and others took on second lives. In 2002, Friendster was a dating service, competing against Match.com in the U.S., but it crashed and burned. Now, Friendster has re-emerged as a social network site, but its strongest markets are in Indonesia, Malaysia, the Philippines, and Singapore. Orkut started in the U.S. as a social network site, but flared out; today, 80% of its users reside in Brazil or India.
Social network analysis, of course, predates online social networks. Some trace the roots of social network analysis to the early 20th century when sociologist Georg Simmel differentiated between social groups (a group with a specific focus such as a family, neighborhood, or job) and a social network (a looser, larger collection of people and groups with connections among groups). Later, psychologist Abraham Maslow’s hierarchy of needs ( physiological, safety, love/belonging, esteem, and self-actualization) was used to understand social networks. Research accelerated in the two decades after World War II as the availability of computers allowed the study of social networks with thousands of nodes. It remained for the Internet to provide networks with millions of nodes. As the size of networks grew, it became more difficult to display a network as a plot of dots connected by relationship lines, and the visual description became points or formulas.
Psychologist Stanley Milgram’s small world, or six degrees of separation, experiments in the 1960s helped explain some aspects of social networks, including the finding that most pairs of nodes passed through 5. 5 nodes to reach the targeted individual. (Don’t look for the phrase “six degrees of separation” in Milgram’s papers; it was coined by playwright John Guare in his 1992 book of the same name.) While six degrees of separation may be true offline, less than three degrees is more likely online.
The Erdős-Rényi models for generating random graphs, which place connections between pairs of nodes with equal probability, help explain some social networks, but later research indicates that random graph models may not scale to larger online networks.
Work in recent years finds intriguing similarities among social network sites as well as with traditional social networks. In the Barabási-Albert model, networks have power-law, scale-free, growth and exhibit preferential attachment. A physics professor at Notre Dame University, Albert-László Barabási has applied the preferential attachment model to online social networks and found that future gains more often accrue to nodes with more connections. In other worlds, a rising tide lifts all yachts, oft-cited academic papers are cited even more often, and a newbie to an online community connects more often to a well-known member.
Ravi Kumar, Jasminie Novak, and Andrew Tomkins at Yahoo! Research studied growth patterns at the Flickr photo-sharing site and the Yahoo! 360 social networking site. In both, they found the network density, or the interconnections per person, followed similar patterns: rapid growth through early adopters, decline in the wake of fewer friendships developing relative to network growth, and slow and steady growth where both members and connections grow. The trio segmented the network in three ways: “singletons” who don’t take part; a large core of connected users; and a middle region of isolated communities that keep to themselves and display a star structure. The stars make up a third of Flickr users and 10% of Yahoo! 360 users; these communities may have a single charismatic activist linked to other users who have few connections outside the star.
Jure Leskovec of Carnegie Mellon, Lars Backstrom of Cornell, and Ravi Kumar and Andrew Tomkins of Yahoo! Research studied large datasets from
computer scientists at the university of california, san diego (ucsd) have developed an algorithm that promises to significantly increase the efficiency of network routing. Known as xL, for approximate link state, the algorithm boosts network routing efficiency by suppressing system updates that force connected networks to continuously recalculate their paths in the internet.
“Being able to adapt to hardware failures is one of the fundamental characteristics of the internet,” says Kirill Levchenko, a student member of the ucsF team. “our routing algorithm reduces the overhead of route recomputation after a network change, making it possible to support larger networks. the benefits are especially significant when networks are made up of low-power devices of slow links.”
researchers at tel Aviv university have developed Korset, an open source program designed to halt malware on Linux, the operating system used by the majority of the world’s Web and email servers. instead of waiting for viruses and other malware to begin operating, Korset models the normal behavior of legitimate programs and instantly shuts down any program that veers away from expected activity. created by Avishai Wool, a professor of computer engineering at tel Aviv university and his graduate student, ohad Ben-cohen, Korset’s code has been released at www.korset.org to promote further development of the program. “it is our hope that this becomes mainstream and that this approach is adopted in standing distributions of operating systems,” said Wool in an interview with MsnBc.
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