* Book: GRL (Graph Representation Learning) by W.L. Hamilton (2020)

General points for thorough reading (not extensive reading)

• Read every words and mark/memorize important words.
• Try to understand every piece the authors want to tell us - if needed, read other reference(s).
• However, if you cannot understand something after you have tried more than 30 minutes, discuss it with others.
• Try to use low-entropy description in explaining.

range: from the title page to p8 (end of Chapter 1)

i: title page

• official book
• author William L. Hamilton was an Assistant Professor at that time at McGill Univ.
• McGill Univ., a famous university in Canada, 12 Nobel laureates, and graduate Yoshua Bengio (2018 ACM Turing Award, one of the three Godfathers of deep learning)

ii: Abstract page

• no-free-lunch theorem
• ⇒ inductive bias is important before optimization (and before machine learning). Ex. Consider to use a linear regression to analyze a dataset. The assumption that a linear regression is approporiate is an inductive bias.
• What is induction?
• Lin & Tegmark'16 Why does deep and cheap learning work so well? ⇒ Assumptions: (1) Low polynomial order in the real world (2) Locality of data (3) Symmetry phenomenon.
• 3 graph learning topics: (1) embedding (2) CNN → Graph (3) Message-passing approach

iii-v: Contents

• not important now

vi: Preface

• “past seven years” ⇒ graph learning started from 2013
• at 2020, fastest growing sub-areas of deep learning
• audience: shall have some background in machine learning and deep learning (e.g., Goodfellow et al, 2016)

vii-viii: Acknowledgements

• connections of the author (e.g., Jure Leskovec is a famous researcher in network science)

p1: Chapter 1 Introduction

• node, edge, graph ⇒ ask the audience to illustrate some graphs
• Zachary Karate Club Network (1977)