A Gentle Introduction to Graph Neural Networks (Basics, DeepWalk, and GraphSage)

Kung-Hsiang, Huang (Steeve)
Towards Data Science
8 min readFeb 10, 2019
Image from Pexels

Recently, Graph Neural Network (GNN) has gained increasing popularity in various domains, including social network, knowledge graph, recommender system, and even life science. The power of GNN in modeling the dependencies between nodes in a graph enables the breakthrough in the research area related to graph analysis. This article aims to introduce the basics of Graph Neural Network and two more advanced algorithms, DeepWalk and GraphSage.

Graph

Before we get into GNN, let’s first understand what is Graph. In Computer Science, a graph is a data structure consisting of two components, vertices and edges. A graph G can be well described by the set of vertices V and edges E it contains.

Edges can be either directed or undirected, depending on whether there exist directional dependencies between vertices.

The vertices are often called nodes. In this article, these two terms are interchangeable.

Graph Neural Network

Graph Neural Network is a type of Neural Network which directly operates on the Graph structure. A typical application of GNN is node classification. Essentially, every node in the graph is associated with a label, and we want to predict the label of the nodes…

Published in Towards Data Science

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Written by Kung-Hsiang, Huang (Steeve)

PhD student at UIUC, Co-Founder at Rosetta.ai | Prev: MSc at USC, BEng at HKUST | Twitter: https://twitter.com/steeve__huang

Responses (23)

What are your thoughts?

Thank u for a nice article.
Kappa — Batch = Lambda
essentially.

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Why is "λ (Complete data) = λ (live streaming data) * λ (Stored data)" instead of "λ (Complete data) = λ (live streaming data) + λ (Stored data)"

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Good article. While academically it makes sense to study the patterns as an evolution of architecture, in practice there are more factors to consider than what is detailed here. For instance the “lambda” architecture - the term frankly is more…

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