Why I’m constantly fascinated by Graph Theory!
The Science behind Networks is one to closely examine
Imagine you are tasked with creating a university course timetable — such a manner that it meets all set constraints without conflicts. If we are dealing with a handful of courses perhaps its doable. However as the number of courses increase, manually keeping track of the schedule becomes near impossible. How would you approach this problem?
Of all the subjects that I was introduced to during my Bachelors degree in Computer Science, Graph Theory was the one that piqued my interest the most. Sure, just like any other topic, it drops a bunch of jargons that may disengage the incurious ones but for the rest, it poses some interesting and perplexing questions to ponder upon.
It most certainly helped that we had a professor who simplified the concepts in intuitive ways, but perhaps it was the summer of 2019 that made me better appreciate network theory. During my internship, I was introduced to graph database systems, where data points were connected to each other through relations instead of tables in the more familiar SQL based systems. Over those two months, we developed a system that would convert an English query sentence into a graph traversal statement and return the result.
In the time that followed I realized that graph theory, or rather network science, is more prevalent than what most would expect. We all have experienced the benefits of social networks, where digital connections help find mutual friends. On the other side of the coin, this pandemic has challenged epidemiologists to map disease spreaders in society by studying interactions at scale.
Sometimes, it may not be obvious that the issue at hand can be perceived as a graph problem. One of the answers to the question I raised in the first paragraph is — believe it or not — graph coloring! This problem originally was considered to find a way to color countries distinguishably on a globe. In the case of University timetables, using this technique helps avoiding overlaps/conflicts that may arise between classes and time.
The internet we use daily is essentially one huge network of interconnected sites. Just a month back, a guy I’d met on a networking platform called Lunchclub introduced me to his study on network motifs in the neurons of Houseflies. At this point, I would not be surprised if someone revealed the evolution of humankind as a result of time dependent graph expansions — with the birth and death of civilizations due to the neighboring resources.
It is perhaps this very seamless nature of network science that makes it omnipresent — and we are barely scratching the surface when it comes to utility. There are many unsolved graph problems that may have multitudes of real world benefits. Network scientists are constantly innovating real world systems with the graph wonders developed through research. And you could add more value in the future.
If reading about graphs piques your interest as well, I would recommend the following resources to get started:
- Check out this website to learn the basics of graph theory
- Have a look at this page to get a sense of network science.
- Examine some case studies.
- If you prefer a formal deep dive, read this reference book.
- Try to build projects using frameworks, like this one by Arjun Nair (2019 batch).
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