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 :)