Network Science Book - Personal Introduction

from Network Science Book Network Science Book - Personal Introduction https://networksciencebook.com/chapter/0

• A subjective narrative of the history by a leading figure in Network Science

• It was a really great story (blu3mo) (blu3mo)

  • Makes you want to become a researcher~

• The following is a rough excerpt. It’s not a summary.

  • It’s fascinating to view the evolution of a field with a decade-long history as a straight path to success. However, amidst the cumulative impact, one might overlook the most intriguing question: Why did this field grow so rapidly?

  • At the end of lunch, I handed a document closely typed by Leka to Reka. This was my early vision on network science. I estimated it would take about 6 months to quantify network topology and another 6 months to understand the impact of topology on network dynamics. Then we could move on to the real problem and explore the co-evolution of network topology and dynamics.

    • I’m interested in what can be seen regarding this (blu3mo)

  • The gap in this discipline reflected two communities asking different questions: graph theorists were concerned about phase transitions, subgraphs, and giant components, while social scientists were fascinated by small worlds, weak ties, and communities. For social scientists, a network of 100 nodes was beyond comprehension, but mathematicians were excited only at the limit of N → ∞.

    • Seems like there’s such a gap~ (blu3mo)
      • I wonder how the current Network Institute is reconciling this
    • The 6-degree separation is interesting on the social science side (blu3mo)

  • Is the World Wide Web still potentially divided into many disconnected components? Or, as everyone recognized at the time, has it already become one large network? Regardless of the outcome, this was an intriguing question.

    • Indeed, in the early days, such questions arise
    • You could probably have fun even with a backyard network (blu3mo)

  • Barabási’s data was a striking deviation from everything I had learned in my 4-year journey on networks. There were no traces in the literature of networks with power-law degree distributions. In fact, it seems that until that point, few people were interested in degree distributions: the literature on random graphs and social networks had accepted Poisson forms as natural. The power-law we observed predicted the existence of hubs on the web, nodes with a vast number of links, outliers that would not be allowed in a random space. None of the existing models could explain them.

    • Scale-Free Networks

  • To understand why the WWW is special, I needed to learn more about other networks. Therefore, before boarding a flight to Europe, I actively sought additional network maps. The first map was provided by Jay Brockman, a computer science professor at the University of Notre Dame, and I received a wiring diagram of computer chips manufactured by IBM. Duncan Watts sent me a map of the power grid, and Brett Tien shared a Hollywood actor database. I handed all of these to Leka to analyze while traveling.

    • Definitely exciting (blu3mo)- My mind was spinning around the meaning of it. If different networks like the web and Hollywood show the same power-law distribution, then the characteristics seen on the World Wide Web must be universal! Therefore, there must be some common laws or mechanisms involved in its occurrence! And if it can be applied to different systems like actors, computer chips, or the web, the explanation must be fundamental and simple.

  • Universal

• At that time, my research group consisted of four students and one postdoc, and it was modest. Except for Leka, everyone was working on surfaces and quantum dots. A few days after a paper was accepted by Science, I held a group meeting and made a shocking announcement for some group members. I told them that I was quitting materials science. The reason was simple: I didn’t want to divide my time and attention between making a living and pursuing topics I was no longer passionate about. With three years left on my term, I decided to switch fields from quantum dots to networks. I gave each group member a choice: to join me on this new journey or to leave.

  • Hilarious

• Two students abandoned ship. The rest followed me on this new, uncharted voyage.

• Viewing the events above as a series of successes is easy. Over the next ten years, the 1999 Science paper became the most cited paper in physical sciences. The 2000 Nature paper not only graced the journal’s cover but also deeply influenced the understanding of network robustness. Leka and I spent the following year writing reviews on networks, formalizing the intellectual foundation of this field, which eventually became the most cited paper in the Review of Modern Physics 19. Next, the 2005 National Research Council report, released by the US National Academy, coined the term network science and convinced the US government to invest hundreds of millions of dollars to support this new research field as an independent discipline. Ultimately, leading scientific publishers such as Cambridge University Press and Oxford University Press, as well as top engineering organizations like IEEE, launched journals to cover advancements in this field. A new discipline emerged by all measures, supported by a vibrant interdisciplinary community.

• Science rarely follows a linear path to success (see image 0.10). New ideas require years of maturation. The theory of scale-free networks can be seen as an exception, as the idea went from conception to paper submission in just 10 days. However, if it hadn’t preceded five years of seemingly futile work on the problem, that spark would never have ignited a fire.