Artificial neural networks help us cluster and classify. Since "Deep learning" became the buzzword, it has been applied for many advances of AI, such as self-driving car, image classification, Alpha Go, etc. There are lots of different deep learning architectures, the most popular ones are based on the well known convolutional neural network which is one type of feed-forward neural networks. This talk will introduce another variant of deep neural network - Graph Neural network which can model the data represented as generic graphs (a graph can have labelled nodes connected via weighted edges). The talk will cover:

• the graph (graph of graphs - GoGs) representation: how we represent different data with graphs
• architecture of graph neural networks (GNN): the architecture of deep graph neural networks and learning algorithm
• applications of GoGs and GNNs: document classification, web spam detection, human action recognition in video