Striking the Balance: Diving into the world of network inference
Innovative new method could make computer calculations more efficient
A team of Johns Hopkins applied mathematicians has devised a new method for untangling the intricate web of connections in complex networks such as social media and the internet. The researchers’ findings, described in The Journal of Computational and Graphical Statistics, have the potential to make computer calculations faster and more efficient. They also shed light on finding the right balance between numerical and statistical errors in data analysis: crucial for making reliable decisions based on data.
Carey Priebe, a professor of applied mathematics and statistics worked with Avanti Athreya and Zachary Lubberts, both associate research professors, to identify communities within a YouTube social network based on connections between users. One challenge they faced was that the network’s connections on any given day were sometimes jumbled and unclear, not always accurately representing the community of users. To tackle this, they used a technique called eigendecomposition, which breaks the network down into simpler parts. The aim was to gain insights into the network’s actual community structure. However, performing these eigendecompositions requires a lot of computer power, and the team was eager to make that process more efficient. Read the full article here.