Graph pooling is the most crucial step designed to handle node representation from graph data and then produce appropriate graph representation which can be used for graph-level learning tasks.

The proposed structural matrix of an algorithm represents its structure in a suitable for its processing form and compare to the adjacency and incidence matrices requires great less memory capacity ...

However, they lack the efficient utilization of labels of nodes in a graph. In this paper, we propose a novel multi-task representation learning architecture coupled with the task of supervised node ...

No particular course is required as pre-requisite. The course requires basic knowledge of linear algebra, calculus, probability, (un)supervised learning, and programming experience in Python (used ...