-AND-FORTH FAILURES
View larger image | When networks depend on other networks,
such as a communications network that relies on a power grid,
failure can cascade back and forth between the two. This behavior
may explain sudden breakdowns in interacting systems. Thus, theeffects of an attack on a single node can reduce an übernetwork(above) that starts with 12 operating nodes to just four.
Source: S.V. Buldyrev et al/Nature 2010, adapted byT. Dubé
“It’s as if someone threw a switch,” Stanley says. “But there is noswitch.”After tweaking the math and running some simulations, theresearchers submitted a paper to Nature. Since its publication, in
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2010, more than 100 other papers have cited it.
Other teams have also found unexpected behavior in networks of
networks. In 2009, D’Souza and a colleague showed that connecting alarge portion of nodes in a network of networks takes fewer links thanwould be required for a similar single network. Other scientists haverevealed that imposing travel restrictions may not reduce the spreadof an epidemic as much as would be expected because of theinterconnected nature of human mobility networks. And in 2008,
Italian researchers reported that a power station shutdown led to a
failure in the Internet communication network, causing the
breakdown of more power stations and triggering an electrical
blackout affecting much of Italy. In its Nature paper, the Boston
group used this disaster as a real-world example to model how
failures can cascade back and forth between networks.
What set the Nature paper apart from the others was that it offered asimple mathematical model to explain real-world phenomena. Thatfinding meshed with others to give network-of-networks science atheoretical foundation.
“They have really figured out the framework of how to think about it,”says Albert-László Barabási of Northeastern University in Boston, who
made seminal contributions to studies of single networks. “They camealong and said, let me show how you calculate this and what are theconsequences of coupling these networks.”
Since the discovery, the Boston cadre — along with a battalion of
graduate students — has extended its framework to study thevulnerability of three or more interconnected systems. In anotherstudy, the researchers have found that terrorist-caused damage to an
important power hub may differ from more arbitrary damage causedby, say, a rat chewing through an electrical wire.
Like a social scene in which all the popular kids hang out together, insome networks well-connected nodes are more likely to link up withother well-connected nodes. Stanley, grad student Di Zhou andcolleagues have found that if one network in an interdependent
system has this property, dubbed assortativity, then the wholesystem is more vulnerable to disturbance.
These early findings were unexpected based on studies of solo
networks, leaving scientists wondering what other secrets networks of
networks might hold. “There are many questions that appear
immediately,” Havlin says.
It’s a small world
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A similar burst of activity in network science occurred in 1998, after
Cornell University’s Steven Strogatz and then-colleague Duncan Watts
published a groundbreaking paper, also in Nature. Titled “Collective
dynamics of ‘small-world’ networks,” it explained why the world
seems so tiny.
At the time, “small-world phenomena” had already gained a degree ofnotoriety. In the 1960s, psychologist Stanley Milgram showed that arandomly selected person living in Nebraska could be connected via
acquaintances to a target person in Massachusetts through just a few(typically six) other people. Students from Albright College in
Reading, Pa., made the idea widely known in the mid-1990s when
they invented a game known as Six Degrees of Kevin Bacon, basedon the actor’s appearances in so many movies. With the links definedas coappearances in any single film, Bacon could supposedly beconnected to any other Hollywood celebrity in no more than six steps.
In the network of actors, moving from the node of Kevin Bacon to thenode of, say, Hilary Swank would pass you over fewer than six films.
(In fact, it’s hard to name an actor who is more than two or threedegrees from Kevin Bacon. Try for yourself at
www.oracleofbacon.org.)

