[Day 94] Link analysis page rank random walks + First assignment + Short intro to GNNs

 Hello :)
Today is Day 94!


A quick summary of today:
  • Finished the last part of Traditional ML methods for Graphs: Link analysis page rank random walks and embeddings
  • Did assignment CoLab 1: Learning Node Embeddings
  • Covered first part of Module 2: Intro to GNNs


Today I continued with the coverage of XCS224W: ML with Graphs


My notes for Link analysis page rank random walks and embeddings

Covered topics: PageRank, Matrix Formulation, Power iteration method, Solutions to dead-ends and spider traps, Personalised PageRank, Random walk with restarts, Using Matrix factorization to express node embeddings based on random walks

Assignment 1: Learning Node Embeddings

 We are not allowed to share any of the code, and I really do not want to risk anything, so I will just say, similar colabs can be found on the course's main webpage. But I spend a lot of time to understand each line of code that I wrote, and how theory from Module 1 on node embeddings is applied to practice. Also, I got 30/30 ^^


Intro to Graph Neural Networks


Tomorrow I continue learning about GNNs


That is all for today!

See you tomorrow :)

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