Hands-On Tutorials

Graph attention, graph convolution, network propagation are all special cases of message passing in graph neural networks.

Message passing networks (MPN), graph attention networks (GAT), graph convolution networks (GCN), and even network propagation (NP) are closely related methods that fall into the category of graph neural networks (GNN). This post will provide a unified view of these methods, following mainly from chapter 5.3 in [1].

TL;DR

  • NP is…


Thoughts and Theory

Two different views of network propagation, a popular method in computational biology, and its connection with graph convolution

You’ve probably heard about graph convolution as it is such a hot topic at the time. Although less well known, network propagation is a dominating method in computational biology for learning on networks. …


A step forward in explainable AI, and why it is hard to adapt existing explanation methods to GNNs

TL;DR

  • Explainability is a big topic in deep learning as it enables more reliable and trustable predictions.
  • Existing explanation methods can’t be easily adapted to Graph Neural Networks due to the irregularity of graph structure.
  • Quick peek into 5 groups of GNN explanation methods.

Explainability increases reliability

Recently, explainability in Artificial Intelligence has attracted…


Getting Started

How second order random walk on graph works, explained via animations

Node2vec is an embedding method that transforms graphs (or networks) into numerical representations [1]. For example, given a social network where people (nodes) interact via relations (edges), node2vec generates numerical representation, i.e., a list of numbers, to represent each person. This representation preserves the structure of the original network in…


Making Sense of Big Data

An easy to use implementation of a popular graph embedding method

Node2vec is a node embedding method that generates numerical representation (or embeddings) of nodes in a graph [1]. These embeddings are then used for various down stream tasks such as node classification and link prediction. …


Advancements in artificial intelligence and machine learning have led to many new methods in the areas of bioinformatics and computational biology. Common applications include gene classification [1], sample annotation [2], enzyme properties [3], and etc.

The problem of optimistic bias

Many papers that propose new computational methods conclude with a similar claim that “the proposed…

Remy Lau

Computational Mathematics | Bioinformatics | Network Science | Deep Learning | linkedin.com/in/remy-liu-a24780213/

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