ENLARGESLEEP SHIFTS
View larger image | During the transition from deep to light sleep,
networks in the body suddenly join up. Each small circle stands fora measurement of a bodily system, and the lines show which
systems are acting in concert over a four-minute period. From aninteracting networks perspective, deep sleep is quite distinct fromlight sleep, which more resembles waking.
Source: A. Bashan et al/Nature Communications 2012, adapted by
T. DubéSmall-world, or Watts-Strogatz, networks exhibit two features: They
are highly clustered, meaning the nodes clump together like cliques of
middle school girls. And shortcuts connect those cliques, akin to acheerleader who occasionally hangs out with a member of the nerdy
group.
Much like the simple framework developed more recently by the
Boston group, the Cornell duo’s findings had implications for how a
network behaves. “Systems synchronize much faster, epidemics
spread much more rapidly,” Strogatz says. “In the case of game
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theory — where you have people, companies, countries playing
prisoner’s dilemma — we were able to show that the small-world
structure would make a difference in how that game evolved.”
But what really launched the Watts-Strogatz revolution was the way
features in their model matched multiple real-world networks. An
electric power grid, actors connected to Kevin Bacon and the nerve
cells in a worm were all in on a secret that scientists had only justuncovered.
“The legacy is the introduction of the idea of looking at thecomparative anatomy of networks,” Strogatz says. “What we wereable to show was there were universal principles that applied todifferent networks that scientifically were completely unrelated but
mathematically were following the same architectural principles.”Almost immediately, researchers from diverse disciplines abandoned
existing projects and redirected their intellectual firepower to develop
network math for proteins, planes, power stations and pathogens.
Friends, film actors and financial players also got their fair share ofattention. Over the last dozen years or so, this flood of effort has led
to a better understanding of how nodes of all types come together toform networks and what happens when one gets plucked out.
But work so far has focused mostly on the comparative anatomy ofsingle networks. Surprising behavior uncovered in networks of
networks presents a new and still puzzling question: Do theübernetworks behind blackouts, stock market crashes, transportation
gridlock and even sudden deteriorations in health — a particularworry of Stanley’s — conceal a deeper shared anatomy?
Stanley believes they might. When he walks down the stairs, he has a
habit of holding the railing. Breaking a hip, he says, could trigger aseries of disconnections in his body’s network of networks.
It’s widely known that an elderly person who fractures a hip faces agreatly increased chance of dying within the next year, even if repair
surgery is successful. What’s not yet clear, though, is whether the
cascading behavior outlined by the Boston team is behind this abrupt
decline in health. An answer may emerge as scientists find out what
networks of networks in the body, in finance and in nature have incommon.
Plumbing networked networks
Of all the world’s network-of-networks problems, climate change is
one of the most challenging to untangle. How much global
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temperatures will increase over the next century depends on patternsof behavior in the air, the ocean, the land and among all theorganisms living on the planet. Natural cycles are influenced byhuman-driven networks — the economics governing greenhouse gas
emissions, the political drive behind energy alternatives and the social
recognition of global warming as a problem in need of a solution.
In a recent study, physicist Jonathan Donges of Germany’s Potsdam
Institute for Climate Impact Research plotted hundreds of thousandsof data points related to air pressure to study networks in just theatmosphere. By tracking how the data changed over time, he
identified a series of horizontal networks that wrap around the Earth,layering on top of one another like Russian nesting dolls. The Arctic
serves as the link, acting as a sort of atmospheric border patrol that
controls mingling between the horizontal layers, he and colleagues
reported last year in European Physical Journal B.
“The Arctic seems to be important in coupling atmospheric dynamics
on the surface and in higher layers up in the atmosphere,” Donges
says.

