The hottest Substack posts of Graphs For Science

And their main takeaways
26 implied HN points 28 Dec 24
  1. Reading improves our understanding of data science and technology. The recommended books cover key topics like natural language processing, AI collaboration, and working with network data.
  2. Books can help us connect complex ideas simply. For example, some books demystify artificial intelligence and explain its role in our lives and work.
  3. Being curious about different perspectives enriches our knowledge. Many of the books encourage readers to think about humanity's place in a tech-driven world and to explore ideas beyond our usual understanding.
52 implied HN points 24 Feb 24
  1. k-Core Decomposition is a way to explore the structure of networks by identifying the largest subgraph where every node has a specified minimum degree.
  2. The k-Core Decomposition algorithm involves recursively removing nodes with degrees lower than a specified threshold to reveal the k-core and k-shell structure of a graph.
  3. The degree of a node in a k-core doesn't have an upper limit, providing unique insights into network connectivity beyond traditional degree-based analysis.
0 implied HN points 17 Feb 24
  1. The post announces a new Graphs For Data Science subscriber chat on Substack for exclusive conversations and updates.
  2. To join the chat, download the Substack app, available for both iOS and Android, and turn on push notifications to stay updated on conversations.
  3. Getting started involves downloading the app, opening it, tapping the Chat icon, and joining the thread to engage with others.