# CS224W Lecture 1 Introduction

- Why Graphs?
- effective data structure to describe and analyze entities with relations.

- How to we take advantages of the graph for better prediction?
- Explicitly modeling relationships

- Modern ML systems are designed for simple sequences and grids
- but graphs have…
- arbitrary size and complex topological structure
- no fixed ordering

- but graphs have…
- We can develop neural net that are much more broadly applicable by modeling Graphs
- Representation learning will be the main topic for this course
- automatically learn the features without tuning the features each time

- ML tasks related to graphs: node classification, link prediction, graph classification, clustering, …
- choice of the proper network representation of a given domain/problem determines our ability to use netowrk successfully
- the way we assign links will determine the nature of the question we can study.
- directed or undirected
- bipartite graph
- edge list, adjacency matrix or adjecency list
- node and edge attributes
- weighted edges, self edges, multigraphs, ..

October 19, 2021

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cs224w