ENLARGESAVING NODES
View larger image | In a simulation of coupled networks in Italy(circles represent a power grid, diamonds a communications
network), protecting just four nodes made a system less vulnerable.
At left, all communications servers are coupled to the power grid; atright, four are decoupled. Colors show the probability that a node
fails after 14 servers fail.
Source: C.M. Schneider et al/arxiv.org 2011; Map:
Geoatlas/graphi-ogre, adapted by T. Dubé
If networks of air molecules sound complicated, consider the networkof goings-on in your cells, where the nodes and their links come indifferent forms. Within each cell of your body there is a constantdance among DNA, RNA and proteins. DNA encodes networks of
20,000-plus genes; at any one time many are being decoded into
complementary strands of messenger RNA, which form their own
networks as they guide the production of proteins. Those proteins cando-si-do with other proteins, interacting within their own network in avery physical way, or can connect with other networks by pulling
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genes onto or off the dance floor.
“You cannot look at these networks in isolation,” says Tom Michoel of
the University of Edinburgh’s Roslin Institute. “Everything there isinterconnected.”
Michoel tries to understand networked networks by studying smallscale patterns that show up more often than expected in a particularsystem, and thus say something about its overall functioning.
Consider a common workplace pattern, in which an intermediary canserve as a point of contact between a boss and an employee. Michoelfound many examples of a similar pattern in yeast cells. One of twolinked-up networks included interactions that regulated gene activity,in which a protein (the boss) chemically tags a gene that codes foranother protein (the intermediary). The other contained more direct
protein-protein interactions (between the intermediary and an
employee).
By looking at how the small-scale patterns clustered and overlapped,
Michoel discerned that one boss interacts with one intermediary butthat each intermediary represents many employees, sort of like aunion spokesperson acting on behalf of union members. Without theübernetwork analysis, there was no way to understand the distinct
roles of bosses and intermediaries, Michoel says. Important largescale interactions would have remained hidden.
Exposing unknown interactions is not the only issue. Strengths of theconnections linking networks are also important. The volume of buses
traveling a route, for example, may ramp up during rush hour. Or inyour social networks, you may see a coworker almost every day but ahigh school friend just once a year.
In his investigation of sleep cycles, Ivanov showed that changing how
tightly two networks are coupled can affect physiology. Links don’t
have to be newly created or severed to matter.
A former student of Stanley’s, Ivanov spent more than a decadecollecting data on heart rate, breathing rate, muscle tone and eye
movement to find out how the body’s networks interact during thevarious stages of sleep. Much like Donges’ approach with theatmosphere, Ivanov inferred links and the nature of those links byanalyzing how measurable markers from each system parallel eachother in time. His team found out how the networks hook andunhook, but also how those hookups vary.
Ivanov believes his problem, as well as other network-of-networks
puzzles that show up in the body, is a bit more challenging than the
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ideal scenario tackled by Stanley and Havlin’s group.
“We could have failure even if a particular link between nodes doesn’t
disappear,” Ivanov says. “We could still have all links present, butwith different strengths, and the system can come to arrest.”
Such considerations inject further complications into the emerging
field, suggesting just how much more there is to be learned.
Physicist and computational scientist Alessandro Vespignani ofNortheastern University, who studies epidemics and other spreading
processes in networks, compares the current state of knowledge towhat the Romans knew about Africa 2,000 years ago. The Romans
had a pretty good map of the world, but they didn’t journey deep into
Africa. “There are lions, that was the only information,” Vespignani
says.
Right now, scientists have a map of the future of network science,and networks of networks offer an exciting new area, but people are
only beginning to travel there. “We need to define new mathematical
tools,” Vespignani says. “We need to gather a lot of data. We need to
do the exploratory work to really chart the territory.”
Linked resilience
D’Souza of UC Davis has made early strides in mapping a landscapedifferent from the one where the Boston team planted its flag. When
she and colleagues became interested in networks of networks, they
focused on success rather than failure.
“We weren’t looking in the realm of something so catastrophic thatthe node goes away forever,” D’Souza says. “We are more interested
in a dynamical thing that will keep the network still working.”
In a recent study, her team looked at how two linked power grids
might interact, say a grid that covers much of the eastern UnitedStates and another that services the West. She constructed links
between the grids that are similar to the links between individual
nodes within each grid: The nodes interact, but the survival of one
doesn’t depend entirely on the other. She calls them connectivity
links.
Each node in each network was assigned a capacity, akin to the load a
power plant can handle before it becomes overwhelmed by thatdemand. Links represent ways for a power plant to hand off its load.
If a plant can’t meet a given demand, it can pass some on to anotherlinked power plant, which can pass it on to another and then another.
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As the researchers gradually add demand, like sand being added to apile, they look for “avalanches” of load. Load will take off running
across nodes the way that sand added to a pile will eventually start
tumbling down the sides. Fittingly, network scientists call theseavalanches “sandpile cascades.”In analyzing the mathematics of these cascades, D’Souza and hercolleagues showed that having two networks can help take some of
the burden off a single network, minimizing the threat of large
avalanches. “A little bit of coupling was incredibly beneficial,” D’Souzasays. “The second network acted as a reservoir where the first could
shed some load.”
But add too many connections between the networks and larger
avalanches become possible, the team reported in March in theProceedings of the National Academy of Sciences.
Connected power grids are a good example of networks thatcooperate, says D’Souza. Adding power lines to one network may
boost the transmitting capabilities of the second. But such networks
may also turn competitive, if, for example, an improvement in one
puts the other at an energy-supplying disadvantage.
D’Souza’s efforts have highlighted other flavors that networks of
networks can come in, too. In your social web, you probably haveoverlapping networks, in which you simultaneously belong to a friendgroup and a family group. Or there may be networks in which thenodes are the same, but the links differ; think of banks that borrowmoney from each other in one network and invest in each other inanother.
Then there are systems in which one network is actually built on topof another, the way hyperlinked Web pages sit atop electric, fiberoptic and wireless communication channels. These “overlay networks”
also show up in the brain. Its physical architecture, the very anatomyof the brain, provides the structural network from which function —thought, memory, reason — emerges.
“Functional activity for me is more of a fleeting, fast-changing,difficult to characterize and for that reason much more ethereal
construct in some ways,” says Olaf Sporns of Indiana University.
Sporns is a major player in the Human Connectome Project, whichseeks to understand how all the nerve cells in the brain interact. “Thestructure of the brain, the anatomy is something that, if we havegood enough instruments, we can measure,” he says. “It is actual
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Brain scientists agree that the functional network must somehow berooted in the structural network. But exactly how one gives rise to theother isn’t clear. What’s more, the networks feed off each other,adding the element of evolution to an already hard-to-follow labyrinth
of nodes and links. The architecture sculpts, constrains and molds thefunction, and the function leaves experiential traces on the structure
over time.
Sporns proposes that these dynamics represent a constant balancing
act between the wiring cost in the anatomical network and the desire
for efficient outcomes in the functional network. “This process ofnegotiating, and renegotiating trade-offs,” Sporns and a colleague
wrote in May in Nature Reviews Neuroscience, “continues over long(decades) and short (millisecond) timescales as brain networks
evolve, grow and adapt to changing cognitive demands.”
As the brain changes in time, so does the behavior of the body —influencing all the larger networks in which a person plays a part.
That can expand the puzzles facing scientists. Questions extend to
how a network of networks reacts to what’s happening within, andhow people adapt to the system, says Vespignani. “If I know there isa blackout, I will do certain things. If I know there is an economiccrisis, I will go to the bank and ask to get all my money back. If there
is an epidemic, I will stay home.”
Some scientists speculate that currently available theoretical
approaches for übernetworks may be too simplistic to be useful. One
economist went so far as to warn of the dangers of applying the
Boston team’s results too widely, assuming everything is a nail justbecause you have a hammer. Most researchers, though, offer a moremeasured take.
Toward better systems
While physicists and mathematicians strive for simplicity, engineers
like Leonardo Dueñas-Osorio of Rice University favor a more datadriven simulation approach, enriching tools from network science withrealities from physical systems.
“When you have a complex problem, abstractions of the analyticalkind can help you narrow down where to focus,” Dueñas-Osorio says.
“Then you need to add refinement, make things more realistic.”
Both approaches — theoretical and simulation-based — have somereal-world payoff. With equations that are mathematically tractable,“you can do a lot of insightful derivations,” he says. “Those are very
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valuable, but sometimes you only achieve those at the expense of
simplifying the systems.”
Dueñas-Osorio and others instead build network models that pin
every node into its proper geographic location and give each one adifferent likelihood of failing, depending on factors such as its age oractivity level. Many of these researchers get their data on the ground.
During a trip to Chile after a 2010 earthquake there, Dueñas-Osoriocollected information about what transformers failed and what pipes
broke. He talked to utility companies to track service interruptions.
“This information allows us to get a sense of how strong theconnections are between systems,” he says.
Such data also reveal ways in which systems are suboptimal andcould be improved. Some areas hard-hit by natural disasters don’t
have enough connections — with, for example, only one power plant
supporting a pumping station.
Efforts by Havlin and colleagues have yielded other tips for designingbetter systems. Selectively choosing which nodes in one network tokeep independent from the second network can prevent “poof”
moments. Looking back to the blackout in Italy, the researchers foundthat they could defend the system by decoupling just fourcommunications servers. “Here, we have some hope to make asystem more robust,” Havlin says.
This promise is what piques the interest of governments and otheragencies with money to fund deeper explorations of network-ofnetworks problems. It’s probably what attracted the attention of the
Defense Threat Reduction Agency in the first place. Others outside theUnited States are also onboard. The European Union is spending
millions of euros on Multiplex, putting together an all-star network
science team to create a solid theoretical foundation for interacting
networks. And an Italian-funded project, called Crisis Lab, will receive
9 million euros over three years to evaluate risk in real-world crises,with a focus on interdependencies among power grids,
telecommunications systems and other critical infrastructures.
Eventually, Dueñas-Osorio envisions that a set of guidelines will
emerge not just for how to simulate and study networks of networks,
but also for how to best link networks up to begin with. The UnitedStates, along with other countries, have rules for designing
independent systems, he notes. There are minimum requirements forconstructing buildings and bridges. But no one says how networks of
networks should come together.

