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].

- NP is…

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. …

- 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.

Recently, **explainability **in Artificial Intelligence has attracted…

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…

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.

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

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