Sizing Up Complex
Webs: Close or far, many networks look the
same
Erica Klarreich
Researchers have discovered that a remarkable
diversity of complex networks, including the
World Wide Web and patterns in cellular biochemistry,
have a common architecture with snowflakes
and trees. These networks all display similar
patterns, whether viewed from up close or
far away.
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FRACTAL BURST.
In the World Wide Web, links between
individual nodes follow patterns
similar to those of links between
clusters of nodes, between clusters
of clusters, and so on.
Makse |
"It's a fundamental advance," says Albert-László
Barabási, a physicist who studies networks
at the University of Notre Dame in Indiana.
The question of whether complex networks can
show such a fractal pattern, also known as
self-similarity, "has been bugging us for
a while," he says.
In recent years, researchers have found that
a surprising range of networks has a common
structure: a few major hubs with many connections
and many minor nodes with only a few connections.
In the World Wide Web, for instance, tens
of thousands of sites link to a few popular
Web sites, such as Google and Yahoo, while
there are often just a few links to an individual's
home page.
Now, in a surprising finding, researchers
have identified self-similarity in four types
of complex networks: the World Wide Web, a
network of actors who have been in films together,
networks of proteins with links between those
that can bind to each other, and networks
of other cellular molecules with links between
molecules involved in the same biochemical
reactions.
The researchers note that they discovered
this wide-ranging characteristic by figuring
out how to "zoom out" and look at networks
from farther and farther away. They started
by using computer analysis to cover each network
with non-overlapping boxes, each of which
contained a cluster of nodes separated by
less than a specified number of links. Next,
the investigators essentially blurred their
vision, paying attention to how the boxes—rather
than the individual nodes—were connected.
By repeating this procedure many times, the
researchers could observe the structure of
a network on many levels. In the Jan. 27 Nature,
Hernán Makse of the City College of New York
and his coworkers report that all the blurred
networks have connectivity patterns similar
to those of the original network.
Ordinarily, Barabási says, objects with fractal
structures fit into a finite-dimensional space,
such as a flat plane or a three-dimensional
space. By contrast, mathematicians have modeled
complex networks such as the World Wide Web
as infinite dimensional because there's no
realistic way to fit such a network, with
hubs having so many links, into a finite-dimensional
space. This property led Barabási and many
other researchers to assume that complex networks
cannot be self-similar.
"If you had asked me yesterday, I would have
said they are surely not self-similar," Barabási
says.
Understanding the architecture of complex
networks is important, for example, for protecting
the World Wide Web from hacker attacks and
for designing drugs with few side effects,
Barabási says. However, the contribution of
the new finding to those advances isn't yet
clear, he says.
"They've found something new here, but we
don't know yet whether it is a Rosetta stone
that will let us translate the mysteries of
networks into something we understand," says
Steven Strogatz, a mathematician at Cornell
University.
References:
Song, C., S. Havlin, and
H.A. Makse. 2005. Self-similarity of complex
networks. Nature 433(Jan. 27):392-395.
Further Readings:
Barabási, A.-L., and E. Bonabeau.
2003. Scale-free networks. Scientific American
288(May):60-69.
Strogatz, S.H. 2005. Complex
systems: Romanesque networks. Nature
433(Jan. 27):365-366.
Sources:
Albert-László Barabási
Department of Physics
University of Notre Dame
Notre Dame, IN, 46556
Hernán Makse
Levich Institute and Physics Department
City College of New York
New York, NY 10031
Steven Strogatz
Department of Theoretical and Applied Mechanics
Cornell University
Ithaca, NY 14853
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