Social networks sturdier than 'Net

By Kimberly Patch, Technology Research News

Although many types of networks, including biological networks, social networks, and the Internet, have a lot in common, when you get right down to who is connecting to whom, social networks follow different rules.

A researcher from the Santa Fe Institute has found that social networks are assortative, meaning people who are social gravitate toward others who are social. This is very different from many other types of networks.

The nodes of social networks are people. People who already have connections like to associate with other nodes who have connections, said Mark Newman, now an assistant professor of physics at the University of Michigan.

In contrast, non-social networks like the Internet, World Wide Web, and biological networks are disassortative, meaning highly-connected nodes tend to connect to nodes that have few connections, said Newman.

There's a "big difference between social networks and all other kinds of networks," said Newman. This was somewhat unexpected, and it has several ramifications, he said.

In social networks, where popular people are friends with other popular people, diseases spread easily, said Newman. At the same time, however, this type of network has a small central set of people that the disease can actually reach. "They support epidemics easily, but... the epidemic is limited in who it can reach," he said.

The opposite is true for the Internet, the Web and biological networks, said Newman. This makes these types of networks more vulnerable to attack than social networks are.

The implications for vaccinating people and for protecting networks like the Internet against attacks are not good, according to Newman. The networks that we might want to break up, like social networks that spread disease, are resilient against attacks; but the networks that we wish to protect, like the Internet, are vulnerable to attack, said Newman.

Social networks hold together even when some of the most connected nodes are removed. This may be because these nodes tend to be clustered together in a core group so that there's a lot of redundancy, according to Newman. This means that vaccination and similar strategies are less effective than in other types of networks.

Attacks on the largest nodes of disassortative networks, however, affect the network as a whole more because the connections are more broadly distributed across the network. "This suggests that if nodes were to fail on the Internet, it would have a bigger effect on the performance of the Net than we might otherwise expect," he said. "In a way, it is telling us that the Internet is fragile."

Newman found that the number of highly-connected nodes that need to be removed to destroy disassortative networks is smaller by a factor of five or 10 than the number needed to destroy assortative networks.

The technical challenge to doing the research was the computer simulations, said Newman. They "involve some tricks that I had to work out specially for this study," he said. The mathematics involved and the computer simulations all tied together to give a clear picture of what is going on, he said.

The model could eventually be used to better understand how diseases spread, said Newman. "[It could] suggest better strategies for preventing their spread," he said.

The model could also be of use in the Internet, he said. "Ultimately the aim... is to understand how network systems work, and how the structure of the network affects their performance, for example, how the structure of a social network affects the way societies work," he said.

The model could be used in epidemiological work now, said Newman.

He published the research in the October 28, 2002 issue of Physical Review Letters. The research was funded by the National Science Foundation.

Timeline:   Now, 3 years
Funding:   Government
TRN Categories:  Networking; Physics
Story Type:   News
Related Elements:  Technical paper, "Assortative Mixing in Networks," Physical Review Letters, October 28, 2002.


February 12/19, 2003

Page One

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