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Ivanov hopes to develop a similar rulebook for the human body thatshows actual designs. Many doctors’ offices display diagrams of the
body that outline the different systems — the circulatory system, therespiratory system, the musculoskeletal system. But no diagrams
show how those systems interact with one another, and thatknowledge might be just as crucial for fighting disease.
As more data come in, the goals of those working on human-built
systems and natural systems may merge. More important thanwhether biological, social and technological systems exhibit similarmathematical properties may be whether they should. Can people
design better systems by learning from the systems that exist innature?
Sporns predicts the answer could be yes. “These systems naturally,
just by virtue of being here, actually having survived, have been
optimized to a certain extent,” he says. “They are existing proof thatyou can have complex networks that are structurally buildable andrealizable and sustainable, at the same time dynamically competent,
resilient against perturbations and evolvable.”
How to maximize sustainability, resilience and evolvability in networksof networks are questions that are still largely open. Geneticists seek
answers in the genes, physiologists in the broader body andecologists in the interactions that govern all living things. Connections
forming among these growing webs of knowledge, as well as withengineers’ models and theorists’ frameworks, will provide muchneeded fuel for a burgeoning intellectual endeavor.
If the efforts prevail, one day preventing blackouts, interrupting
epidemics and handling a complicated commute may be as easy as
waking up in the morning.
Network catastrophes
While researchers have not yet analyzed them in detail, some recent
real-world incidents highlight what can happen if disaster strikes
within a network of networks.

© AP, Corbis

India blackout, 2012
Power grids collapsed in India earlier this year,
leaving hundreds of millions of people without
power. The outage triggered transportation
failures as local and long-distance trains stopped
running. Some sources speculate that the grid
was overloaded because a weak monsoon had
farmers using more electricity to pump water tohttp://www.sciencenews.org/view/feature/id/343939/title/When_Networks_Network

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fields.

Stocktrek
Images/Richard
Roscoe/getty
imagesEyjafjallajökull eruption, 2010
Iceland’s Eyjafjallajökull volcano erupted in 2010,
spewing ash that shut down air travel throughout
Europe. But travelers weren’t the only ones
affected: Manufacturers, medical suppliers andcrop producers couldn’t move their goods. The
effects of the grounding rippled into the fuel,hotel and car rental industries.

Swine flu pandemic, 2009
When a swine flu outbreak
hit Mexico in 2009, officials
responded with travel bansand other control measures.
But a drop in international
air traffic to and from
Eneas De
Mexico didn’t prevent a
Troya/flickr
pandemic. Viruses travel
through a complex global mobility über​network that is made up of
long-distance flights as well as local commutes, and interacts withsocial and economic networks.

SUGGESTED READING :Nature Physics special issue, January 2012:
CITATIONS & REFERENCES :P. Bajardi et al. “Human Mobility Networks, Travel Restrictions, and the
Global Spread of 2009 H1N1 Pandemic.” PLoS ONE. Jan. 31, 2011.
Link: .
A.L. Barabasi. “Scale-free Networks: A Decade and Beyond.” Science.
24 July 2009.
A. Bashan et al. “Network physiology reveals relations between network
topology and physiological function.” Nature Communications. 28
February, 2012. doi:10.1038/ncomms1705
S. Buldyrev et al. “Catastrophic cascade of failures in interdependent
networks.” Nature. 15, April 2010. Doi: 10.1038/nature08932.
E. Bullmore and O. Sporns. “The economy of brain networkorganization.” Nature Reviews Neuroscience. May 2012.
X. Huang et al. “Robustness of interdependent networks under targeted
attack.” Physical Review E. 2011.
E.A. Leicht and Raissa M. D’Souza. “Percolation on interacting
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networks.” July 6, 2009. Arxiv.org: .
T. Michoel et al. “Enrichment and aggregation of topological motifs areinterdependent organizational principles of integrated interaction
networks.” Molecular BioSystems. 2011. 2769-2778.
M. Ouyang and L. Duenas-Osorio. “An approach to design interface
topologies across interdependent urban infrastructure systems.”
Reliability Engineering and Systems Safety. 2011, 1462-1473.
V. Rosato et al. “Modelling interdependent infrastructures using
interacting dynamical models.” International Journal of Critical
Infrastructure. 2008.
C. Schneider et al. “Towards designing robust coupled networks.”
Arxiv.org. . June 2011.
J. Travers and S. Milgram. “An Experimental Study of the Small World
Problem.” Sociometry. Dec. 1969, 425-443.
D. Watts and S. Strogatz. “Collective dynamics of ‘small-world’
networks.” Nature. 4 June, 1998.
D. Zhou et al. “Assortativity Decreases the Robustness ofInterdependent Networks.” Arxiv.org. 29 Feb. 2012.
J.F. Donges et al. "Investigating the topology of interacting networks Theory and application to coupled climate subnetworks." European
Physical Journal B 84(4), 635-652 (2011). DOI: 10.1140/epjb/e201110795-8.

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